diff --git a/README.md b/README.md
index 7f1a9d6..4ca3e82 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ This is updated **face-api.js** with latest available TensorFlow/JS as the origi
 
 Forked from [face-api.js](https://github.com/justadudewhohacks/face-api.js) version **0.22.2** released on March 22nd, 2020  
 
-Currently based on **`TensorFlow/JS` 2.8.2**  
+Currently based on **`TensorFlow/JS` 2.8.3**  
 
 ### Why?
 
@@ -35,6 +35,7 @@ Unfortunately, changes ended up being too large for a simple pull request on ori
 - Updated TensorFlow/JS dependencies since backends were removed from `@tensorflow/tfjs-core`  
 - Updated mobileNetv1 model due to `batchNorm()` dependency  
 - Added `version` class that returns JSON object with version of FaceAPI as well as linked TFJS  
+- Added test/dev built-in HTTP & HTTPS Web server
 - Removed `mtcnn` and `tinyYolov2` models as they were non-functional in latest public version of `Face-API`  
   *If there is a demand, I can re-implement them back.*  
 
@@ -50,8 +51,12 @@ Which means valid models are **tinyFaceDetector** and **mobileNetv1**
 
 ### Browser
 
-Browser example that uses both models as well as all of the extensions is included in `/example/index.html`  
-Example can be accessed directly using Git pages using URL: <https://vladmandic.github.io/face-api/example/>  
+Browser example that uses static images and showcases both models as well as all of the extensions is included in `/example/index.html`  
+Example can be accessed directly using Git pages using URL: <https://vladmandic.github.io/face-api/example/index.html>  
+
+Browser example that uses live webcam is included in `/example/webcam.html`  
+Example can be accessed directly using Git pages using URL: <https://vladmandic.github.io/face-api/example/webcam.html>  
+
 
 <br>
 
@@ -242,6 +247,37 @@ And then use with:
 
 Pretrained models and their weights are includes in `./model`.
 
+<br>
+<hr>
+<br>
+
+## Test & Dev Web Server
+
+Built-in test&dev web server can be started using
+```shell
+npm run dev
+```
+
+By default it starts HTTP server on port 8000 and HTTPS server on port 8001 and can be accessed as:  
+- <https://localhost:8001/example/index.html>
+- <https://localhost:8001/example/webcam.html>
+
+```json
+2021-01-10 08:39:00 INFO:  @vladmandic/face-api version 0.10.2
+2021-01-10 08:39:00 INFO:  User: vlado Platform: linux Arch: x64 Node: v15.4.0
+2021-01-10 08:39:00 INFO:  Build: file startup all target: es2018
+2021-01-10 08:39:00 STATE:  HTTP server listening: 8000
+2021-01-10 08:39:00 STATE:  HTTP2 server listening: 8001
+2021-01-10 08:39:00 STATE:  Monitoring: [ 'package.json', 'config.js', 'example', 'src', [length]: 4 ]
+2021-01-10 08:39:00 STATE:  Monitoring: [ 'package.json', 'config.js', 'example', 'src', [length]: 4 ]
+2021-01-10 08:39:01 STATE:  Build for: browserBundle type: tfjs: { modules: 1253, moduleBytes: 3997175, imports: 7, importBytes: 276, outputBytes: 1565414, outputFiles: 'dist/tfjs.esm.js' }
+2021-01-10 08:39:01 STATE:  Build for: browserBundle type: iife: { imports: 160, importBytes: 1797487, outputBytes: 1699552, outputFiles: 'dist/face-api.js' }
+2021-01-10 08:39:01 STATE:  Build for: browserBundle type: esm: { imports: 160, importBytes: 1797487, outputBytes: 1697086, outputFiles: 'dist/face-api.esm.js' }
+2021-01-10 08:39:01 INFO:  Compile: [ 'src/index.ts', [length]: 1 ]
+```
+
+<br>
+<hr>
 <br>
 
 ## Build
@@ -272,19 +308,21 @@ npm run build
 > @vladmandic/face-api@0.8.9 build /home/vlado/dev/face-api
 > rimraf dist/* && node ./build.js
 ```
+
 ```json
-2020-12-02 16:31:23 INFO:  @vladmandic/face-api version 0.8.9
-2020-12-02 16:31:23 INFO:  User: vlado Platform: linux Arch: x64 Node: v15.0.1
-2020-12-02 16:31:23 INFO:  Build: file startup all target: es2018
-2020-12-02 16:31:23 STATE:  Build for: node type: tfjs: { imports: 1, importBytes: 39, outputBytes: 1042, outputFiles: 'dist/tfjs.esm.js' }
-2020-12-02 16:31:23 STATE:  Build for: node type: node: { imports: 160, importBytes: 228038, outputBytes: 134190, outputFiles: 'dist/face-api.node.js' }
-2020-12-02 16:31:23 STATE:  Build for: nodeGPU type: tfjs: { imports: 1, importBytes: 43, outputBytes: 1046, outputFiles: 'dist/tfjs.esm.js' }
-2020-12-02 16:31:23 STATE:  Build for: nodeGPU type: node: { imports: 160, importBytes: 228042, outputBytes: 134198, outputFiles: 'dist/face-api.node-gpu.js' }
-2020-12-02 16:31:23 STATE:  Build for: browserNoBundle type: tfjs: { imports: 1, importBytes: 1784, outputBytes: 244, outputFiles: 'dist/tfjs.esm.js' }
-2020-12-02 16:31:23 STATE:  Build for: browserNoBundle type: esm: { imports: 160, importBytes: 227240, outputBytes: 131024, outputFiles: 'dist/face-api.esm-nobundle.js' }
-2020-12-02 16:31:24 STATE:  Build for: browserBundle type: tfjs: { modules: 1045, moduleBytes: 3718721, imports: 7, importBytes: 1784, outputBytes: 1501677, outputFiles: 'dist/tfjs.esm.js' }
-2020-12-02 16:31:24 STATE:  Build for: browserBundle type: iife: { imports: 162, importBytes: 1728673, modules: 576, moduleBytes: 1359851, outputBytes: 1903311, outputFiles: 'dist/face-api.js' }
-2020-12-02 16:31:25 STATE:  Build for: browserBundle type: esm: { imports: 162, importBytes: 1728673, modules: 576, moduleBytes: 1359851, outputBytes: 1900836, outputFiles: 'dist/face-api.esm.js' }
+2021-01-10 08:42:01 INFO:  @vladmandic/face-api version 0.10.2
+2021-01-10 08:42:01 INFO:  User: vlado Platform: linux Arch: x64 Node: v15.4.0
+2021-01-10 08:42:01 INFO:  Build: file startup all target: es2018
+2021-01-10 08:42:01 STATE:  Build for: node type: tfjs: { imports: 1, importBytes: 143, outputBytes: 1042, outputFiles: 'dist/tfjs.esm.js' }
+2021-01-10 08:42:01 STATE:  Build for: node type: node: { imports: 160, importBytes: 233115, outputBytes: 132266, outputFiles: 'dist/face-api.node.js' }
+2021-01-10 08:42:01 STATE:  Build for: nodeGPU type: tfjs: { imports: 1, importBytes: 147, outputBytes: 1046, outputFiles: 'dist/tfjs.esm.js' }
+2021-01-10 08:42:01 STATE:  Build for: nodeGPU type: node: { imports: 160, importBytes: 233119, outputBytes: 132274, outputFiles: 'dist/face-api.node-gpu.js' }
+2021-01-10 08:42:01 STATE:  Build for: browserNoBundle type: tfjs: { imports: 1, importBytes: 276, outputBytes: 244, outputFiles: 'dist/tfjs.esm.js' }
+2021-01-10 08:42:01 STATE:  Build for: browserNoBundle type: esm: { imports: 160, importBytes: 232317, outputBytes: 129069, outputFiles: 'dist/face-api.esm-nobundle.js' }
+2021-01-10 08:42:01 STATE:  Build for: browserBundle type: tfjs: { modules: 1253, moduleBytes: 3997175, imports: 7, importBytes: 276, outputBytes: 1565414, outputFiles: 'dist/tfjs.esm.js' }
+2021-01-10 08:42:02 STATE:  Build for: browserBundle type: iife: { imports: 160, importBytes: 1797487, outputBytes: 1699552, outputFiles: 'dist/face-api.js' }
+2021-01-10 08:42:02 STATE:  Build for: browserBundle type: esm: { imports: 160, importBytes: 1797487, outputBytes: 1697086, outputFiles: 'dist/face-api.esm.js' }
+2021-01-10 08:42:02 INFO:  Compile: [ 'src/index.ts', [length]: 1 ]```
 ```
 
 <br>
diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js
index c939297..7252701 100644
--- a/dist/face-api.esm-nobundle.js
+++ b/dist/face-api.esm-nobundle.js
@@ -5,5 +5,5 @@
   author: <https://github.com/vladmandic>'
   */
 
-var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);import*as dist_star from"@tensorflow/tfjs/dist/index.js";import*as tfjs_backend_wasm_star from"@tensorflow/tfjs-backend-wasm";var require_tfjs_esm=__commonJS(exports=>{__exportStar(exports,dist_star);__exportStar(exports,tfjs_backend_wasm_star)}),require_isNodejs=__commonJS((exports,module)=>{__export(exports,{isNodejs:()=>isNodejs2});function isNodejs2(){return typeof global=="object"&&!0&&typeof module!="undefined"&&typeof process!="undefined"&&!!process.version}}),tf42=__toModule(require_tfjs_esm()),draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range,round:()=>round});var tf=__toModule(require_tfjs_esm()),Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof tf.Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!==0}function isEven(num){return num%2===0}function round(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale2=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale2),Math.round(height*scale2))}function getCenterPoint(pts){return pts.reduce((sum,pt)=>sum.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range(num,start,step){return Array(num).fill(0).map((_,i)=>start+i*step)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min,x)=>x<min?x:min,Infinity),minY=ys.reduce((min,y)=>y<min?y:min,Infinity),maxX=xs.reduce((max,x)=>max<x?x:max,0),maxY=ys.reduce((max,y)=>max<y?y:max,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}var tf2=__toModule(require_tfjs_esm());function normalize(x,meanRgb){return tf2.tidy(()=>{let[r,g,b]=meanRgb,avg_r=tf2.fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=tf2.fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=tf2.fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=tf2.concat([avg_r,avg_g,avg_b],3);return tf2.sub(x,avg_rgb)})}var tf3=__toModule(require_tfjs_esm());function padToSquare(imgTensor,isCenterImage=!1){return tf3.tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,tf3.fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>tf3.cast(t,"float32"));return tf3.concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array=inputArray.slice();for(let i=array.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array[i];array[i]=array[j],array[j]=x}return array}function sigmoid(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json){let descriptors=json.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch=window.fetch;if(!fetch)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer)=>err?reject(err):resolve(buffer))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch,...fileSystem}}function isBrowser(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env2){environment=env2}function initialize(){return isBrowser()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env2){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env2;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env2.createCanvasElement||(()=>new Canvas),environment.createImageElement=env2.createImageElement||(()=>new Image),environment.ImageData=env2.ImageData||environment.ImageData,environment.Video=env2.Video||environment.Video,environment.fetch=env2.fetch||environment.fetch,environment.readFile=env2.readFile||environment.readFile}var env={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}var tf18=__toModule(require_tfjs_esm());function isMediaLoaded(media){let{Image,Video}=env.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input){let{Image,Video}=env.getEnv();return input instanceof Image?new Dimensions(input.naturalWidth,input.naturalHeight):input instanceof Video?new Dimensions(input.videoWidth,input.videoHeight):new Dimensions(input.width,input.height)}function createCanvas({width,height}){let{createCanvasElement}=env.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}var tf4=__toModule(require_tfjs_esm());async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tf4.tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await tf4.browser.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input){let{Image,Canvas,Video}=env.getEnv();return input instanceof Image||input instanceof Canvas||input instanceof Video}var tf5=__toModule(require_tfjs_esm());function imageToSquare(input,inputSize,centerImage=!1){let{Image,Canvas}=env.getEnv();if(!(input instanceof Image||input instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input),scale2=inputSize/Math.max(dims.height,dims.width),width=scale2*dims.width,height=scale2*dims.height,targetCanvas=createCanvas({width:inputSize,height:inputSize}),inputCanvas=input instanceof Canvas?input:createCanvasFromMedia(input),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input,idx)=>{if(isTensor3D(input)){this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape;return}if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape.slice(1);return}let canvas=input instanceof env.getEnv().Canvas?input:createCanvasFromMedia(input);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tf5.tidy(()=>{let inputTensors=range(this.batchSize,0,1).map(batchIdx=>{let input=this.getInput(batchIdx);if(input instanceof tf5.Tensor){let imgTensor=isTensor4D(input)?input:input.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=tf5.image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input instanceof env.getEnv().Canvas)return tf5.browser.fromPixels(imageToSquare(input,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)});return tf5.stack(inputTensors.map(t=>tf5.cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input,i)=>{if(!isMediaElement(input)&&!isTensor3D(input)&&!isTensor4D(input))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input=>isMediaElement(input)&&awaitMediaLoaded(input))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input,detections){let{Canvas}=env.getEnv(),canvas=input;if(!(input instanceof Canvas)){let netInput=await toNetInput(input);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}var tf6=__toModule(require_tfjs_esm());async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tf6.tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>tf6.slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init){let{fetch}=env.getEnv(),res=await fetch(url,init);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}var tf7=__toModule(require_tfjs_esm());function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return tf7.io.loadWeights(manifest,modelBaseUri)}function matchDimensions(input,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input.width=width,input.height=height,{width,height}}var tf15=__toModule(require_tfjs_esm()),tf8=__toModule(require_tfjs_esm()),NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof tf8.Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof tf8.Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable})=>{let tensor2=tf8.tensor(variable.dataSync());variable.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights=tf8.io.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof tf8.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}},tf10=__toModule(require_tfjs_esm()),tf9=__toModule(require_tfjs_esm());function depthwiseSeparableConv(x,params,stride){return tf9.tidy(()=>{let out=tf9.separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=tf9.add(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=tf10.relu(tf10.add(out1,tf10.add(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,tf10.add(out3,out4))))})}var tf11=__toModule(require_tfjs_esm());function convLayer(x,params,padding="same",withRelu=!1){return tf11.tidy(()=>{let out=tf11.add(tf11.conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?tf11.relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}var tf12=__toModule(require_tfjs_esm());function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tf12.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf12.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}var tf13=__toModule(require_tfjs_esm());function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tf13.tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tf13.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var tf14=__toModule(require_tfjs_esm()),SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tf14.tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tf14.tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tf14.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tf15.tidy(()=>{let batchTensor=tf15.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf15.scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=tf15.avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}},tf17=__toModule(require_tfjs_esm()),tf16=__toModule(require_tfjs_esm());function fullyConnectedLayer(x,params){return tf16.tidy(()=>tf16.add(tf16.matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf17.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}},FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input){return tf18.tidy(()=>tf18.softmax(this.runNet(input)))}async forward(input){return this.forwardInput(await toNetInput(input))}async predictExpressions(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(tf18.unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version="0.10.1",tf20=__toModule(require_tfjs_esm()),tf19=__toModule(require_tfjs_esm());function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return tf19.add(tf19.conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?tf19.relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=tf19.maxPool(out,[3,3],[2,2],"same"),out=tf19.add(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(tf19.relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv2,[1,1]),out=tf19.add(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tf19.tidy(()=>{let batchTensor=tf19.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf19.scalar(256)),out=tf19.relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=tf19.relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf20.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input,pooled=tf20.avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input){return tf20.tidy(()=>{let{age,gender}=this.runNet(input);return{age,gender:tf20.softmax(gender)}})}async forward(input){return this.forwardInput(await toNetInput(input))}async predictAgeAndGender(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),ages=tf20.unstack(out.age),genders=tf20.unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var tf21=__toModule(require_tfjs_esm()),FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale2=inputSize/Math.max(height,width);return{width:width*scale2,height:height*scale2}}),batchSize=inputDimensions.length;return tf21.tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>tf21.stack([tf21.fill([68],fillX,"float32"),tf21.fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding(batchIdx,(w,h)=>h<w);return output.mul(tf21.fill([batchSize,136],inputSize,"float32")).sub(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input){return tf21.tidy(()=>{let out=this.runNet(input);return this.postProcess(out,input.inputSize,input.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input){return this.forwardInput(await toNetInput(input))}async detectLandmarks(input){let netInput=await toNetInput(input),landmarkTensors=tf21.tidy(()=>tf21.unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven(i)),yCoords=landmarksArray.filter((_,i)=>!isEven(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var tf22=__toModule(require_tfjs_esm());function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tf22.tidy(()=>{let batchTensor=tf22.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf22.scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=tf22.avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};var tf27=__toModule(require_tfjs_esm()),tf24=__toModule(require_tfjs_esm()),tf23=__toModule(require_tfjs_esm());function scale(x,params){return tf23.add(tf23.mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=tf24.conv2d(x,filters,strides,padding);return out=tf24.add(out,bias),out=scale(out,params.scale),withRelu?tf24.relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}var tf25=__toModule(require_tfjs_esm());function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tf25.tidy(()=>tf25.transpose(tf25.tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tf25.tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tf25.tensor1d(extractWeights(numWeights)),biases=tf25.tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale2=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale2}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tf25.tidy(()=>tf25.transpose(tf25.tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale2=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale2}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf26=__toModule(require_tfjs_esm());function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=tf26.add(out,x),out=tf26.relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=tf26.avgPool(x,2,2,"valid"),zeros2=tf26.zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=tf26.zeros(padShapeX);out=tf26.concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=tf26.zeros(padShapeY);out=tf26.concat([out,zerosH],2)}return pooled=isPad?tf26.concat([pooled,zeros2],3):pooled,out=tf26.add(pooled,out),out=tf26.relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tf27.tidy(()=>{let batchTensor=tf27.cast(input.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf27.scalar(256)),out=convDown(normalized,params.conv32_down);out=tf27.maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return tf27.matMul(globalAvg,params.fc)})}async forward(input){return this.forwardInput(await toNetInput(input))}async computeFaceDescriptor(input){let netInput=await toNetInput(input),faceDescriptorTensors=tf27.tidy(()=>tf27.unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}var tf34=__toModule(require_tfjs_esm()),tf28=__toModule(require_tfjs_esm());function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tf28.tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tf28.tensor1d(extractWeights(numChannels)),batch_norm_offset=tf28.tensor1d(extractWeights(numChannels)),batch_norm_mean=tf28.tensor1d(extractWeights(numChannels)),batch_norm_variance=tf28.tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tf28.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf28.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tf28.tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf30=__toModule(require_tfjs_esm()),tf29=__toModule(require_tfjs_esm());function pointwiseConvLayer(x,params,strides){return tf29.tidy(()=>{let out=tf29.conv2d(x,params.filters,strides,"same");return out=tf29.add(out,params.batch_norm_offset),tf29.clipByValue(out,0,6)})}var epsilon=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tf30.tidy(()=>{let out=tf30.depthwiseConv2d(x,params.filters,strides,"same");return out=tf30.batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon),tf30.clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tf30.tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(!(selected.length>=outputSize)){let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}}),selected}var tf31=__toModule(require_tfjs_esm());function getCenterCoordinatesAndSizesLayer(x){let vec=tf31.unstack(tf31.transpose(x,[1,0])),sizes=[tf31.sub(vec[2],vec[0]),tf31.sub(vec[3],vec[1])],centers=[tf31.add(vec[0],tf31.div(sizes[0],tf31.scalar(2))),tf31.add(vec[1],tf31.div(sizes[1],tf31.scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=tf31.unstack(tf31.transpose(x1,[1,0])),div0_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2],tf31.scalar(5))),sizes[0]),tf31.scalar(2)),add0_out=tf31.add(tf31.mul(tf31.div(vec[0],tf31.scalar(10)),sizes[0]),centers[0]),div1_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3],tf31.scalar(5))),sizes[1]),tf31.scalar(2)),add1_out=tf31.add(tf31.mul(tf31.div(vec[1],tf31.scalar(10)),sizes[1]),centers[1]);return tf31.transpose(tf31.stack([tf31.sub(add0_out,div0_out),tf31.sub(add1_out,div1_out),tf31.add(add0_out,div0_out),tf31.add(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tf31.tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(tf31.reshape(tf31.tile(params.extra_dim,[batchSize,1,1]),[-1,4]),tf31.reshape(boxPredictions,[-1,4]));boxes=tf31.reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=tf31.sigmoid(tf31.slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=tf31.slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=tf31.reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=tf31.unstack(boxes),scoresByBatch=tf31.unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}var tf33=__toModule(require_tfjs_esm()),tf32=__toModule(require_tfjs_esm());function boxPredictionLayer(x,params){return tf32.tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=tf32.reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=tf32.reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tf33.tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=tf33.concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=tf33.concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tf34.tidy(()=>{let batchTensor=tf34.cast(input.toBatchTensor(512,!1),"float32"),x=tf34.sub(tf34.mul(batchTensor,tf34.scalar(.007843137718737125)),tf34.scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input){return this.forwardInput(await toNetInput(input))}async locateFaces(input,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression2(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",tf39=__toModule(require_tfjs_esm()),isNumber=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber(a.x)&&isNumber(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}var tf36=__toModule(require_tfjs_esm()),tf35=__toModule(require_tfjs_esm());function leaky(x){return tf35.tidy(()=>{let min=tf35.mul(x,tf35.scalar(.10000000149011612));return tf35.add(tf35.relu(tf35.sub(x,min)),min)})}function convWithBatchNorm(x,params){return tf36.tidy(()=>{let out=tf36.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf36.conv2d(out,params.conv.filters,[1,1],"valid"),out=tf36.sub(out,params.bn.sub),out=tf36.mul(out,params.bn.truediv),out=tf36.add(out,params.conv.bias),leaky(out)})}var tf37=__toModule(require_tfjs_esm());function depthwiseSeparableConv2(x,params){return tf37.tidy(()=>{let out=tf37.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf37.separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=tf37.add(out,params.bias),leaky(out)})}var tf38=__toModule(require_tfjs_esm());function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub6=tf38.tensor1d(extractWeights(size)),truediv=tf38.tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub6,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub6=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub6,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}},TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(x,params){let out=convWithBatchNorm(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tf39.tidy(()=>{let batchTensor=tf39.cast(input.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(tf39.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input,inputSize){return this.forwardInput(await toNetInput(input),inputSize)}async detect(input,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input),out=await this.forwardInput(netInput,inputSize),out0=tf39.tidy(()=>tf39.unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tf39.tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?tf39.softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):tf39.scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max,curr)=>max.classScore>curr.classScore?max:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},tf41=__toModule(require_tfjs_esm()),tf40=__toModule(require_tfjs_esm());async function extractAllFacesAndComputeResults(parentResults,input,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input instanceof tf40.Tensor?await extractFaceTensors(input,faceBoxes):await extractFaces(input,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof tf40.Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}},nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input,options)=>nets.ssdMobilenetv1.locateFaces(input,options),tinyFaceDetector=(input,options)=>nets.tinyFaceDetector.locateFaces(input,options),tinyYolov2=(input,options)=>nets.tinyYolov2.locateFaces(input,options),detectFaceLandmarks=input=>nets.faceLandmark68Net.detectLandmarks(input),detectFaceLandmarksTiny=input=>nets.faceLandmark68TinyNet.detectLandmarks(input),computeFaceDescriptor=input=>nets.faceRecognitionNet.computeFaceDescriptor(input),recognizeFaceExpressions=input=>nets.faceExpressionNet.predictExpressions(input),predictAgeAndGender=input=>nets.ageGenderNet.predictAgeAndGender(input),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input){super();this.parentTask=parentTask;this.input=input}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{detection}=parentResult,faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input,options=new SsdMobilenetv1Options){super();this.input=input;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input2=>nets.tinyFaceDetector.locateFaces(input2,options):options instanceof SsdMobilenetv1Options?input2=>nets.ssdMobilenetv1.locateFaces(input2,options):options instanceof TinyYolov2Options?input2=>nets.tinyYolov2.locateFaces(input2,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input,options)}function detectAllFaces(input,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input,options)}async function allFacesSsdMobilenetv1(input,minConfidence){return detectAllFaces(input,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input,forwardParams={}){return detectAllFaces(input,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count=1,createUniqueLabel=()=>`person ${count++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json){let labeledDescriptors=json.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser3=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version2={faceapi:version,node,browser:browser3};export{AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw_exports as draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf42 as tf,tinyFaceDetector,tinyYolov2,toNetInput,utils_exports as utils,validateConfig,version2 as version};
+var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);import*as dist_star from"@tensorflow/tfjs/dist/index.js";import*as tfjs_backend_wasm_star from"@tensorflow/tfjs-backend-wasm";var require_tfjs_esm=__commonJS(exports=>{__exportStar(exports,dist_star);__exportStar(exports,tfjs_backend_wasm_star)});var require_isNodejs=__commonJS((exports,module)=>{__export(exports,{isNodejs:()=>isNodejs2});function isNodejs2(){return typeof global=="object"&&!0&&typeof module!="undefined"&&typeof process!="undefined"&&!!process.version}});var tf42=__toModule(require_tfjs_esm());var draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range,round:()=>round});var tf=__toModule(require_tfjs_esm());var Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof tf.Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!=0}function isEven(num){return num%2==0}function round(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale2=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale2),Math.round(height*scale2))}function getCenterPoint(pts){return pts.reduce((sum,pt)=>sum.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range(num,start,step){return Array(num).fill(0).map((_,i)=>start+i*step)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}};var Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}};var BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}};var FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min,x)=>x<min?x:min,Infinity),minY=ys.reduce((min,y)=>y<min?y:min,Infinity),maxX=xs.reduce((max,x)=>max<x?x:max,0),maxY=ys.reduce((max,y)=>max<y?y:max,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}var tf2=__toModule(require_tfjs_esm());function normalize(x,meanRgb){return tf2.tidy(()=>{let[r,g,b]=meanRgb,avg_r=tf2.fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=tf2.fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=tf2.fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=tf2.concat([avg_r,avg_g,avg_b],3);return tf2.sub(x,avg_rgb)})}var tf3=__toModule(require_tfjs_esm());function padToSquare(imgTensor,isCenterImage=!1){return tf3.tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,tf3.fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>tf3.cast(t,"float32"));return tf3.concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array=inputArray.slice();for(let i=array.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array[i];array[i]=array[j],array[j]=x}return array}function sigmoid(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}};var relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json){let descriptors=json.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch=window.fetch;if(!fetch)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer)=>err?reject(err):resolve(buffer))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch,...fileSystem}}function isBrowser(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env2){environment=env2}function initialize(){return isBrowser()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env2){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env2;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env2.createCanvasElement||(()=>new Canvas),environment.createImageElement=env2.createImageElement||(()=>new Image),environment.ImageData=env2.ImageData||environment.ImageData,environment.Video=env2.Video||environment.Video,environment.fetch=env2.fetch||environment.fetch,environment.readFile=env2.readFile||environment.readFile}var env={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}};var DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}var tf18=__toModule(require_tfjs_esm());function isMediaLoaded(media){let{Image,Video}=env.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input){let{Image,Video}=env.getEnv();return input instanceof Image?new Dimensions(input.naturalWidth,input.naturalHeight):input instanceof Video?new Dimensions(input.videoWidth,input.videoHeight):new Dimensions(input.width,input.height)}function createCanvas({width,height}){let{createCanvasElement}=env.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}var tf4=__toModule(require_tfjs_esm());async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tf4.tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await tf4.browser.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input){let{Image,Canvas,Video}=env.getEnv();return input instanceof Image||input instanceof Canvas||input instanceof Video}var tf5=__toModule(require_tfjs_esm());function imageToSquare(input,inputSize,centerImage=!1){let{Image,Canvas}=env.getEnv();if(!(input instanceof Image||input instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input),scale2=inputSize/Math.max(dims.height,dims.width),width=scale2*dims.width,height=scale2*dims.height,targetCanvas=createCanvas({width:inputSize,height:inputSize}),inputCanvas=input instanceof Canvas?input:createCanvasFromMedia(input),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input,idx)=>{if(isTensor3D(input)){this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape;return}if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape.slice(1);return}let canvas=input instanceof env.getEnv().Canvas?input:createCanvasFromMedia(input);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tf5.tidy(()=>{let inputTensors=range(this.batchSize,0,1).map(batchIdx=>{let input=this.getInput(batchIdx);if(input instanceof tf5.Tensor){let imgTensor=isTensor4D(input)?input:input.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=tf5.image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input instanceof env.getEnv().Canvas)return tf5.browser.fromPixels(imageToSquare(input,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)});return tf5.stack(inputTensors.map(t=>tf5.cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input,i)=>{if(!isMediaElement(input)&&!isTensor3D(input)&&!isTensor4D(input))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input=>isMediaElement(input)&&awaitMediaLoaded(input))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input,detections){let{Canvas}=env.getEnv(),canvas=input;if(!(input instanceof Canvas)){let netInput=await toNetInput(input);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}var tf6=__toModule(require_tfjs_esm());async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tf6.tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>tf6.slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init){let{fetch}=env.getEnv(),res=await fetch(url,init);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}var tf7=__toModule(require_tfjs_esm());function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return tf7.io.loadWeights(manifest,modelBaseUri)}function matchDimensions(input,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input.width=width,input.height=height,{width,height}}var tf15=__toModule(require_tfjs_esm());var tf8=__toModule(require_tfjs_esm());var NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof tf8.Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof tf8.Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable})=>{let tensor2=tf8.tensor(variable.dataSync());variable.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights=tf8.io.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof tf8.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}};var tf10=__toModule(require_tfjs_esm());var tf9=__toModule(require_tfjs_esm());function depthwiseSeparableConv(x,params,stride){return tf9.tidy(()=>{let out=tf9.separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=tf9.add(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=tf10.relu(tf10.add(out1,tf10.add(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,tf10.add(out3,out4))))})}var tf11=__toModule(require_tfjs_esm());function convLayer(x,params,padding="same",withRelu=!1){return tf11.tidy(()=>{let out=tf11.add(tf11.conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?tf11.relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}var tf12=__toModule(require_tfjs_esm());function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tf12.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf12.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}var tf13=__toModule(require_tfjs_esm());function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tf13.tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tf13.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var tf14=__toModule(require_tfjs_esm());var SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tf14.tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tf14.tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tf14.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tf15.tidy(()=>{let batchTensor=tf15.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf15.scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=tf15.avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}};var tf17=__toModule(require_tfjs_esm());var tf16=__toModule(require_tfjs_esm());function fullyConnectedLayer(x,params){return tf16.tidy(()=>tf16.add(tf16.matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf17.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}};var FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input){return tf18.tidy(()=>tf18.softmax(this.runNet(input)))}async forward(input){return this.forwardInput(await toNetInput(input))}async predictExpressions(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(tf18.unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version="0.10.2";var tf20=__toModule(require_tfjs_esm());var tf19=__toModule(require_tfjs_esm());function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return tf19.add(tf19.conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?tf19.relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=tf19.maxPool(out,[3,3],[2,2],"same"),out=tf19.add(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(tf19.relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv2,[1,1]),out=tf19.add(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tf19.tidy(()=>{let batchTensor=tf19.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf19.scalar(256)),out=tf19.relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=tf19.relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf20.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input,pooled=tf20.avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input){return tf20.tidy(()=>{let{age,gender}=this.runNet(input);return{age,gender:tf20.softmax(gender)}})}async forward(input){return this.forwardInput(await toNetInput(input))}async predictAgeAndGender(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),ages=tf20.unstack(out.age),genders=tf20.unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var tf21=__toModule(require_tfjs_esm());var FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale2=inputSize/Math.max(height,width);return{width:width*scale2,height:height*scale2}}),batchSize=inputDimensions.length;return tf21.tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>tf21.stack([tf21.fill([68],fillX,"float32"),tf21.fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding(batchIdx,(w,h)=>h<w);return output.mul(tf21.fill([batchSize,136],inputSize,"float32")).sub(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input){return tf21.tidy(()=>{let out=this.runNet(input);return this.postProcess(out,input.inputSize,input.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input){return this.forwardInput(await toNetInput(input))}async detectLandmarks(input){let netInput=await toNetInput(input),landmarkTensors=tf21.tidy(()=>tf21.unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven(i)),yCoords=landmarksArray.filter((_,i)=>!isEven(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}};var FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var tf22=__toModule(require_tfjs_esm());function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tf22.tidy(()=>{let batchTensor=tf22.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf22.scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=tf22.avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}};var FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var FaceLandmarkNet=class extends FaceLandmark68Net{};var tf27=__toModule(require_tfjs_esm());var tf24=__toModule(require_tfjs_esm());var tf23=__toModule(require_tfjs_esm());function scale(x,params){return tf23.add(tf23.mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=tf24.conv2d(x,filters,strides,padding);return out=tf24.add(out,bias),out=scale(out,params.scale),withRelu?tf24.relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}var tf25=__toModule(require_tfjs_esm());function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tf25.tidy(()=>tf25.transpose(tf25.tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tf25.tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tf25.tensor1d(extractWeights(numWeights)),biases=tf25.tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale2=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale2}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tf25.tidy(()=>tf25.transpose(tf25.tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale2=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale2}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf26=__toModule(require_tfjs_esm());function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=tf26.add(out,x),out=tf26.relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=tf26.avgPool(x,2,2,"valid"),zeros2=tf26.zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=tf26.zeros(padShapeX);out=tf26.concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=tf26.zeros(padShapeY);out=tf26.concat([out,zerosH],2)}return pooled=isPad?tf26.concat([pooled,zeros2],3):pooled,out=tf26.add(pooled,out),out=tf26.relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tf27.tidy(()=>{let batchTensor=tf27.cast(input.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf27.scalar(256)),out=convDown(normalized,params.conv32_down);out=tf27.maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return tf27.matMul(globalAvg,params.fc)})}async forward(input){return this.forwardInput(await toNetInput(input))}async computeFaceDescriptor(input){let netInput=await toNetInput(input),faceDescriptorTensors=tf27.tidy(()=>tf27.unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}var tf34=__toModule(require_tfjs_esm());var tf28=__toModule(require_tfjs_esm());function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tf28.tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tf28.tensor1d(extractWeights(numChannels)),batch_norm_offset=tf28.tensor1d(extractWeights(numChannels)),batch_norm_mean=tf28.tensor1d(extractWeights(numChannels)),batch_norm_variance=tf28.tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tf28.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf28.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tf28.tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf30=__toModule(require_tfjs_esm());var tf29=__toModule(require_tfjs_esm());function pointwiseConvLayer(x,params,strides){return tf29.tidy(()=>{let out=tf29.conv2d(x,params.filters,strides,"same");return out=tf29.add(out,params.batch_norm_offset),tf29.clipByValue(out,0,6)})}var epsilon=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tf30.tidy(()=>{let out=tf30.depthwiseConv2d(x,params.filters,strides,"same");return out=tf30.batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon),tf30.clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tf30.tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(selected.length>=outputSize)return;let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}),selected}var tf31=__toModule(require_tfjs_esm());function getCenterCoordinatesAndSizesLayer(x){let vec=tf31.unstack(tf31.transpose(x,[1,0])),sizes=[tf31.sub(vec[2],vec[0]),tf31.sub(vec[3],vec[1])],centers=[tf31.add(vec[0],tf31.div(sizes[0],tf31.scalar(2))),tf31.add(vec[1],tf31.div(sizes[1],tf31.scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=tf31.unstack(tf31.transpose(x1,[1,0])),div0_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2],tf31.scalar(5))),sizes[0]),tf31.scalar(2)),add0_out=tf31.add(tf31.mul(tf31.div(vec[0],tf31.scalar(10)),sizes[0]),centers[0]),div1_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3],tf31.scalar(5))),sizes[1]),tf31.scalar(2)),add1_out=tf31.add(tf31.mul(tf31.div(vec[1],tf31.scalar(10)),sizes[1]),centers[1]);return tf31.transpose(tf31.stack([tf31.sub(add0_out,div0_out),tf31.sub(add1_out,div1_out),tf31.add(add0_out,div0_out),tf31.add(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tf31.tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(tf31.reshape(tf31.tile(params.extra_dim,[batchSize,1,1]),[-1,4]),tf31.reshape(boxPredictions,[-1,4]));boxes=tf31.reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=tf31.sigmoid(tf31.slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=tf31.slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=tf31.reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=tf31.unstack(boxes),scoresByBatch=tf31.unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}var tf33=__toModule(require_tfjs_esm());var tf32=__toModule(require_tfjs_esm());function boxPredictionLayer(x,params){return tf32.tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=tf32.reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=tf32.reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tf33.tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=tf33.concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=tf33.concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tf34.tidy(()=>{let batchTensor=tf34.cast(input.toBatchTensor(512,!1),"float32"),x=tf34.sub(tf34.mul(batchTensor,tf34.scalar(.007843137718737125)),tf34.scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input){return this.forwardInput(await toNetInput(input))}async locateFaces(input,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression2(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{};var IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model";var tf39=__toModule(require_tfjs_esm());var isNumber=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber(a.x)&&isNumber(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}var tf36=__toModule(require_tfjs_esm());var tf35=__toModule(require_tfjs_esm());function leaky(x){return tf35.tidy(()=>{let min=tf35.mul(x,tf35.scalar(.10000000149011612));return tf35.add(tf35.relu(tf35.sub(x,min)),min)})}function convWithBatchNorm(x,params){return tf36.tidy(()=>{let out=tf36.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf36.conv2d(out,params.conv.filters,[1,1],"valid"),out=tf36.sub(out,params.bn.sub),out=tf36.mul(out,params.bn.truediv),out=tf36.add(out,params.conv.bias),leaky(out)})}var tf37=__toModule(require_tfjs_esm());function depthwiseSeparableConv2(x,params){return tf37.tidy(()=>{let out=tf37.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf37.separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=tf37.add(out,params.bias),leaky(out)})}var tf38=__toModule(require_tfjs_esm());function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub6=tf38.tensor1d(extractWeights(size)),truediv=tf38.tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub6,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub6=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub6,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(x,params){let out=convWithBatchNorm(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tf39.tidy(()=>{let batchTensor=tf39.cast(input.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(tf39.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input,inputSize){return this.forwardInput(await toNetInput(input),inputSize)}async detect(input,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input),out=await this.forwardInput(netInput,inputSize),out0=tf39.tidy(()=>tf39.unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tf39.tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?tf39.softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):tf39.scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max,curr)=>max.classScore>curr.classScore?max:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var tf41=__toModule(require_tfjs_esm());var tf40=__toModule(require_tfjs_esm());async function extractAllFacesAndComputeResults(parentResults,input,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input instanceof tf40.Tensor?await extractFaceTensors(input,faceBoxes):await extractFaces(input,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof tf40.Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404];var TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};var nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input,options)=>nets.ssdMobilenetv1.locateFaces(input,options),tinyFaceDetector=(input,options)=>nets.tinyFaceDetector.locateFaces(input,options),tinyYolov2=(input,options)=>nets.tinyYolov2.locateFaces(input,options),detectFaceLandmarks=input=>nets.faceLandmark68Net.detectLandmarks(input),detectFaceLandmarksTiny=input=>nets.faceLandmark68TinyNet.detectLandmarks(input),computeFaceDescriptor=input=>nets.faceRecognitionNet.computeFaceDescriptor(input),recognizeFaceExpressions=input=>nets.faceExpressionNet.predictExpressions(input),predictAgeAndGender=input=>nets.ageGenderNet.predictAgeAndGender(input),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks;var PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input){super();this.parentTask=parentTask;this.input=input}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}};var DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{detection}=parentResult,faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var DetectFacesTaskBase=class extends ComposableTask{constructor(input,options=new SsdMobilenetv1Options){super();this.input=input;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input2=>nets.tinyFaceDetector.locateFaces(input2,options):options instanceof SsdMobilenetv1Options?input2=>nets.ssdMobilenetv1.locateFaces(input2,options):options instanceof TinyYolov2Options?input2=>nets.tinyYolov2.locateFaces(input2,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input,options)}function detectAllFaces(input,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input,options)}async function allFacesSsdMobilenetv1(input,minConfidence){return detectAllFaces(input,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input,forwardParams={}){return detectAllFaces(input,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count=1,createUniqueLabel=()=>`person ${count++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json){let labeledDescriptors=json.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser3=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version2={faceapi:version,node,browser:browser3};export{AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw_exports as draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf42 as tf,tinyFaceDetector,tinyYolov2,toNetInput,utils_exports as utils,validateConfig,version2 as version};
 //# sourceMappingURL=face-api.esm-nobundle.js.map
diff --git a/dist/face-api.esm-nobundle.js.map b/dist/face-api.esm-nobundle.js.map
index 07403e9..4d0d7b1 100644
--- a/dist/face-api.esm-nobundle.js.map
+++ b/dist/face-api.esm-nobundle.js.map
@@ -2,6 +2,6 @@
   "version": 3,
   "sources": ["../src/tfjs/tf-browser.ts", "../src/env/isNodejs.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts"],
   "sourcesContent": ["/* eslint-disable import/no-extraneous-dependencies */\n/* eslint-disable node/no-unpublished-import */\n\n// wrapper to load tfjs in a single place so version can be changed quickly\n\nexport * from '@tensorflow/tfjs/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm';\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\n// eslint-disable-next-line no-undef\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  // eslint-disable-next-line no-mixed-operators\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n\n    const { readFile } = env.getEnv();\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)),\n    );\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParamsFromWeigthMap(weightMap);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParams(weights);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input);\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      (t) => t.data(),\n    )) as Float32Array[];\n\n    faceDescriptorTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    // TODO batches\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data());\n\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) {\n      return;\n    }\n    const originalScore = c.score;\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n\n    return {\n      boxPredictionEncoding,\n      classPrediction,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024,\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n    // return tf.maximum(x, min)\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config';\nexport * from './types';\n\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n"],
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         "src/ssdMobilenetv1/extractParamsFromWeigthMap.ts": {
           "bytesInOutput": 4319
         },
         "src/ssdMobilenetv1/mobileNetV1.ts": {
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         },
         "src/ssdMobilenetv1/pointwiseConvLayer.ts": {
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         "src/ssdMobilenetv1/nonMaxSuppression.ts": {
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         },
         "src/ssdMobilenetv1/outputLayer.ts": {
           "bytesInOutput": 1590
         },
         "src/ssdMobilenetv1/predictionLayer.ts": {
-          "bytesInOutput": 1418
+          "bytesInOutput": 1419
         },
         "src/ssdMobilenetv1/boxPredictionLayer.ts": {
-          "bytesInOutput": 350
+          "bytesInOutput": 354
         },
         "src/ssdMobilenetv1/SsdMobilenetv1Options.ts": {
-          "bytesInOutput": 554
+          "bytesInOutput": 555
         },
         "src/ssdMobilenetv1/index.ts": {
-          "bytesInOutput": 234
+          "bytesInOutput": 235
         },
         "src/tinyYolov2/const.ts": {
-          "bytesInOutput": 485
+          "bytesInOutput": 490
         },
         "src/tinyYolov2/TinyYolov2Base.ts": {
-          "bytesInOutput": 5885
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         },
         "src/tinyYolov2/config.ts": {
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         },
         "src/tinyYolov2/convWithBatchNorm.ts": {
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         },
         "src/tinyYolov2/leaky.ts": {
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+          "bytesInOutput": 182
         },
         "src/tinyYolov2/depthwiseSeparableConv.ts": {
           "bytesInOutput": 293
@@ -2678,37 +2678,37 @@
           "bytesInOutput": 139
         },
         "src/tinyFaceDetector/TinyFaceDetectorOptions.ts": {
-          "bytesInOutput": 132
+          "bytesInOutput": 133
         },
         "src/globalApi/ComposableTask.ts": {
-          "bytesInOutput": 154
+          "bytesInOutput": 159
         },
         "src/globalApi/DetectFaceLandmarksTasks.ts": {
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+          "bytesInOutput": 1926
         },
         "src/globalApi/extractFacesAndComputeResults.ts": {
-          "bytesInOutput": 812
+          "bytesInOutput": 816
         },
         "src/tinyFaceDetector/const.ts": {
-          "bytesInOutput": 215
+          "bytesInOutput": 216
         },
         "src/tinyFaceDetector/TinyFaceDetector.ts": {
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+          "bytesInOutput": 629
         },
         "src/globalApi/nets.ts": {
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+          "bytesInOutput": 1520
         },
         "src/globalApi/PredictFaceExpressionsTask.ts": {
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         },
         "src/globalApi/PredictAgeAndGenderTask.ts": {
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+          "bytesInOutput": 1794
         },
         "src/globalApi/ComputeFaceDescriptorsTasks.ts": {
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+          "bytesInOutput": 1399
         },
         "src/globalApi/DetectFacesTasks.ts": {
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+          "bytesInOutput": 2225
         },
         "src/globalApi/detectFaces.ts": {
           "bytesInOutput": 224
@@ -2732,7 +2732,7 @@
           "bytesInOutput": 824
         }
       },
-      "bytes": 129069
+      "bytes": 129272
     }
   }
 }
diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js
index b1a6736..2773a3b 100644
--- a/dist/face-api.esm.js
+++ b/dist/face-api.esm.js
@@ -5,43 +5,43 @@
   author: <https://github.com/vladmandic>'
   */
 
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k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,k==strseed.length&&(me.d=me.x<<10^me.x>>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift7=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j<seed2.length;++j)X[j&7]=X[j&7]<<15^seed2.charCodeAt(j)+X[j+1&7]<<13;for(;X.length<8;)X.push(0);for(j=0;j<8&&X[j]===0;++j);for(j==8?w=X[7]=-1:w=X[j],me2.x=X,me2.i=0,j=256;j>0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor4096=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j<limit;++j)seed2&&(v^=seed2.charCodeAt((j+32)%seed2.length)),j===0&&(w=v),v^=v<<10,v^=v>>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&&copy(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten4(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten4(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser2=global2.navigator,plugins=browser2&&browser2.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool3)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool3),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)}),require_seedrandom2=__commonJS2((exports,module)=>{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_string_decoder=__commonJS2(()=>{}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data=read(f,"binary"),assert3(typeof data=="object"),data)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:167,maximum:167+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack2===0&&(stack2=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack2!==0&&stackRestore(stack2),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,str="";!(idx>=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i<str.length;++i){var u=str.charCodeAt(i);u>=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i<pthreadPoolSize;++i)PThread.allocateUnusedWorker();PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i],pthread=worker.pthread;PThread.freeThreadData(pthread),worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(pthread){if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data){worker.onmessage({data})}),worker.on("error",function(data){worker.onerror(data)}),worker.on("exit",function(data){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()<t;);}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop,STACK_MAX=stackMax,stackRestore(stackTop)}Module.establishStackSpace=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module.getNoExitRuntime=getNoExitRuntime;function ___assert_fail(condition,filename,line,func2){abort("Assertion failed: "+UTF8ToString(condition)+", at: "+[filename?UTF8ToString(filename):"unknown filename",line,func2?UTF8ToString(func2):"unknown function"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;ENVIRONMENT_IS_NODE?_emscripten_get_now=function(){var t=process.hrtime();return t[0]*1e3+t[1]/1e6}:ENVIRONMENT_IS_PTHREAD?_emscripten_get_now=function(){return performance.now()-Module.__performance_now_clock_drift}:typeof dateNow!="undefined"?_emscripten_get_now=dateNow:_emscripten_get_now=function(){return performance.now()};function setErrNo(value){return GROWABLE_HEAP_I32()[___errno_location()>>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i<numCallArgs;i++)GROWABLE_HEAP_F64()[b+i]=arguments[2+i];var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);return stackRestore(stack2),ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgs.array||(readAsmConstArgs.array=[]);var args=readAsmConstArgs.array;args.length=0;for(var ch;ch=GROWABLE_HEAP_U8()[sigPtr++];)ch===100||ch===102?(buf=buf+7&~7,args.push(GROWABLE_HEAP_F64()[buf>>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i<numCallArgs;i++)_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i];var isEmAsmConst=index<0,func2=isEmAsmConst?ASM_CONSTS[-index-1]:proxiedFunctionTable[index];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1],varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2],constArgs=readAsmConstArgs(sigPtr,varargPtr);return func2.apply(null,constArgs)}return func2.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{return wasmMemory.grow(size-buffer2.byteLength+65535>>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i)JSEvents.deferredCalls[i].targetFunction==targetFunction&&(JSEvents.deferredCalls.splice(i,1),--i)},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(JSEvents.canPerformEventHandlerRequests())for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1),--i,call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)&&JSEvents._removeHandler(i--)},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture),JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function(event){++JSEvents.inEventHandler,JSEvents.currentEventHandler=eventHandler,JSEvents.runDeferredCalls(),eventHandler.handlerFunc(event),JSEvents.runDeferredCalls(),--JSEvents.inEventHandler};if(eventHandler.callbackfunc)eventHandler.eventListenerFunc=jsEventHandler,eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture),JSEvents.eventHandlers.push(eventHandler),JSEvents.registerRemoveEventListeners();else for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString&&JSEvents._removeHandler(i--)},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave(),varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1);for(var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)},recordError:function(errorCode){GL.lastError||(GL.lastError=errorCode)},getNewId:function(table){for(var ret=GL.counter++,i=table.length;i<ret;i++)table[i]=null;return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count2,string,length){for(var source="",i=0;i<count2;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p2,i),name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1),name.slice(-1)=="]"&&(name=name.slice(0,name.lastIndexOf("[")));var loc=GLctx.getUniformLocation(p2,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id],GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+"["+j+"]";loc=GLctx.getUniformLocation(p2,n),id=GL.getNewId(GL.uniforms),GL.uniforms[id]=loc}}}}},__emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={},a=attributes>>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i<iovcnt;i++){for(var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j]);num+=len}return GROWABLE_HEAP_I32()[pnum>>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 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cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,endPtr=idx;heap[endPtr]&&!(endPtr>=endIdx);)++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder)return UTF8Decoder.decode(heap.subarray(idx,endPtr));for(var str="";idx<endPtr;){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array2,buffer3){HEAP8.set(array2,buffer3)}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATPOSTRUN__=[],runtimeInitialized=!1,runtimeExited=!1;function preRun(){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=!0}function postRun(){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). 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fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){for(var num=0,i=0;i<iovcnt;i++){for(var ptr=HEAP32[iov+i*8>>2],len=HEAP32[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,HEAPU8[ptr+j]);num+=len}return HEAP32[pnum>>2]=num,0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}var asmLibraryArg={emscripten_notify_memory_growth:_emscripten_notify_memory_growth,fd_close:_fd_close,fd_seek:_fd_seek,fd_write:_fd_write,proc_exit:_proc_exit,roundf:_roundf},asm=createWasm();Module.asm=asm;var 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Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config]=value,[backend2]=key.split("_");backend2===backendName&&result.push(config)}return result}function registerKernel(config){let{kernelName,backendName}=config,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config)}function registerGradient(config){let{kernelName}=config;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now(){return env().platform.now()}function fetch2(path,requestInits){return env().platform.fetch(path,requestInits)}function encodeString(s,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.encode(s,encoding)}function decodeString(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.decode(bytes,encoding)}var Profiler=class{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad(name,25),rank=result.rank,size=result.size,shape=rightPad(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape.length;i++){let node2=tape[i],nodeInputs=node2.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node2.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node2.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node2=tape[i],nodeInputs=node2.inputs;for(let j=0;j<node2.outputs.length;j++)if(tensorsLeadToY[node2.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node2.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape.length;i++){let node2=tape[i];if(nodesFromX[node2.id]&&nodesToY[node2.id]){let prunedInputs={};for(let inputName in node2.inputs){let nodeInput=node2.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node2);prunedNode.inputs=prunedInputs,prunedNode.outputs=node2.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients(tensorAccumulatedGradientMap,filteredTape,tidy2,add5){for(let i=filteredTape.length-1;i>=0;i--){let node2=filteredTape[i],dys=[];if(node2.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node2.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);let inputGradients=node2.gradient(dys);for(let inputName in node2.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node2.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
+var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);var require_isNodejs=__commonJS((exports,module)=>{__export(exports,{isNodejs:()=>isNodejs2});function isNodejs2(){return typeof global=="object"&&!0&&typeof module!="undefined"&&typeof process!="undefined"&&!!process.version}});var tfjs_esm_exports={};__export(tfjs_esm_exports,{Abs:()=>Abs,Acos:()=>Acos,Acosh:()=>Acosh,AdadeltaOptimizer:()=>AdadeltaOptimizer,AdagradOptimizer:()=>AdagradOptimizer,AdamOptimizer:()=>AdamOptimizer,AdamaxOptimizer:()=>AdamaxOptimizer,Add:()=>Add,AddN:()=>AddN,All:()=>All,Any:()=>Any,ArgMax:()=>ArgMax,ArgMin:()=>ArgMin,Asin:()=>Asin,Asinh:()=>Asinh,Atan:()=>Atan,Atan2:()=>Atan2,Atanh:()=>Atanh,AvgPool:()=>AvgPool,AvgPool3D:()=>AvgPool3D,AvgPool3DGrad:()=>AvgPool3DGrad,AvgPoolGrad:()=>AvgPoolGrad,BackendWasm:()=>BackendWasm,BatchMatMul:()=>BatchMatMul,BatchToSpaceND:()=>BatchToSpaceND,Bincount:()=>Bincount,BroadcastTo:()=>BroadcastTo,Callback:()=>Callback,CallbackList:()=>CallbackList,Cast:()=>Cast,Ceil:()=>Ceil,ClipByValue:()=>ClipByValue,Complex:()=>Complex,ComplexAbs:()=>ComplexAbs,Concat:()=>Concat,Conv2D:()=>Conv2D,Conv2DBackpropFilter:()=>Conv2DBackpropFilter,Conv2DBackpropInput:()=>Conv2DBackpropInput,Conv3D:()=>Conv3D,Conv3DBackpropFilterV2:()=>Conv3DBackpropFilterV2,Conv3DBackpropInputV2:()=>Conv3DBackpropInputV2,Cos:()=>Cos,Cosh:()=>Cosh,CropAndResize:()=>CropAndResize,Cumsum:()=>Cumsum,CustomCallback:()=>CustomCallback,DataStorage:()=>DataStorage,DenseBincount:()=>DenseBincount,DepthToSpace:()=>DepthToSpace,DepthwiseConv2dNative:()=>DepthwiseConv2dNative,DepthwiseConv2dNativeBackpropFilter:()=>DepthwiseConv2dNativeBackpropFilter,DepthwiseConv2dNativeBackpropInput:()=>DepthwiseConv2dNativeBackpropInput,Diag:()=>Diag,Dilation2D:()=>Dilation2D,Dilation2DBackpropFilter:()=>Dilation2DBackpropFilter,Dilation2DBackpropInput:()=>Dilation2DBackpropInput,ENV:()=>ENV,EarlyStopping:()=>EarlyStopping,Elu:()=>Elu,EluGrad:()=>EluGrad,Environment:()=>Environment,Equal:()=>Equal,Erf:()=>Erf,Exp:()=>Exp,ExpandDims:()=>ExpandDims,Expm1:()=>Expm1,FFT:()=>FFT,Fill:()=>Fill,FlipLeftRight:()=>FlipLeftRight,Floor:()=>Floor,FloorDiv:()=>FloorDiv,FromPixels:()=>FromPixels,FusedBatchNorm:()=>FusedBatchNorm,FusedConv2D:()=>FusedConv2D,FusedDepthwiseConv2D:()=>FusedDepthwiseConv2D,GatherNd:()=>GatherNd,GatherV2:()=>GatherV2,GraphModel:()=>GraphModel,Greater:()=>Greater,GreaterEqual:()=>GreaterEqual,History:()=>History,IFFT:()=>IFFT,Identity:()=>Identity,Imag:()=>Imag,InputSpec:()=>InputSpec,IsFinite:()=>IsFinite,IsInf:()=>IsInf,IsNan:()=>IsNan,KernelBackend:()=>KernelBackend,LRN:()=>LRN,LRNGrad:()=>LRNGrad,LayerVariable:()=>LayerVariable,LayersModel:()=>LayersModel,LeakyRelu:()=>LeakyRelu,Less:()=>Less,LessEqual:()=>LessEqual,LinSpace:()=>LinSpace,Log:()=>Log,Log1p:()=>Log1p,LogSoftmax:()=>LogSoftmax,LogicalAnd:()=>LogicalAnd,LogicalNot:()=>LogicalNot,LogicalOr:()=>LogicalOr,Max:()=>Max,MaxPool:()=>MaxPool,MaxPool3D:()=>MaxPool3D,MaxPool3DGrad:()=>MaxPool3DGrad,MaxPoolGrad:()=>MaxPoolGrad,MaxPoolWithArgmax:()=>MaxPoolWithArgmax,Maximum:()=>Maximum,Mean:()=>Mean,Min:()=>Min,Minimum:()=>Minimum,MirrorPad:()=>MirrorPad,Mod:()=>Mod,MomentumOptimizer:()=>MomentumOptimizer,Multinomial:()=>Multinomial,Multiply:()=>Multiply,Neg:()=>Neg,NonMaxSuppressionV3:()=>NonMaxSuppressionV3,NonMaxSuppressionV4:()=>NonMaxSuppressionV4,NonMaxSuppressionV5:()=>NonMaxSuppressionV5,NotEqual:()=>NotEqual,OP_SCOPE_SUFFIX:()=>OP_SCOPE_SUFFIX,OneHot:()=>OneHot,OnesLike:()=>OnesLike,Optimizer:()=>Optimizer,Pack:()=>Pack,PadV2:()=>PadV2,Pool:()=>Pool,Pow:()=>Pow,Prelu:()=>Prelu,Prod:()=>Prod,RMSPropOptimizer:()=>RMSPropOptimizer,RNN:()=>RNN,Range:()=>Range,Rank:()=>Rank,Real:()=>Real,RealDiv:()=>RealDiv,Reciprocal:()=>Reciprocal,Reduction:()=>Reduction,Relu:()=>Relu,Relu6:()=>Relu6,Reshape:()=>Reshape,ResizeBilinear:()=>ResizeBilinear,ResizeBilinearGrad:()=>ResizeBilinearGrad,ResizeNearestNeighbor:()=>ResizeNearestNeighbor,ResizeNearestNeighborGrad:()=>ResizeNearestNeighborGrad,Reverse:()=>Reverse,RotateWithOffset:()=>RotateWithOffset,Round:()=>Round,Rsqrt:()=>Rsqrt,SGDOptimizer:()=>SGDOptimizer,ScatterNd:()=>ScatterNd,Select:()=>Select,Selu:()=>Selu,Sequential:()=>Sequential,Sigmoid:()=>Sigmoid,Sign:()=>Sign,Sin:()=>Sin,Sinh:()=>Sinh,Slice:()=>Slice,Softmax:()=>Softmax,Softplus:()=>Softplus,SpaceToBatchND:()=>SpaceToBatchND,SparseToDense:()=>SparseToDense,SplitV:()=>SplitV,Sqrt:()=>Sqrt,Square:()=>Square,SquaredDifference:()=>SquaredDifference,Step:()=>Step,StridedSlice:()=>StridedSlice,Sub:()=>Sub,Sum:()=>Sum,SymbolicTensor:()=>SymbolicTensor,Tan:()=>Tan,Tanh:()=>Tanh,Tensor:()=>Tensor,TensorBuffer:()=>TensorBuffer,Tile:()=>Tile,TopK:()=>TopK,Transpose:()=>Transpose,Unique:()=>Unique,Unpack:()=>Unpack,UnsortedSegmentSum:()=>UnsortedSegmentSum,Variable:()=>Variable,ZerosLike:()=>ZerosLike,_FusedMatMul:()=>_FusedMatMul,abs:()=>abs,acos:()=>acos,acosh:()=>acosh,add:()=>add2,addN:()=>addN,addStrict:()=>addStrict,all:()=>all,any:()=>any,argMax:()=>argMax,argMin:()=>argMin,asin:()=>asin,asinh:()=>asinh,atan:()=>atan,atan2:()=>atan2,atanh:()=>atanh,avgPool:()=>avgPool,avgPool3d:()=>avgPool3d,backend:()=>backend,backend_util:()=>backend_util_exports,basicLSTMCell:()=>basicLSTMCell,batchNorm:()=>batchNorm,batchNorm2d:()=>batchNorm2d,batchNorm3d:()=>batchNorm3d,ba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copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&&copy(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten4(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten4(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser2=global2.navigator,plugins=browser2&&browser2.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool3)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool3),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)}),require_seedrandom2=__commonJS2((exports,module)=>{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_string_decoder=__commonJS2(()=>{}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data=read(f,"binary"),assert3(typeof data=="object"),data)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack2===0&&(stack2=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack2!==0&&stackRestore(stack2),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,str="";!(idx>=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i<str.length;++i){var u=str.charCodeAt(i);u>=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i<pthreadPoolSize;++i)PThread.allocateUnusedWorker();PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i],pthread=worker.pthread;PThread.freeThreadData(pthread),worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(pthread){if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data){worker.onmessage({data})}),worker.on("error",function(data){worker.onerror(data)}),worker.on("exit",function(data){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()<t;);}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop,STACK_MAX=stackMax,stackRestore(stackTop)}Module.establishStackSpace=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module.getNoExitRuntime=getNoExitRuntime;function ___assert_fail(condition,filename,line,func2){abort("Assertion failed: "+UTF8ToString(condition)+", at: "+[filename?UTF8ToString(filename):"unknown filename",line,func2?UTF8ToString(func2):"unknown function"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;ENVIRONMENT_IS_NODE?_emscripten_get_now=function(){var t=process.hrtime();return t[0]*1e3+t[1]/1e6}:ENVIRONMENT_IS_PTHREAD?_emscripten_get_now=function(){return performance.now()-Module.__performance_now_clock_drift}:typeof dateNow!="undefined"?_emscripten_get_now=dateNow:_emscripten_get_now=function(){return performance.now()};function setErrNo(value){return GROWABLE_HEAP_I32()[___errno_location()>>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i<numCallArgs;i++)GROWABLE_HEAP_F64()[b+i]=arguments[2+i];var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);return stackRestore(stack2),ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgs.array||(readAsmConstArgs.array=[]);var args=readAsmConstArgs.array;args.length=0;for(var ch;ch=GROWABLE_HEAP_U8()[sigPtr++];)ch===100||ch===102?(buf=buf+7&~7,args.push(GROWABLE_HEAP_F64()[buf>>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i<numCallArgs;i++)_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i];var isEmAsmConst=index<0,func2=isEmAsmConst?ASM_CONSTS[-index-1]:proxiedFunctionTable[index];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1],varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2],constArgs=readAsmConstArgs(sigPtr,varargPtr);return func2.apply(null,constArgs)}return func2.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{return wasmMemory.grow(size-buffer2.byteLength+65535>>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i)JSEvents.deferredCalls[i].targetFunction==targetFunction&&(JSEvents.deferredCalls.splice(i,1),--i)},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(JSEvents.canPerformEventHandlerRequests())for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1),--i,call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)&&JSEvents._removeHandler(i--)},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture),JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function(event){++JSEvents.inEventHandler,JSEvents.currentEventHandler=eventHandler,JSEvents.runDeferredCalls(),eventHandler.handlerFunc(event),JSEvents.runDeferredCalls(),--JSEvents.inEventHandler};if(eventHandler.callbackfunc)eventHandler.eventListenerFunc=jsEventHandler,eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture),JSEvents.eventHandlers.push(eventHandler),JSEvents.registerRemoveEventListeners();else for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString&&JSEvents._removeHandler(i--)},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave(),varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1);for(var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)},recordError:function(errorCode){GL.lastError||(GL.lastError=errorCode)},getNewId:function(table){for(var ret=GL.counter++,i=table.length;i<ret;i++)table[i]=null;return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count2,string,length){for(var source="",i=0;i<count2;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p2,i),name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1),name.slice(-1)=="]"&&(name=name.slice(0,name.lastIndexOf("[")));var loc=GLctx.getUniformLocation(p2,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id],GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+"["+j+"]";loc=GLctx.getUniformLocation(p2,n),id=GL.getNewId(GL.uniforms),GL.uniforms[id]=loc}}}}},__emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={},a=attributes>>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i<iovcnt;i++){for(var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j]);num+=len}return GROWABLE_HEAP_I32()[pnum>>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array2,buffer3){HEAP8.set(array2,buffer3)}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATPOSTRUN__=[],runtimeInitialized=!1,runtimeExited=!1;function preRun(){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=!0}function postRun(){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={env:asmLibraryArg,wasi_snapshot_preview1:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;Module.asm=exports3,wasmMemory=exports3.memory,updateGlobalBufferAndViews(wasmMemory.buffer),removeRunDependency("wasm-instantiate")}addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){for(var num=0,i=0;i<iovcnt;i++){for(var ptr=HEAP32[iov+i*8>>2],len=HEAP32[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,HEAPU8[ptr+j]);num+=len}return HEAP32[pnum>>2]=num,0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}var asmLibraryArg={emscripten_notify_memory_growth:_emscripten_notify_memory_growth,fd_close:_fd_close,fd_seek:_fd_seek,fd_write:_fd_write,proc_exit:_proc_exit,roundf:_roundf},asm=createWasm();Module.asm=asm;var _init=Module._init=function(){return(_init=Module._init=Module.asm.init).apply(null,arguments)},_register_tensor=Module._register_tensor=function(){return(_register_tensor=Module._register_tensor=Module.asm.register_tensor).apply(null,arguments)},_dispose_data=Module._dispose_data=function(){return(_dispose_data=Module._dispose_data=Module.asm.dispose_data).apply(null,arguments)},_dispose=Module._dispose=function(){return(_dispose=Module._dispose=Module.asm.dispose).apply(null,arguments)},_Abs=Module._Abs=function(){return(_Abs=Module._Abs=Module.asm.Abs).apply(null,arguments)},_Add=Module._Add=function(){return(_Add=Module._Add=Module.asm.Add).apply(null,arguments)},_AddN=Module._AddN=function(){return(_AddN=Module._AddN=Module.asm.AddN).apply(null,arguments)},_ArgMax=Module._ArgMax=function(){return(_ArgMax=Module._ArgMax=Module.asm.ArgMax).apply(null,arguments)},_AvgPool=Module._AvgPool=function(){return(_AvgPool=Module._AvgPool=Module.asm.AvgPool).apply(null,arguments)},_BatchMatMul=Module._BatchMatMul=function(){return(_BatchMatMul=Module._BatchMatMul=Module.asm.BatchMatMul).apply(null,arguments)},_ClipByValue=Module._ClipByValue=function(){return(_ClipByValue=Module._ClipByValue=Module.asm.ClipByValue).apply(null,arguments)},_Conv2D=Module._Conv2D=function(){return(_Conv2D=Module._Conv2D=Module.asm.Conv2D).apply(null,arguments)},_Conv2DBackpropInput=Module._Conv2DBackpropInput=function(){return(_Conv2DBackpropInput=Module._Conv2DBackpropInput=Module.asm.Conv2DBackpropInput).apply(null,arguments)},_Cos=Module._Cos=function(){return(_Cos=Module._Cos=Module.asm.Cos).apply(null,arguments)},_CropAndResize=Module._CropAndResize=function(){return(_CropAndResize=Module._CropAndResize=Module.asm.CropAndResize).apply(null,arguments)},_Cumsum=Module._Cumsum=function(){return(_Cumsum=Module._Cumsum=Module.asm.Cumsum).apply(null,arguments)},_DepthToSpace=Module._DepthToSpace=function(){return(_DepthToSpace=Module._DepthToSpace=Module.asm.DepthToSpace).apply(null,arguments)},_DepthwiseConv2dNative=Module._DepthwiseConv2dNative=function(){return(_DepthwiseConv2dNative=Module._DepthwiseConv2dNative=Module.asm.DepthwiseConv2dNative).apply(null,arguments)},_Equal=Module._Equal=function(){return(_Equal=Module._Equal=Module.asm.Equal).apply(null,arguments)},_Exp=Module._Exp=function(){return(_Exp=Module._Exp=Module.asm.Exp).apply(null,arguments)},_FlipLeftRight=Module._FlipLeftRight=function(){return(_FlipLeftRight=Module._FlipLeftRight=Module.asm.FlipLeftRight).apply(null,arguments)},_Floor=Module._Floor=function(){return(_Floor=Module._Floor=Module.asm.Floor).apply(null,arguments)},_FloorDiv=Module._FloorDiv=function(){return(_FloorDiv=Module._FloorDiv=Module.asm.FloorDiv).apply(null,arguments)},_FusedBatchNorm=Module._FusedBatchNorm=function(){return(_FusedBatchNorm=Module._FusedBatchNorm=Module.asm.FusedBatchNorm).apply(null,arguments)},_FusedConv2D=Module._FusedConv2D=function(){return(_FusedConv2D=Module._FusedConv2D=Module.asm.FusedConv2D).apply(null,arguments)},_FusedDepthwiseConv2D=Module._FusedDepthwiseConv2D=function(){return(_FusedDepthwiseConv2D=Module._FusedDepthwiseConv2D=Module.asm.FusedDepthwiseConv2D).apply(null,arguments)},_Gather=Module._Gather=function(){return(_Gather=Module._Gather=Module.asm.Gather).apply(null,arguments)},_GatherNd=Module._GatherNd=function(){return(_GatherNd=Module._GatherNd=Module.asm.GatherNd).apply(null,arguments)},_Greater=Module._Greater=function(){return(_Greater=Module._Greater=Module.asm.Greater).apply(null,arguments)},_GreaterEqual=Module._GreaterEqual=function(){return(_GreaterEqual=Module._GreaterEqual=Module.asm.GreaterEqual).apply(null,arguments)},_LeakyRelu=Module._LeakyRelu=function(){return(_LeakyRelu=Module._LeakyRelu=Module.asm.LeakyRelu).apply(null,arguments)},_Less=Module._Less=function(){return(_Less=Module._Less=Module.asm.Less).apply(null,arguments)},_LessEqual=Module._LessEqual=function(){return(_LessEqual=Module._LessEqual=Module.asm.LessEqual).apply(null,arguments)},_Log=Module._Log=function(){return(_Log=Module._Log=Module.asm.Log).apply(null,arguments)},_LogicalAnd=Module._LogicalAnd=function(){return(_LogicalAnd=Module._LogicalAnd=Module.asm.LogicalAnd).apply(null,arguments)},_Max=Module._Max=function(){return(_Max=Module._Max=Module.asm.Max).apply(null,arguments)},_MaxPool=Module._MaxPool=function(){return(_MaxPool=Module._MaxPool=Module.asm.MaxPool).apply(null,arguments)},_Maximum=Module._Maximum=function(){return(_Maximum=Module._Maximum=Module.asm.Maximum).apply(null,arguments)},_Mean=Module._Mean=function(){return(_Mean=Module._Mean=Module.asm.Mean).apply(null,arguments)},_Min=Module._Min=function(){return(_Min=Module._Min=Module.asm.Min).apply(null,arguments)},_Minimum=Module._Minimum=function(){return(_Minimum=Module._Minimum=Module.asm.Minimum).apply(null,arguments)},_Multiply=Module._Multiply=function(){return(_Multiply=Module._Multiply=Module.asm.Multiply).apply(null,arguments)},_Neg=Module._Neg=function(){return(_Neg=Module._Neg=Module.asm.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calledRun;Module.then=function(func2){if(calledRun)func2(Module);else{var old=Module.onRuntimeInitialized;Module.onRuntimeInitialized=function(){old&&old(),func2(Module)}}return Module};function ExitStatus(status){this.name="ExitStatus",this.message="Program terminated with exit("+status+")",this.status=status}var calledMain=!1;dependenciesFulfilled=function runCaller(){calledRun||run(),calledRun||(dependenciesFulfilled=runCaller)};function callMain(args){var entryFunction=Module.__start;try{entryFunction();var ret=0;exit(ret,!0)}catch(e){if(e instanceof ExitStatus)return;if(e=="unwind"){noExitRuntime=!0;return}else{var toLog=e;e&&typeof e=="object"&&e.stack&&(toLog=[e,e.stack]),err("exception thrown: "+toLog),quit_(1,e)}}finally{calledMain=!0}}function run(args){if(args=args||arguments_,runDependencies>0||(preRun(),runDependencies>0))return;function doRun(){calledRun||(calledRun=!0,Module.calledRun=!0,!ABORT&&(initRuntime(),preMain(),Module.onRuntimeInitialized&&Module.onRuntimeInitialized(),shouldRunNow&&callMain(args),postRun()))}Module.setStatus?(Module.setStatus("Running..."),setTimeout(function(){setTimeout(function(){Module.setStatus("")},1),doRun()},1)):doRun()}Module.run=run;function exit(status,implicit){implicit&&noExitRuntime&&status===0||(noExitRuntime||(ABORT=!0,EXITSTATUS=status,exitRuntime(),Module.onExit&&Module.onExit(status)),quit_(status,new ExitStatus(status)))}if(Module.preInit)for(typeof Module.preInit=="function"&&(Module.preInit=[Module.preInit]);Module.preInit.length>0;)Module.preInit.pop()();var shouldRunNow=!0;return Module.noInitialRun&&(shouldRunNow=!1),noExitRuntime=!0,run(),WasmBackendModule2}}();typeof exports=="object"&&typeof module=="object"?module.exports=WasmBackendModule:typeof define=="function"&&define.amd?define([],function(){return WasmBackendModule}):typeof 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urlParams=getQueryParams(this.global.location.search);TENSORFLOWJS_FLAGS_PREFIX in urlParams&&urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",").forEach(keyValue=>{let[key,value]=keyValue.split(":");this.urlFlags[key]=parseValue(key,value)})}};function getQueryParams(queryString){let params={};return queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(s,...t)=>(decodeParam(params,t[0],t[1]),t.join("="))),params}function decodeParam(params,name,value){params[decodeURIComponent(name)]=decodeURIComponent(value||"")}function parseValue(flagName,value){if(value=value.toLowerCase(),value==="true"||value==="false")return value==="true";if(`${+value}`===value)return+value;throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`)}function env(){return ENV}var ENV=null;function setEnvironmentGlobal(environment2){ENV=environment2}var globalNameSpace;function getGlobalNamespace(){if(globalNameSpace==null){let ns;if(typeof window!="undefined")ns=window;else if(typeof global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){let ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){let globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{let singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}var Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config]=value,[backend2]=key.split("_");backend2===backendName&&result.push(config)}return result}function registerKernel(config){let{kernelName,backendName}=config,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config)}function registerGradient(config){let{kernelName}=config;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now(){return env().platform.now()}function fetch2(path,requestInits){return env().platform.fetch(path,requestInits)}function encodeString(s,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.encode(s,encoding)}function decodeString(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.decode(bytes,encoding)}var Profiler=class{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad(name,25),rank=result.rank,size=result.size,shape=rightPad(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape.length;i++){let node2=tape[i],nodeInputs=node2.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node2.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node2.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node2=tape[i],nodeInputs=node2.inputs;for(let j=0;j<node2.outputs.length;j++)if(tensorsLeadToY[node2.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node2.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape.length;i++){let node2=tape[i];if(nodesFromX[node2.id]&&nodesToY[node2.id]){let prunedInputs={};for(let inputName in node2.inputs){let nodeInput=node2.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node2);prunedNode.inputs=prunedInputs,prunedNode.outputs=node2.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients(tensorAccumulatedGradientMap,filteredTape,tidy2,add5){for(let i=filteredTape.length-1;i>=0;i--){let node2=filteredTape[i],dys=[];if(node2.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node2.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);let inputGradients=node2.gradient(dys);for(let inputName in node2.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node2.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
 `)),lines.join(`
 `)}function computeMaxSizePerColumn(vals,shape,dtype,strides){let n=sizeFromShape(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples(vals):vals;if(rank>1)for(let row=0;row<n/numCols;row++){let offset=row*numCols;for(let j=0;j<numCols;j++)padPerCol[j]=Math.max(padPerCol[j],valToString(valuesOrTuples[offset+j],0,dtype).length)}return padPerCol}function valToString(val,pad3,dtype){let valStr;return Array.isArray(val)?valStr=`${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`:isString(val)?valStr=`'${val}'`:dtype==="bool"?valStr=boolNumToString(val):valStr=parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),rightPad(valStr,pad3)}function boolNumToString(v){return v===0?"false":"true"}function subTensorToString(vals,shape,dtype,strides,padPerCol,isLast=!0){let storagePerElement=dtype==="complex64"?2:1,size=shape[0],rank=shape.length;if(rank===0){if(dtype==="complex64"){let complexTuple=createComplexTuples(vals);return[valToString(complexTuple[0],0,dtype)]}return dtype==="bool"?[boolNumToString(vals[0])]:[vals[0].toString()]}if(rank===1){if(size>FORMAT_LIMIT_NUM_VALS){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples(firstVals),lastVals=createComplexTuples(lastVals)),["["+firstVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS){for(let i=0;i<FORMAT_NUM_FIRST_LAST_VALS;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,!1))}lines.push("...");for(let i=size-FORMAT_NUM_FIRST_LAST_VALS;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}}else for(let i=0;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}let sep=rank===2?",":"";lines[0]="["+lines[0]+sep;for(let i=1;i<lines.length-1;i++)lines[i]=" "+lines[i]+sep;let newLineSep=`,
 `;for(let i=2;i<rank;i++)newLineSep+=`
-`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape(shape),values!=null){let n=values.length;assert(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn().makeTensor(this.values,this.shape,this.dtype)}},trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(fn){trackerFn=fn}function setOpHandler(handler){opHandler=handler}function setDeprecationWarningFn(fn){deprecationWarningFn=fn}var Tensor=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape(shape),this.strides=computeStrides(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray(this.shape,vals)}arraySync(){return toNestedArray(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export2(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor2,tensorList){return tensorList.some(x=>x.id===tensor2.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container,list,seen){if(container!=null){if(container instanceof Tensor){list.push(container);return}if(isIterable(container)){let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(k=>k.name)))}}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance==null){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i];if(await this.initializeBackend(backendName).success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName);if(!(asyncInit?await success:success))return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler(this.backendInstance),!0}setupRegisteredKernels(){getKernelsForBackend(this.backendName).forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){getKernelsForBackend(backendName).forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend2=registryFactoryEntry.factory();if(backend2&&!(backend2 instanceof KernelBackend)&&typeof backend2.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend2.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend2,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend2,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend2,backend2.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend2=>backend2.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3,kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(outInfo=>{if(outInfo.rank!=null)return outInfo;let{dataId,shape,dtype}=outInfo;return this.makeTensorFromDataId(dataId,shape,dtype)});if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{if(forwardFunc==null)throw new Error(`Error running ${kernelName}: Neither modular kernel nor forward func passed`);let saveFunc=tensors=>{!isTapeOn||(saved=tensors.map(tensor2=>this.keep(this.clone(tensor2))))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){return tensors.map(tensor2=>this.keep(this.clone(tensor2)))}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend2){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend2=backend2||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend2.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend2),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend2){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend2),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend2){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend2||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(this.state.tensorInfo.has(a.dataId)){this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId);info.refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor2=this.state.activeScope.track[i];!tensor2.kept&&!tensorsToTrackInParentSet.has(tensor2.id)&&tensor2.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor2=>{!tensor2.kept&&tensor2.scopeId===oldScope.id&&this.track(tensor2)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node2=>{for(let tensor2 of node2.saved)tensor2.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){return this.state.tensorInfo.get(dataId).backend.readSync(dataId)}read(dataId){return this.state.tensorInfo.get(dataId).backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment2=new Environment(ns);ns._tfengine=new Engine(environment2)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernel(Add,inputs)}var device_util_exports={};__export2(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s 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is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency(val[i],subShape,indices.concat(i))}function assertDtype(expectedDtype,actualDType,argName,functionName){if(expectedDtype!=="string_or_numeric"){if(expectedDtype==null)throw new Error("Expected dtype cannot be null.");if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}}function convertToTensor(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor)return assertDtype(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],!0);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);return arg.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName,parseAsDtype))}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real4,imag4){let $real=convertToTensor(real4,"real","complex"),$imag=convertToTensor(imag4,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let inputs={real:$real,imag:$imag};return ENGINE.runKernel(Complex,inputs)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape(shape.slice(i)):!0;assert(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor2=>tensor2.name):Object.keys(tensors);for(let i=0;i<names.length;++i){let name=names[i],t=Array.isArray(tensors)?tensors[i].tensor:tensors[name];if(t.dtype!=="float32"&&t.dtype!=="int32"&&t.dtype!=="bool"&&t.dtype!=="string"&&t.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);let spec={name,shape:t.shape,dtype:t.dtype};if(t.dtype==="string"){let utf8bytes=new Promise(async resolve=>{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2<vals.length;i2++){let val=vals[i2],bytesOfLength=new Uint8Array(new Uint32Array([val.length]).buffer);bytes.set(bytesOfLength,offset),offset+=NUM_BYTES_STRING_LENGTH,bytes.set(val,offset),offset+=val.length}resolve(bytes)});dataPromises.push(utf8bytes)}else dataPromises.push(t.data());group!=null&&(spec.group=group),specs.push(spec)}let tensorValues=await Promise.all(dataPromises);return{data:concatenateTypedArrays(tensorValues),specs}}function decodeWeights(buffer2,specs){let out={},float16Decode,offset=0;for(let spec of specs){let name=spec.name,dtype=spec.dtype,shape=spec.shape,size=sizeFromShape(shape),values;if("quantization"in spec){let quantization=spec.quantization;if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){if(!("min"in quantization&&"scale"in quantization))throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`)}else if(quantization.dtype==="float16"){if(dtype!=="float32")throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`)}else throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let quantizationSizeFactor=DTYPE_VALUE_SIZE_MAP[quantization.dtype],byteBuffer=buffer2.slice(offset,offset+size*quantizationSizeFactor),quantizedArray=quantization.dtype==="uint8"?new Uint8Array(byteBuffer):new Uint16Array(byteBuffer);if(dtype==="float32")if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){values=new Float32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=v*quantization.scale+quantization.min}}else if(quantization.dtype==="float16")float16Decode===void 0&&(float16Decode=getFloat16Decoder()),values=float16Decode(quantizedArray);else throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);else if(dtype==="int32"){if(quantization.dtype!=="uint8"&&quantization.dtype!=="uint16")throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);values=new Int32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=Math.round(v*quantization.scale+quantization.min)}}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*quantizationSizeFactor}else if(dtype==="string"){let size2=sizeFromShape(spec.shape);values=[];for(let i=0;i<size2;i++){let byteLength=new Uint32Array(buffer2.slice(offset,offset+NUM_BYTES_STRING_LENGTH))[0];offset+=NUM_BYTES_STRING_LENGTH;let bytes=new Uint8Array(buffer2.slice(offset,offset+byteLength));values.push(bytes),offset+=byteLength}}else{let dtypeFactor=DTYPE_VALUE_SIZE_MAP[dtype],byteBuffer=buffer2.slice(offset,offset+size*dtypeFactor);if(dtype==="float32")values=new Float32Array(byteBuffer);else if(dtype==="int32")values=new Int32Array(byteBuffer);else if(dtype==="bool")values=new Uint8Array(byteBuffer);else if(dtype==="complex64"){values=new Float32Array(byteBuffer);let real4=new Float32Array(values.length/2),image3=new Float32Array(values.length/2);for(let i=0;i<real4.length;i++)real4[i]=values[i*2],image3[i]=values[i*2+1];let realTensor=tensor(real4,shape,"float32"),imageTensor=tensor(image3,shape,"float32");out[name]=complex(realTensor,imageTensor),realTensor.dispose(),imageTensor.dispose()}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*dtypeFactor}dtype!=="complex64"&&(out[name]=tensor(values,shape,dtype))}return out}function concatenateTypedArrays(xs){if(xs===null)throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);let totalByteLength=0,normalizedXs=[];xs.forEach(x=>{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer2){if(useNodeBuffer)return Buffer.from(buffer2).toString("base64");let buf=new Uint8Array(buffer2),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer(str){if(useNodeBuffer){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer2=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer2.set([s.charCodeAt(i)],i);return buffer2.buffer}function concatenateArrayBuffers(buffers){if(buffers.length===1)return buffers[0];let totalByteLength=0;buffers.forEach(buffer2=>{totalByteLength+=buffer2.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer2=>{temp.set(new Uint8Array(buffer2),offset),offset+=buffer2.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)===0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer2=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer2);for(let index=0;index<quantizedArray.length;index++){let float16Bits=quantizedArray[index],float32Bits=mantisaTable[offsetTable[float16Bits>>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer2)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[];return(handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters).forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions),DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),getRequest=modelTx.objectStore(MODEL_STORE_NAME).get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let putModelRequest=modelTx.objectStore(MODEL_STORE_NAME).put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),getAllInfoRequest=tx.objectStore(INFO_STORE_NAME).getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let deleteModelRequest=modelTx.objectStore(MODEL_STORE_NAME).delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}},PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData));let result={format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy};return modelArtifacts.signature!=null&&(result.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(result.modelInitializer=modelArtifacts.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(result)),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,metadata.signature!=null&&(out.signature=metadata.signature),metadata.userDefinedMetadata!=null&&(out.userDefinedMetadata=metadata.userDefinedMetadata),metadata.modelInitializer!=null&&(out.modelInitializer=metadata.modelInitializer)}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme2(path);let keys=getModelKeys(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}},URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry.instance==null&&(ModelStoreManagerRegistry.instance=new ModelStoreManagerRegistry),ModelStoreManagerRegistry.instance}static registerManager(scheme,manager){assert(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url);return ModelStoreManagerRegistry.getManager(schemeAndPath.scheme).removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!1)}async function moveModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!0)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}now(){return performance.now()}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernel(Cast,inputs,attrs)}var cast=op({cast_});function clone_(x){let inputs={x:convertToTensor(x,"x","clone","string_or_numeric")};return ENGINE.runKernel(Identity,inputs)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export2(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer);let modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);if(perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1){let result={modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy};modelJSON.signature!=null&&(result.signature=modelJSON.signature),modelJSON.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelJSON.userDefinedMetadata),modelJSON.modelInitializer!=null&&(result.modelInitializer=modelJSON.modelInitializer),resolve(result)}},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,bufferPromises=(loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction)).map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1;return loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction)}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){return weightsLoaderFactory(fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}))(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
-Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2<numBuffers;i2++)groupBytes+=buffers[bufferIndexOffset+i2].byteLength;let groupBuffer=new ArrayBuffer(groupBytes),groupByteBuffer=new Uint8Array(groupBuffer),groupBufferOffset=0;for(let i2=0;i2<numBuffers;i2++){let buffer2=new Uint8Array(buffers[bufferIndexOffset+i2]);groupByteBuffer.set(buffer2,groupBufferOffset),groupBufferOffset+=buffer2.byteLength}groupWeightsToFetch[i].forEach(weightsEntry=>{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}return tensor3d(values,[height,width,numChannels],"int32")}async function toPixels(img,canvas){let $img=convertToTensor(img,"img","toPixels");if(!(img instanceof Tensor)){let originalImgTensor=$img;$img=cast(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;i<indicesShape.length-1;++i)nResult*=indicesShape[i];let inputShape=tensor2.shape,resultShape=indicesShape.slice();resultShape.pop();let sliceSize=1;for(let i=sliceRank;i<tensorRank;++i)sliceSize*=inputShape[i],resultShape.push(inputShape[i]);let strides=[...computeStrides(tensor2.shape).map(stride=>stride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank<batchDim)throw new Error(shapeError+` update.rank < ${batchDim}. `);if(shape.length<sliceDim+(updates.rank-batchDim))throw new Error(shapeError+` Output shape length < ${sliceDim+(updates.rank-batchDim)}`);if(updates.rank!==batchDim+shape.length-sliceDim)throw new Error(shapeError+` update.rank != ${batchDim+shape.length-sliceDim}`);for(let d=0;d<batchDim;++d)if(updates.shape[d]!==indices.shape[d])throw new Error(shapeError+` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);for(let d=0;d<updates.rank-batchDim;++d)if(updates.shape[d+batchDim]!==shape[d+sliceDim])throw new Error(shapeError+` updates.shape[${d+batchDim}] (${updates.shape[d+batchDim]}) != shape[${d+batchDim}] (${shape[d+batchDim]})`)}function validateInput(updates,indices,shape){if(indices.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(updates.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);if(indices.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);if(shape.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);if(shape.length===0){if(indices.size===0)throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);if(updates.size===0)throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`)}validateUpdateShape(shape,indices,updates)}function calculateShapes(updates,indices,shape){let indicesRank=indices.shape.length,sliceRank=indicesRank>1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;i<totalNd;++i)sliceSize*=shape[i];let safeSliceDim=sliceRank<1?1:sliceRank,numUpdates=sizeFromShape(indices.shape)/safeSliceDim,strides=[...computeStrides(shape.slice(0,sliceRank)),1],outputSize=sizeFromShape(shape);return{sliceRank,numUpdates,sliceSize,strides,outputSize}}var slice_util_exports={};__export2(slice_util_exports,{assertParamsValid:()=>assertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i<size.length;i++)if(size[i]>1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i<size.length;i++)if(begin[i]>0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i<begin.length-1;i++)flatOffset+=begin[i]*strides[i];return flatOffset}function parseSliceParams(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config){return new cls(config)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon32){return epsilon32==null&&(epsilon32=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon32))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
+`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape(shape),values!=null){let n=values.length;assert(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn().makeTensor(this.values,this.shape,this.dtype)}},trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(fn){trackerFn=fn}function setOpHandler(handler){opHandler=handler}function setDeprecationWarningFn(fn){deprecationWarningFn=fn}var Tensor=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape(shape),this.strides=computeStrides(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray(this.shape,vals)}arraySync(){return toNestedArray(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export2(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor2,tensorList){return tensorList.some(x=>x.id===tensor2.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container,list,seen){if(container==null)return;if(container instanceof Tensor){list.push(container);return}if(!isIterable(container))return;let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(k=>k.name)))}}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i];if(await this.initializeBackend(backendName).success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName);if(!(asyncInit?await success:success))return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler(this.backendInstance),!0}setupRegisteredKernels(){getKernelsForBackend(this.backendName).forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){getKernelsForBackend(backendName).forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend2=registryFactoryEntry.factory();if(backend2&&!(backend2 instanceof KernelBackend)&&typeof backend2.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend2.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend2,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend2,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend2,backend2.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend2=>backend2.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3;this.backendName==null&&this.backend;let kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(outInfo=>{if(outInfo.rank!=null)return outInfo;let{dataId,shape,dtype}=outInfo;return this.makeTensorFromDataId(dataId,shape,dtype)});if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{if(forwardFunc==null)throw new Error(`Error running ${kernelName}: Neither modular kernel nor forward func passed`);let saveFunc=tensors=>{!isTapeOn||(saved=tensors.map(tensor2=>this.keep(this.clone(tensor2))))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){return tensors.map(tensor2=>this.keep(this.clone(tensor2)))}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend2){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend2=backend2||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend2.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend2),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend2){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend2),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend2){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend2||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId);info.refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor2=this.state.activeScope.track[i];!tensor2.kept&&!tensorsToTrackInParentSet.has(tensor2.id)&&tensor2.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor2=>{!tensor2.kept&&tensor2.scopeId===oldScope.id&&this.track(tensor2)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node2=>{for(let tensor2 of node2.saved)tensor2.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){return this.state.tensorInfo.get(dataId).backend.readSync(dataId)}read(dataId){return this.state.tensorInfo.get(dataId).backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment2=new Environment(ns);ns._tfengine=new Engine(environment2)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernel(Add,inputs)}var device_util_exports={};__export2(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s 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)|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0,4))}return!1}function isBrowser(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var ENV2=env();ENV2.registerFlag("DEBUG",()=>!1,debugValue=>{debugValue&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency(val[i],subShape,indices.concat(i))}function assertDtype(expectedDtype,actualDType,argName,functionName){if(expectedDtype!=="string_or_numeric"){if(expectedDtype==null)throw new Error("Expected dtype cannot be null.");if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}}function convertToTensor(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor)return assertDtype(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],!0);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);return arg.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName,parseAsDtype))}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real4,imag4){let $real=convertToTensor(real4,"real","complex"),$imag=convertToTensor(imag4,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let inputs={real:$real,imag:$imag};return ENGINE.runKernel(Complex,inputs)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape(shape.slice(i)):!0;assert(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor2=>tensor2.name):Object.keys(tensors);for(let i=0;i<names.length;++i){let name=names[i],t=Array.isArray(tensors)?tensors[i].tensor:tensors[name];if(t.dtype!=="float32"&&t.dtype!=="int32"&&t.dtype!=="bool"&&t.dtype!=="string"&&t.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);let spec={name,shape:t.shape,dtype:t.dtype};if(t.dtype==="string"){let utf8bytes=new Promise(async resolve=>{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2<vals.length;i2++){let val=vals[i2],bytesOfLength=new Uint8Array(new Uint32Array([val.length]).buffer);bytes.set(bytesOfLength,offset),offset+=NUM_BYTES_STRING_LENGTH,bytes.set(val,offset),offset+=val.length}resolve(bytes)});dataPromises.push(utf8bytes)}else dataPromises.push(t.data());group!=null&&(spec.group=group),specs.push(spec)}let tensorValues=await Promise.all(dataPromises);return{data:concatenateTypedArrays(tensorValues),specs}}function decodeWeights(buffer2,specs){let out={},float16Decode,offset=0;for(let spec of specs){let name=spec.name,dtype=spec.dtype,shape=spec.shape,size=sizeFromShape(shape),values;if("quantization"in spec){let quantization=spec.quantization;if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){if(!("min"in quantization&&"scale"in quantization))throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`)}else if(quantization.dtype==="float16"){if(dtype!=="float32")throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`)}else throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let quantizationSizeFactor=DTYPE_VALUE_SIZE_MAP[quantization.dtype],byteBuffer=buffer2.slice(offset,offset+size*quantizationSizeFactor),quantizedArray=quantization.dtype==="uint8"?new Uint8Array(byteBuffer):new Uint16Array(byteBuffer);if(dtype==="float32")if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){values=new Float32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=v*quantization.scale+quantization.min}}else if(quantization.dtype==="float16")float16Decode===void 0&&(float16Decode=getFloat16Decoder()),values=float16Decode(quantizedArray);else throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);else if(dtype==="int32"){if(quantization.dtype!=="uint8"&&quantization.dtype!=="uint16")throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);values=new Int32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=Math.round(v*quantization.scale+quantization.min)}}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*quantizationSizeFactor}else if(dtype==="string"){let size2=sizeFromShape(spec.shape);values=[];for(let i=0;i<size2;i++){let byteLength=new Uint32Array(buffer2.slice(offset,offset+NUM_BYTES_STRING_LENGTH))[0];offset+=NUM_BYTES_STRING_LENGTH;let bytes=new Uint8Array(buffer2.slice(offset,offset+byteLength));values.push(bytes),offset+=byteLength}}else{let dtypeFactor=DTYPE_VALUE_SIZE_MAP[dtype],byteBuffer=buffer2.slice(offset,offset+size*dtypeFactor);if(dtype==="float32")values=new Float32Array(byteBuffer);else if(dtype==="int32")values=new Int32Array(byteBuffer);else if(dtype==="bool")values=new Uint8Array(byteBuffer);else if(dtype==="complex64"){values=new Float32Array(byteBuffer);let real4=new Float32Array(values.length/2),image3=new Float32Array(values.length/2);for(let i=0;i<real4.length;i++)real4[i]=values[i*2],image3[i]=values[i*2+1];let realTensor=tensor(real4,shape,"float32"),imageTensor=tensor(image3,shape,"float32");out[name]=complex(realTensor,imageTensor),realTensor.dispose(),imageTensor.dispose()}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*dtypeFactor}dtype!=="complex64"&&(out[name]=tensor(values,shape,dtype))}return out}function concatenateTypedArrays(xs){if(xs===null)throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);let totalByteLength=0,normalizedXs=[];xs.forEach(x=>{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer2){if(useNodeBuffer)return Buffer.from(buffer2).toString("base64");let buf=new Uint8Array(buffer2),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer(str){if(useNodeBuffer){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer2=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer2.set([s.charCodeAt(i)],i);return buffer2.buffer}function concatenateArrayBuffers(buffers){if(buffers.length===1)return buffers[0];let totalByteLength=0;buffers.forEach(buffer2=>{totalByteLength+=buffer2.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer2=>{temp.set(new Uint8Array(buffer2),offset),offset+=buffer2.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)==0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer2=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer2);for(let index=0;index<quantizedArray.length;index++){let float16Bits=quantizedArray[index],float32Bits=mantisaTable[offsetTable[float16Bits>>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer2)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[];return(handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters).forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions),DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),getRequest=modelTx.objectStore(MODEL_STORE_NAME).get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let putModelRequest=modelTx.objectStore(MODEL_STORE_NAME).put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),getAllInfoRequest=tx.objectStore(INFO_STORE_NAME).getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let deleteModelRequest=modelTx.objectStore(MODEL_STORE_NAME).delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}},PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData));let result={format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy};return modelArtifacts.signature!=null&&(result.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(result.modelInitializer=modelArtifacts.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(result)),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,metadata.signature!=null&&(out.signature=metadata.signature),metadata.userDefinedMetadata!=null&&(out.userDefinedMetadata=metadata.userDefinedMetadata),metadata.modelInitializer!=null&&(out.modelInitializer=metadata.modelInitializer)}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme2(path);let keys=getModelKeys(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}},URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry.instance==null&&(ModelStoreManagerRegistry.instance=new ModelStoreManagerRegistry),ModelStoreManagerRegistry.instance}static registerManager(scheme,manager){assert(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url);return ModelStoreManagerRegistry.getManager(schemeAndPath.scheme).removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!1)}async function moveModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!0)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}now(){return performance.now()}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernel(Cast,inputs,attrs)}var cast=op({cast_});function clone_(x){let inputs={x:convertToTensor(x,"x","clone","string_or_numeric")};return ENGINE.runKernel(Identity,inputs)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export2(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer);let modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);if(perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1){let result={modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy};modelJSON.signature!=null&&(result.signature=modelJSON.signature),modelJSON.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelJSON.userDefinedMetadata),modelJSON.modelInitializer!=null&&(result.modelInitializer=modelJSON.modelInitializer),resolve(result)}},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,bufferPromises=(loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction)).map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1;return loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction)}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){return weightsLoaderFactory(fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}))(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
+Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2<numBuffers;i2++)groupBytes+=buffers[bufferIndexOffset+i2].byteLength;let groupBuffer=new ArrayBuffer(groupBytes),groupByteBuffer=new Uint8Array(groupBuffer),groupBufferOffset=0;for(let i2=0;i2<numBuffers;i2++){let buffer2=new Uint8Array(buffers[bufferIndexOffset+i2]);groupByteBuffer.set(buffer2,groupBufferOffset),groupBufferOffset+=buffer2.byteLength}groupWeightsToFetch[i].forEach(weightsEntry=>{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1,isImageBitmap=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else if(typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap)isImageBitmap=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}return tensor3d(values,[height,width,numChannels],"int32")}async function toPixels(img,canvas){let $img=convertToTensor(img,"img","toPixels");if(!(img instanceof Tensor)){let originalImgTensor=$img;$img=cast(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;i<indicesShape.length-1;++i)nResult*=indicesShape[i];let inputShape=tensor2.shape,resultShape=indicesShape.slice();resultShape.pop();let sliceSize=1;for(let i=sliceRank;i<tensorRank;++i)sliceSize*=inputShape[i],resultShape.push(inputShape[i]);let strides=[...computeStrides(tensor2.shape).map(stride=>stride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank<batchDim)throw new Error(shapeError+` update.rank < ${batchDim}. `);if(shape.length<sliceDim+(updates.rank-batchDim))throw new Error(shapeError+` Output shape length < ${sliceDim+(updates.rank-batchDim)}`);if(updates.rank!==batchDim+shape.length-sliceDim)throw new Error(shapeError+` update.rank != ${batchDim+shape.length-sliceDim}`);for(let d=0;d<batchDim;++d)if(updates.shape[d]!==indices.shape[d])throw new Error(shapeError+` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);for(let d=0;d<updates.rank-batchDim;++d)if(updates.shape[d+batchDim]!==shape[d+sliceDim])throw new Error(shapeError+` updates.shape[${d+batchDim}] (${updates.shape[d+batchDim]}) != shape[${d+batchDim}] (${shape[d+batchDim]})`)}function validateInput(updates,indices,shape){if(indices.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(updates.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);if(indices.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);if(shape.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);if(shape.length===0){if(indices.size===0)throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);if(updates.size===0)throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`)}validateUpdateShape(shape,indices,updates)}function calculateShapes(updates,indices,shape){let indicesRank=indices.shape.length,sliceRank=indicesRank>1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;i<totalNd;++i)sliceSize*=shape[i];let safeSliceDim=sliceRank<1?1:sliceRank,numUpdates=sizeFromShape(indices.shape)/safeSliceDim,strides=[...computeStrides(shape.slice(0,sliceRank)),1],outputSize=sizeFromShape(shape);return{sliceRank,numUpdates,sliceSize,strides,outputSize}}var slice_util_exports={};__export2(slice_util_exports,{assertParamsValid:()=>assertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i<size.length;i++)if(size[i]>1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i<size.length;i++)if(begin[i]>0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i<begin.length-1;i++)flatOffset+=begin[i]*strides[i];return flatOffset}function parseSliceParams(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config){return new cls(config)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon32){return epsilon32==null&&(epsilon32=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon32))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
 Actual:   ${actualFlat}.
 Expected: ${expectedFlat}.`);for(let i=0;i<expectedFlat.length;++i){let a=actualFlat[i],e=expectedFlat[i];if(!predicate(a,e))throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
 Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`)}}function expectPromiseToFail(fn,done){fn().then(()=>done.fail(),()=>done())}function expectArraysEqual(actual,expected){let exp4=typeof expected=="string"||typeof expected=="number"||typeof expected=="boolean"?[expected]:expected;return isString(actual)||isString(actual[0])||isString(expected)||isString(expected[0])?expectArraysPredicate(actual,exp4,(a,b)=>a==b):expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,0))}function expectNumbersClose(a,e,epsilon32){if(epsilon32==null&&(epsilon32=testEpsilon()),!areClose(a,e,epsilon32))throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`)}function areClose(a,e,epsilon32){return!isFinite(a)&&!isFinite(e)?!0:!(isNaN(a)||isNaN(e)||Math.abs(a-e)>epsilon32)}function expectValuesInRange(actual,low,high){for(let i=0;i<actual.length;i++)if(actual[i]<low||actual[i]>high)throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`)}function expectArrayBuffersEqual(actual,expected){expect(new Float32Array(actual)).toEqual(new Float32Array(expected))}function encodeStrings(a){for(let i=0;i<a.length;i++){let val=a[i];Array.isArray(val)?encodeStrings(val):a[i]=encodeString(val)}return a}var version="2.8.2";function enableProdMode(){env().set("PROD",!0)}function enableDebugMode(){env().set("DEBUG",!0)}function disableDeprecationWarnings(){env().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function deprecationWarn(msg){env().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn(deprecationWarn);function disposeVariables(){ENGINE.disposeVariables()}function engine(){return ENGINE}function memory(){return ENGINE.memory()}function profile(f){return ENGINE.profile(f)}function tidy(nameOrFn,fn){return ENGINE.tidy(nameOrFn,fn)}function dispose(container){getTensorsInContainer(container).forEach(tensor2=>tensor2.dispose())}function keep(result){return ENGINE.keep(result)}function time(f){return ENGINE.time(f)}function setBackend(backendName){return ENGINE.setBackend(backendName)}function ready(){return ENGINE.ready()}function getBackend(){return ENGINE.backendName}function removeBackend(name){ENGINE.removeBackend(name)}function findBackend(name){return ENGINE.findBackend(name)}function findBackendFactory(name){return ENGINE.findBackendFactory(name)}function registerBackend(name,factory,priority=1){return ENGINE.registerBackend(name,factory,priority)}function backend(){return ENGINE.backend}function setPlatform(platformName,platform){env().setPlatform(platformName,platform)}function add_(a,b){let $a=convertToTensor(a,"a","add"),$b=convertToTensor(b,"b","add");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Add,inputs)}var add2=op({add_});function floorDiv_(a,b){let $a=convertToTensor(a,"a","floorDiv"),$b=convertToTensor(b,"b","floorDiv");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(FloorDiv,inputs)}var floorDiv=op({floorDiv_});function div_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");if([$a,$b]=makeTypesMatch($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv($a,$b);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(RealDiv,inputs,attrs)}var div=op({div_});function mul_(a,b){let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Multiply,inputs)}var mul=op({mul_});function abs_(x){let $x=convertToTensor(x,"x","abs");if($x.dtype==="complex64"){let inputs={x:$x};return ENGINE.runKernel(ComplexAbs,inputs)}else{let inputs={x:$x};return ENGINE.runKernel(Abs,inputs)}}var abs=op({abs_});function acos_(x){let inputs={x:convertToTensor(x,"x","acos")};return ENGINE.runKernel(Acos,inputs)}var acos=op({acos_});function acosh_(x){let inputs={x:convertToTensor(x,"x","acosh")};return ENGINE.runKernel(Acosh,inputs)}var acosh=op({acosh_});function addN_(tensors){assert(Array.isArray(tensors),()=>"The argument passed to tf.addN() must be a list of tensors"),assert(tensors.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);let $tensors=tensors.map((t,i)=>convertToTensor(t,`tensors${i}`,"addN")),firstTensor=$tensors[0];$tensors.forEach(t=>{if(t.dtype!==firstTensor.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),$tensors.forEach(t=>{if(!arraysEqual(t.shape,firstTensor.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let inputs=$tensors;return ENGINE.runKernel(AddN,inputs)}var addN=op({addN_});function all_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","all","bool")},attrs={axis,keepDims};return ENGINE.runKernel(All,inputs,attrs)}var all=op({all_});function any_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","any","bool")},attrs={axis,keepDims};return ENGINE.runKernel(Any,inputs,attrs)}var any=op({any_});function argMax_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMax")},attrs={axis};return ENGINE.runKernel(ArgMax,inputs,attrs)}var argMax=op({argMax_});function argMin_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMin")},attrs={axis};return ENGINE.runKernel(ArgMin,inputs,attrs)}var argMin=op({argMin_});function asin_(x){let inputs={x:convertToTensor(x,"x","asin")};return ENGINE.runKernel(Asin,inputs)}var asin=op({asin_});function asinh_(x){let inputs={x:convertToTensor(x,"x","asinh")};return ENGINE.runKernel(Asinh,inputs)}var asinh=op({asinh_});function atan_(x){let inputs={x:convertToTensor(x,"x","atan")};return ENGINE.runKernel(Atan,inputs)}var atan=op({atan_});function atan2_(a,b){let $a=convertToTensor(a,"a","atan2"),$b=convertToTensor(b,"b","atan2");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Atan2,inputs)}var atan2=op({atan2_});function atanh_(x){let inputs={x:convertToTensor(x,"x","atanh")};return ENGINE.runKernel(Atanh,inputs)}var atanh=op({atanh_});function computeDilation2DInfo(inputShape,filterShape,strides,pad3,dataFormat="NHWC",dilations){let inputChannels=inputShape[3],$filterShape=[...filterShape,inputChannels],$dataFormat=convertConv2DDataFormat(dataFormat);return computeConv2DInfo(inputShape,$filterShape,strides,dilations,pad3,null,null,$dataFormat)}function computePool2DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,!1,dataFormat)}function computePool3DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,!1,$dataFormat,roundingMode)}function computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam(strides),[dilationHeight,dilationWidth]=parseTupleParam(dilations),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam(dilations),effectiveFilterDepth=getEffectiveFilterSize(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputRows,outputCols]}function computeOutputShape4D(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=round((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode),outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape2D([inHeight,inWidth],filterHeight,strideHeight,pad3,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad3==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad3=="object"){let top=dataFormat==="channelsLast"?pad3[1][0]:pad3[2][0],bottom=dataFormat==="channelsLast"?pad3[1][1]:pad3[2][1],left=dataFormat==="channelsLast"?pad3[2][0]:pad3[3][0],right=dataFormat==="channelsLast"?pad3[2][1]:pad3[3][1];padInfo={top,bottom,left,right,type:top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT"},outHeight=round((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=round((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,front:pad3,back:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape4D([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad3,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad3==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outDepth,outHeight,outWidth}}function round(value,roundingMode){if(!roundingMode)return Math.trunc(value);switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne(param){let[dimA,dimB,dimC]=parseTupleParam(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne(strides,dilations){return tupleValuesAreOne(strides)||tupleValuesAreOne(dilations)}function convertConv2DDataFormat(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function reshape_(x,shape){let inputs={x:convertToTensor(x,"x","reshape","string_or_numeric")},attrs={shape};return ENGINE.runKernel(Reshape,inputs,attrs)}var reshape=op({reshape_});function avgPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","avgPool","float32"),dilations=1;assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool,inputs,attrs);return res=cast(res,$x.dtype),reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool=op({avgPool_});function avgPool3d_(x,filterSize,strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","avgPool3d","float32"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(AvgPool3D,inputs,attrs);return res=cast(res,x5D.dtype),reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3d=op({avgPool3d_});function concat_(tensors,axis=0){assert(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray(tensors,"tensors","concat","string_or_numeric");if($tensors[0].dtype==="complex64"&&$tensors.forEach(tensor2=>{if(tensor2.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
-          with dtype ${tensor2.dtype}. `)}),$tensors.length===1)return clone($tensors[0]);let inputs=$tensors,attr={axis};return ENGINE.runKernel(Concat,inputs,attr)}var concat=op({concat_});function sigmoid_(x){let inputs={x:convertToTensor(x,"x","sigmoid")};return ENGINE.runKernel(Sigmoid,inputs)}var sigmoid=op({sigmoid_});function slice_(x,begin,size){let $x=convertToTensor(x,"x","slice","string_or_numeric");if($x.rank===0)throw new Error("Slicing scalar is not possible");let inputs={x:$x},attrs={begin,size};return ENGINE.runKernel(Slice,inputs,attrs)}var slice=op({slice_});function tanh_(x){let inputs={x:convertToTensor(x,"x","tanh")};return ENGINE.runKernel(Tanh,inputs)}var tanh2=op({tanh_});function basicLSTMCell_(forgetBias,lstmKernel,lstmBias,data,c,h){let $forgetBias=convertToTensor(forgetBias,"forgetBias","basicLSTMCell"),$lstmKernel=convertToTensor(lstmKernel,"lstmKernel","basicLSTMCell"),$lstmBias=convertToTensor(lstmBias,"lstmBias","basicLSTMCell"),$data=convertToTensor(data,"data","basicLSTMCell"),$c=convertToTensor(c,"c","basicLSTMCell"),$h=convertToTensor(h,"h","basicLSTMCell"),combined=concat([$data,$h],1),weighted=matMul(combined,$lstmKernel),res=add2(weighted,$lstmBias),batchSize=res.shape[0],sliceCols=res.shape[1]/4,sliceSize=[batchSize,sliceCols],i=slice(res,[0,0],sliceSize),j=slice(res,[0,sliceCols],sliceSize),f=slice(res,[0,sliceCols*2],sliceSize),o=slice(res,[0,sliceCols*3],sliceSize),newC=add2(mul(sigmoid(i),tanh2(j)),mul($c,sigmoid(add2($forgetBias,f)))),newH=mul(tanh2(newC),sigmoid(o));return[newC,newH]}var basicLSTMCell=op({basicLSTMCell_});function batchToSpaceND_(x,blockShape,crops){let $x=convertToTensor(x,"x","batchToSpaceND"),prod5=blockShape.reduce((a,b)=>a*b);assert($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert($x.shape[0]%prod5===0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`);let inputs={x:$x},attrs={blockShape,crops};return ENGINE.runKernel(BatchToSpaceND,inputs,attrs)}var batchToSpaceND=op({batchToSpaceND_});function xAs4D(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape(x,[1,1,1,x.size]):x.rank===2?x4D=reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_(x,mean4,variance,offset,scale22,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let inputs={x:xAs4D($x),scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE.runKernel(FusedBatchNorm,inputs,attrs);return reshape(res,$x.shape)}var batchNorm=op({batchNorm_});function batchNorm2d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`),assert($mean.rank===2||$mean.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===2||$variance.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===2||$scale.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===2||$offset.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm2d=op({batchNorm2d_});function batchNorm3d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`),assert($mean.rank===3||$mean.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===3||$variance.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===3||$scale.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===3||$offset.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm3d=op({batchNorm3d_});function batchNorm4d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`),assert($mean.rank===4||$mean.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===4||$variance.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===4||$scale.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===4||$offset.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm4d=op({batchNorm4d_});function bincount_(x,weights,size){let $x=convertToTensor(x,"x","bincount"),$weights=convertToTensor(weights,"weights","bincount");assert($x.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${$x.dtype}`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size};return ENGINE.runKernel(Bincount,inputs,attrs)}var bincount=op({bincount_});function broadcastTo_(x,shape){let input2=convertToTensor(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);if(reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0).length===0)return clone(input2);let inputs={x:input2},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var broadcastTo=op({broadcastTo_});function ceil_(x){let inputs={x:convertToTensor(x,"x","ceil")};return ENGINE.runKernel(Ceil,inputs)}var ceil=op({ceil_});function clipByValue_(x,clipValueMin,clipValueMax){let $x=convertToTensor(x,"x","clipByValue");assert(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE.runKernel(ClipByValue,inputs,attrs)}var clipByValue=op({clipByValue_});function concat1d_(tensors){return concat(tensors,0)}var concat1d=op({concat1d_});function concat2d_(tensors,axis){return concat(tensors,axis)}var concat2d=op({concat2d_});function concat3d_(tensors,axis){return concat(tensors,axis)}var concat3d=op({concat3d_});function concat4d_(tensors,axis){return concat(tensors,axis)}var concat4d=op({concat4d_});function conv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(Conv2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d=op({conv2d_});function conv1d_(x,filter,stride,pad3,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor(x,"x","conv1d"),$filter=convertToTensor(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape($x,[1,$x.shape[0],$x.shape[1]])),assert(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert(eitherStridesOrDilationsAreOne(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),res=conv2d(input4D,filter4D,[1,stride],pad3,"NHWC",[1,dilation],dimRoundingMode);return reshapedTo3D?reshape(res,[res.shape[2],res.shape[3]]):reshape(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d=op({conv1d_});function conv2DBackpropInput_(xShape,dy,filter,strides,pad3,dataFormat="NHWC",dimRoundingMode){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE.runKernel(Conv2DBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput=op({conv2DBackpropInput_});function conv2dTranspose_(x,filter,outputShape,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","conv2dTranspose"),$filter=convertToTensor(filter,"filter","conv2dTranspose");return conv2DBackpropInput(outputShape,$x,$filter,strides,pad3,"NHWC",dimRoundingMode)}var conv2dTranspose=op({conv2dTranspose_});function conv3d_(x,filter,strides,pad3,dataFormat="NDHWC",dilations=[1,1,1]){let $x=convertToTensor(x,"x","conv3d"),$filter=convertToTensor(filter,"filter","conv3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`),assert($filter.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`),assert(x5D.shape[4]===$filter.shape[3],()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NDHWC",()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);let inputs={x:x5D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations},res=ENGINE.runKernel(Conv3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3d=op({conv3d_});function conv3DBackpropInput_(xShape,dy,filter,strides,pad3){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let inputs={dy:dy5D,filter},attrs={pad:pad3,strides,inputShape:xShape5D},res=ENGINE.runKernel(Conv3DBackpropInputV2,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput=op({conv3DBackpropInput_});function conv3dTranspose_(x,filter,outputShape,strides,pad3){let $x=convertToTensor(x,"x","conv3dTranspose"),$filter=convertToTensor(filter,"filter","conv3dTranspose");return conv3DBackpropInput(outputShape,$x,$filter,strides,pad3)}var conv3dTranspose=op({conv3dTranspose_});function cos_(x){let inputs={x:convertToTensor(x,"x","cos")};return ENGINE.runKernel(Cos,inputs)}var cos=op({cos_});function cosh_(x){let inputs={x:convertToTensor(x,"x","cosh")};return ENGINE.runKernel(Cosh,inputs)}var cosh=op({cosh_});function cumsum_(x,axis=0,exclusive=!1,reverse5=!1){let inputs={x:convertToTensor(x,"x","cumsum")},attrs={axis,exclusive,reverse:reverse5};return ENGINE.runKernel(Cumsum,inputs,attrs)}var cumsum=op({cumsum_});function denseBincount_(x,weights,size,binaryOutput=!1){let $x=convertToTensor(x,"x","denseBincount"),$weights=convertToTensor(weights,"weights","denseBincount");assert($x.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`),assert($x.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size,binaryOutput};return ENGINE.runKernel(DenseBincount,inputs,attrs)}var denseBincount=op({denseBincount_});function depthToSpace_(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+Expected: ${expectedFlat}.`)}}function expectPromiseToFail(fn,done){fn().then(()=>done.fail(),()=>done())}function expectArraysEqual(actual,expected){let exp4=typeof expected=="string"||typeof expected=="number"||typeof expected=="boolean"?[expected]:expected;return isString(actual)||isString(actual[0])||isString(expected)||isString(expected[0])?expectArraysPredicate(actual,exp4,(a,b)=>a==b):expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,0))}function expectNumbersClose(a,e,epsilon32){if(epsilon32==null&&(epsilon32=testEpsilon()),!areClose(a,e,epsilon32))throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`)}function areClose(a,e,epsilon32){return!isFinite(a)&&!isFinite(e)?!0:!(isNaN(a)||isNaN(e)||Math.abs(a-e)>epsilon32)}function expectValuesInRange(actual,low,high){for(let i=0;i<actual.length;i++)if(actual[i]<low||actual[i]>high)throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`)}function expectArrayBuffersEqual(actual,expected){expect(new Float32Array(actual)).toEqual(new Float32Array(expected))}function encodeStrings(a){for(let i=0;i<a.length;i++){let val=a[i];Array.isArray(val)?encodeStrings(val):a[i]=encodeString(val)}return a}var version="2.8.3";function enableProdMode(){env().set("PROD",!0)}function enableDebugMode(){env().set("DEBUG",!0)}function disableDeprecationWarnings(){env().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function deprecationWarn(msg){env().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn(deprecationWarn);function disposeVariables(){ENGINE.disposeVariables()}function engine(){return ENGINE}function memory(){return ENGINE.memory()}function profile(f){return ENGINE.profile(f)}function tidy(nameOrFn,fn){return ENGINE.tidy(nameOrFn,fn)}function dispose(container){getTensorsInContainer(container).forEach(tensor2=>tensor2.dispose())}function keep(result){return ENGINE.keep(result)}function time(f){return ENGINE.time(f)}function setBackend(backendName){return ENGINE.setBackend(backendName)}function ready(){return ENGINE.ready()}function getBackend(){return ENGINE.backendName}function removeBackend(name){ENGINE.removeBackend(name)}function findBackend(name){return ENGINE.findBackend(name)}function findBackendFactory(name){return ENGINE.findBackendFactory(name)}function registerBackend(name,factory,priority=1){return ENGINE.registerBackend(name,factory,priority)}function backend(){return ENGINE.backend}function setPlatform(platformName,platform){env().setPlatform(platformName,platform)}function add_(a,b){let $a=convertToTensor(a,"a","add"),$b=convertToTensor(b,"b","add");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Add,inputs)}var add2=op({add_});function floorDiv_(a,b){let $a=convertToTensor(a,"a","floorDiv"),$b=convertToTensor(b,"b","floorDiv");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(FloorDiv,inputs)}var floorDiv=op({floorDiv_});function div_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");if([$a,$b]=makeTypesMatch($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv($a,$b);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(RealDiv,inputs,attrs)}var div=op({div_});function mul_(a,b){let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Multiply,inputs)}var mul=op({mul_});function abs_(x){let $x=convertToTensor(x,"x","abs");if($x.dtype==="complex64"){let inputs={x:$x};return ENGINE.runKernel(ComplexAbs,inputs)}else{let inputs={x:$x};return ENGINE.runKernel(Abs,inputs)}}var abs=op({abs_});function acos_(x){let inputs={x:convertToTensor(x,"x","acos")};return ENGINE.runKernel(Acos,inputs)}var acos=op({acos_});function acosh_(x){let inputs={x:convertToTensor(x,"x","acosh")};return ENGINE.runKernel(Acosh,inputs)}var acosh=op({acosh_});function addN_(tensors){assert(Array.isArray(tensors),()=>"The argument passed to tf.addN() must be a list of tensors"),assert(tensors.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);let $tensors=tensors.map((t,i)=>convertToTensor(t,`tensors${i}`,"addN")),firstTensor=$tensors[0];$tensors.forEach(t=>{if(t.dtype!==firstTensor.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),$tensors.forEach(t=>{if(!arraysEqual(t.shape,firstTensor.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let inputs=$tensors;return ENGINE.runKernel(AddN,inputs)}var addN=op({addN_});function all_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","all","bool")},attrs={axis,keepDims};return ENGINE.runKernel(All,inputs,attrs)}var all=op({all_});function any_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","any","bool")},attrs={axis,keepDims};return ENGINE.runKernel(Any,inputs,attrs)}var any=op({any_});function argMax_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMax")},attrs={axis};return ENGINE.runKernel(ArgMax,inputs,attrs)}var argMax=op({argMax_});function argMin_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMin")},attrs={axis};return ENGINE.runKernel(ArgMin,inputs,attrs)}var argMin=op({argMin_});function asin_(x){let inputs={x:convertToTensor(x,"x","asin")};return ENGINE.runKernel(Asin,inputs)}var asin=op({asin_});function asinh_(x){let inputs={x:convertToTensor(x,"x","asinh")};return ENGINE.runKernel(Asinh,inputs)}var asinh=op({asinh_});function atan_(x){let inputs={x:convertToTensor(x,"x","atan")};return ENGINE.runKernel(Atan,inputs)}var atan=op({atan_});function atan2_(a,b){let $a=convertToTensor(a,"a","atan2"),$b=convertToTensor(b,"b","atan2");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Atan2,inputs)}var atan2=op({atan2_});function atanh_(x){let inputs={x:convertToTensor(x,"x","atanh")};return ENGINE.runKernel(Atanh,inputs)}var atanh=op({atanh_});function computeDilation2DInfo(inputShape,filterShape,strides,pad3,dataFormat="NHWC",dilations){let inputChannels=inputShape[3],$filterShape=[...filterShape,inputChannels],$dataFormat=convertConv2DDataFormat(dataFormat);return computeConv2DInfo(inputShape,$filterShape,strides,dilations,pad3,null,null,$dataFormat)}function computePool2DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,!1,dataFormat)}function computePool3DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,!1,$dataFormat,roundingMode)}function computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam(strides),[dilationHeight,dilationWidth]=parseTupleParam(dilations),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam(dilations),effectiveFilterDepth=getEffectiveFilterSize(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputRows,outputCols]}function computeOutputShape4D(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=round((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode),outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape2D([inHeight,inWidth],filterHeight,strideHeight,pad3,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad3==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad3=="object"){let top=dataFormat==="channelsLast"?pad3[1][0]:pad3[2][0],bottom=dataFormat==="channelsLast"?pad3[1][1]:pad3[2][1],left=dataFormat==="channelsLast"?pad3[2][0]:pad3[3][0],right=dataFormat==="channelsLast"?pad3[2][1]:pad3[3][1];padInfo={top,bottom,left,right,type:top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT"},outHeight=round((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=round((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,front:pad3,back:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape4D([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad3,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad3==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outDepth,outHeight,outWidth}}function round(value,roundingMode){if(!roundingMode)return Math.trunc(value);switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne(param){let[dimA,dimB,dimC]=parseTupleParam(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne(strides,dilations){return tupleValuesAreOne(strides)||tupleValuesAreOne(dilations)}function convertConv2DDataFormat(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function reshape_(x,shape){let inputs={x:convertToTensor(x,"x","reshape","string_or_numeric")},attrs={shape};return ENGINE.runKernel(Reshape,inputs,attrs)}var reshape=op({reshape_});function avgPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","avgPool","float32"),dilations=1;assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool,inputs,attrs);return res=cast(res,$x.dtype),reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool=op({avgPool_});function avgPool3d_(x,filterSize,strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","avgPool3d","float32"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(AvgPool3D,inputs,attrs);return res=cast(res,x5D.dtype),reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3d=op({avgPool3d_});function concat_(tensors,axis=0){assert(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray(tensors,"tensors","concat","string_or_numeric");if($tensors[0].dtype==="complex64"&&$tensors.forEach(tensor2=>{if(tensor2.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
+          with dtype ${tensor2.dtype}. `)}),$tensors.length===1)return clone($tensors[0]);let inputs=$tensors,attr={axis};return ENGINE.runKernel(Concat,inputs,attr)}var concat=op({concat_});function sigmoid_(x){let inputs={x:convertToTensor(x,"x","sigmoid")};return ENGINE.runKernel(Sigmoid,inputs)}var sigmoid=op({sigmoid_});function slice_(x,begin,size){let $x=convertToTensor(x,"x","slice","string_or_numeric");if($x.rank===0)throw new Error("Slicing scalar is not possible");let inputs={x:$x},attrs={begin,size};return ENGINE.runKernel(Slice,inputs,attrs)}var slice=op({slice_});function tanh_(x){let inputs={x:convertToTensor(x,"x","tanh")};return ENGINE.runKernel(Tanh,inputs)}var tanh2=op({tanh_});function basicLSTMCell_(forgetBias,lstmKernel,lstmBias,data,c,h){let $forgetBias=convertToTensor(forgetBias,"forgetBias","basicLSTMCell"),$lstmKernel=convertToTensor(lstmKernel,"lstmKernel","basicLSTMCell"),$lstmBias=convertToTensor(lstmBias,"lstmBias","basicLSTMCell"),$data=convertToTensor(data,"data","basicLSTMCell"),$c=convertToTensor(c,"c","basicLSTMCell"),$h=convertToTensor(h,"h","basicLSTMCell"),combined=concat([$data,$h],1),weighted=matMul(combined,$lstmKernel),res=add2(weighted,$lstmBias),batchSize=res.shape[0],sliceCols=res.shape[1]/4,sliceSize=[batchSize,sliceCols],i=slice(res,[0,0],sliceSize),j=slice(res,[0,sliceCols],sliceSize),f=slice(res,[0,sliceCols*2],sliceSize),o=slice(res,[0,sliceCols*3],sliceSize),newC=add2(mul(sigmoid(i),tanh2(j)),mul($c,sigmoid(add2($forgetBias,f)))),newH=mul(tanh2(newC),sigmoid(o));return[newC,newH]}var basicLSTMCell=op({basicLSTMCell_});function batchToSpaceND_(x,blockShape,crops){let $x=convertToTensor(x,"x","batchToSpaceND"),prod5=blockShape.reduce((a,b)=>a*b);assert($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert($x.shape[0]%prod5==0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`);let inputs={x:$x},attrs={blockShape,crops};return ENGINE.runKernel(BatchToSpaceND,inputs,attrs)}var batchToSpaceND=op({batchToSpaceND_});function xAs4D(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape(x,[1,1,1,x.size]):x.rank===2?x4D=reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_(x,mean4,variance,offset,scale22,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let inputs={x:xAs4D($x),scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE.runKernel(FusedBatchNorm,inputs,attrs);return reshape(res,$x.shape)}var batchNorm=op({batchNorm_});function batchNorm2d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`),assert($mean.rank===2||$mean.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===2||$variance.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===2||$scale.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===2||$offset.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm2d=op({batchNorm2d_});function batchNorm3d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`),assert($mean.rank===3||$mean.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===3||$variance.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===3||$scale.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===3||$offset.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm3d=op({batchNorm3d_});function batchNorm4d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`),assert($mean.rank===4||$mean.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===4||$variance.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===4||$scale.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===4||$offset.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm4d=op({batchNorm4d_});function bincount_(x,weights,size){let $x=convertToTensor(x,"x","bincount"),$weights=convertToTensor(weights,"weights","bincount");assert($x.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${$x.dtype}`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size};return ENGINE.runKernel(Bincount,inputs,attrs)}var bincount=op({bincount_});function broadcastTo_(x,shape){let input2=convertToTensor(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);if(reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0).length===0)return clone(input2);let inputs={x:input2},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var broadcastTo=op({broadcastTo_});function ceil_(x){let inputs={x:convertToTensor(x,"x","ceil")};return ENGINE.runKernel(Ceil,inputs)}var ceil=op({ceil_});function clipByValue_(x,clipValueMin,clipValueMax){let $x=convertToTensor(x,"x","clipByValue");assert(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE.runKernel(ClipByValue,inputs,attrs)}var clipByValue=op({clipByValue_});function concat1d_(tensors){return concat(tensors,0)}var concat1d=op({concat1d_});function concat2d_(tensors,axis){return concat(tensors,axis)}var concat2d=op({concat2d_});function concat3d_(tensors,axis){return concat(tensors,axis)}var concat3d=op({concat3d_});function concat4d_(tensors,axis){return concat(tensors,axis)}var concat4d=op({concat4d_});function conv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(Conv2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d=op({conv2d_});function conv1d_(x,filter,stride,pad3,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor(x,"x","conv1d"),$filter=convertToTensor(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape($x,[1,$x.shape[0],$x.shape[1]])),assert(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert(eitherStridesOrDilationsAreOne(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),res=conv2d(input4D,filter4D,[1,stride],pad3,"NHWC",[1,dilation],dimRoundingMode);return reshapedTo3D?reshape(res,[res.shape[2],res.shape[3]]):reshape(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d=op({conv1d_});function conv2DBackpropInput_(xShape,dy,filter,strides,pad3,dataFormat="NHWC",dimRoundingMode){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE.runKernel(Conv2DBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput=op({conv2DBackpropInput_});function conv2dTranspose_(x,filter,outputShape,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","conv2dTranspose"),$filter=convertToTensor(filter,"filter","conv2dTranspose");return conv2DBackpropInput(outputShape,$x,$filter,strides,pad3,"NHWC",dimRoundingMode)}var conv2dTranspose=op({conv2dTranspose_});function conv3d_(x,filter,strides,pad3,dataFormat="NDHWC",dilations=[1,1,1]){let $x=convertToTensor(x,"x","conv3d"),$filter=convertToTensor(filter,"filter","conv3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`),assert($filter.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`),assert(x5D.shape[4]===$filter.shape[3],()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NDHWC",()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);let inputs={x:x5D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations},res=ENGINE.runKernel(Conv3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3d=op({conv3d_});function conv3DBackpropInput_(xShape,dy,filter,strides,pad3){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let inputs={dy:dy5D,filter},attrs={pad:pad3,strides,inputShape:xShape5D},res=ENGINE.runKernel(Conv3DBackpropInputV2,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput=op({conv3DBackpropInput_});function conv3dTranspose_(x,filter,outputShape,strides,pad3){let $x=convertToTensor(x,"x","conv3dTranspose"),$filter=convertToTensor(filter,"filter","conv3dTranspose");return conv3DBackpropInput(outputShape,$x,$filter,strides,pad3)}var conv3dTranspose=op({conv3dTranspose_});function cos_(x){let inputs={x:convertToTensor(x,"x","cos")};return ENGINE.runKernel(Cos,inputs)}var cos=op({cos_});function cosh_(x){let inputs={x:convertToTensor(x,"x","cosh")};return ENGINE.runKernel(Cosh,inputs)}var cosh=op({cosh_});function cumsum_(x,axis=0,exclusive=!1,reverse5=!1){let inputs={x:convertToTensor(x,"x","cumsum")},attrs={axis,exclusive,reverse:reverse5};return ENGINE.runKernel(Cumsum,inputs,attrs)}var cumsum=op({cumsum_});function denseBincount_(x,weights,size,binaryOutput=!1){let $x=convertToTensor(x,"x","denseBincount"),$weights=convertToTensor(weights,"weights","denseBincount");assert($x.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`),assert($x.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size,binaryOutput};return ENGINE.runKernel(DenseBincount,inputs,attrs)}var denseBincount=op({denseBincount_});function depthToSpace_(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
     ${inputHeight} and ${blockSize}  for depthToSpace with input shape
     ${$x.shape}`),assert(inputWidth*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
     ${inputWidth} and ${blockSize} for depthToSpace with input shape
-        ${$x.shape}`),assert(inputDepth%(blockSize*blockSize)===0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernel(DepthToSpace,inputs,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(DepthwiseConv2dNative,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let inputs={x:convertToTensor(x,"x","diag")};return ENGINE.runKernel(Diag,inputs)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad3,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i<inRank;i++){let dim=inRank-1-i,a=inShape[dim]||1;(outShape[outShape.length-1-i]||1)>1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_(a,b){let $a=convertToTensor(a,"a","equal"),$b=convertToTensor(b,"b","equal");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Equal,inputs)}var equal=op({equal_});function where_(condition,a,b){let $a=convertToTensor(a,"a","where"),$b=convertToTensor(b,"b","where"),$condition=convertToTensor(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape($a.shape,$b.shape),$broadcastedA=broadcastTo($a,broadcastShape),$broadcastedB=broadcastTo($b,broadcastShape);$condition.rank===1&&assert($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernel(Select,inputs)}var where=op({where_});function zerosLike_(x){let inputs={x:convertToTensor(x,"x","zerosLike")};return ENGINE.runKernel(ZerosLike,inputs)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros4=zerosLike(divResult),bEqualsZero=equal($b,zeros4);return where(bEqualsZero,zeros4,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]);return matMul($t1,t22D)}}var dot=op({dot_});function elu_(x){let inputs={x:convertToTensor(x,"x","elu")};return ENGINE.runKernel(Elu,inputs)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernel(Erf,inputs)}var erf=op({erf_});function exp_(x){let inputs={x:convertToTensor(x,"x","exp")};return ENGINE.runKernel(Exp,inputs)}var exp=op({exp_});function expandDims_(x,axis=0){let $x=convertToTensor(x,"x","expandDims","string_or_numeric");assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let inputs={input:$x},attrs={dim:axis};return ENGINE.runKernel(ExpandDims,inputs,attrs)}var expandDims=op({expandDims_});function expm1_(x){let inputs={x:convertToTensor(x,"x","expm1")};return ENGINE.runKernel(Expm1,inputs)}var expm1=op({expm1_});function tile_(x,reps){let $x=convertToTensor(x,"x","tile","string_or_numeric");assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let inputs={x:$x},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile(expandDims(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile(expandDims(expandDims(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile(expandDims(expandDims(expandDims(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye=op({eye_});function fill(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE.runKernel(Fill,{},attrs)}function floor_(x){let inputs={x:convertToTensor(x,"x","floor")};return ENGINE.runKernel(Floor,inputs)}var floor=op({floor_});function gather_(x,indices,axis=0,batchDims=0){let $x=convertToTensor(x,"x","gather"),$indices=convertToTensor(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis,batchDims};return ENGINE.runKernel(GatherV2,inputs,attrs)}var gather=op({gather_});function greater_(a,b){let $a=convertToTensor(a,"a","greater"),$b=convertToTensor(b,"b","greater");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Greater,inputs)}var greater=op({greater_});function greaterEqual_(a,b){let $a=convertToTensor(a,"a","greaterEqual"),$b=convertToTensor(b,"b","greaterEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(GreaterEqual,inputs)}var greaterEqual=op({greaterEqual_});function imag_(input2){let inputs={input:convertToTensor(input2,"input","imag")};return ENGINE.runKernel(Imag,inputs)}var imag=op({imag_});function isFinite_(x){let inputs={x:convertToTensor(x,"x","isFinite")};return ENGINE.runKernel(IsFinite,inputs)}var isFinite2=op({isFinite_});function isInf_(x){let inputs={x:convertToTensor(x,"x","isInf")};return ENGINE.runKernel(IsInf,inputs)}var isInf=op({isInf_});function isNaN_(x){let inputs={x:convertToTensor(x,"x","isNaN")};return ENGINE.runKernel(IsNan,inputs)}var isNaN2=op({isNaN_});function leakyRelu_(x,alpha=.2){let inputs={x:convertToTensor(x,"x","leakyRelu")},attrs={alpha};return ENGINE.runKernel(LeakyRelu,inputs,attrs)}var leakyRelu=op({leakyRelu_});function less_(a,b){let $a=convertToTensor(a,"a","less"),$b=convertToTensor(b,"b","less");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Less,inputs)}var less=op({less_});function lessEqual_(a,b){let $a=convertToTensor(a,"a","lessEqual"),$b=convertToTensor(b,"b","lessEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LessEqual,inputs)}var lessEqual=op({lessEqual_});function linspace(start,stop,num){if(num<=0)throw new Error("The number of values should be positive.");let attrs={start,stop,num};return ENGINE.runKernel(LinSpace,{},attrs)}function localResponseNormalization_(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor(x,"x","localResponseNormalization");assert($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
+        ${$x.shape}`),assert(inputDepth%(blockSize*blockSize)==0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernel(DepthToSpace,inputs,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(DepthwiseConv2dNative,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let inputs={x:convertToTensor(x,"x","diag")};return ENGINE.runKernel(Diag,inputs)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad3,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i<inRank;i++){let dim=inRank-1-i,a=inShape[dim]||1;(outShape[outShape.length-1-i]||1)>1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_(a,b){let $a=convertToTensor(a,"a","equal"),$b=convertToTensor(b,"b","equal");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Equal,inputs)}var equal=op({equal_});function where_(condition,a,b){let $a=convertToTensor(a,"a","where"),$b=convertToTensor(b,"b","where"),$condition=convertToTensor(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape($a.shape,$b.shape),$broadcastedA=broadcastTo($a,broadcastShape),$broadcastedB=broadcastTo($b,broadcastShape);$condition.rank===1&&assert($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernel(Select,inputs)}var where=op({where_});function zerosLike_(x){let inputs={x:convertToTensor(x,"x","zerosLike")};return ENGINE.runKernel(ZerosLike,inputs)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros4=zerosLike(divResult),bEqualsZero=equal($b,zeros4);return where(bEqualsZero,zeros4,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]);return matMul($t1,t22D)}}var dot=op({dot_});function elu_(x){let inputs={x:convertToTensor(x,"x","elu")};return ENGINE.runKernel(Elu,inputs)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernel(Erf,inputs)}var erf=op({erf_});function exp_(x){let inputs={x:convertToTensor(x,"x","exp")};return ENGINE.runKernel(Exp,inputs)}var exp=op({exp_});function expandDims_(x,axis=0){let $x=convertToTensor(x,"x","expandDims","string_or_numeric");assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let inputs={input:$x},attrs={dim:axis};return ENGINE.runKernel(ExpandDims,inputs,attrs)}var expandDims=op({expandDims_});function expm1_(x){let inputs={x:convertToTensor(x,"x","expm1")};return ENGINE.runKernel(Expm1,inputs)}var expm1=op({expm1_});function tile_(x,reps){let $x=convertToTensor(x,"x","tile","string_or_numeric");assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let inputs={x:$x},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile(expandDims(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile(expandDims(expandDims(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile(expandDims(expandDims(expandDims(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye=op({eye_});function fill(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE.runKernel(Fill,{},attrs)}function floor_(x){let inputs={x:convertToTensor(x,"x","floor")};return ENGINE.runKernel(Floor,inputs)}var floor=op({floor_});function gather_(x,indices,axis=0,batchDims=0){let $x=convertToTensor(x,"x","gather"),$indices=convertToTensor(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis,batchDims};return ENGINE.runKernel(GatherV2,inputs,attrs)}var gather=op({gather_});function greater_(a,b){let $a=convertToTensor(a,"a","greater"),$b=convertToTensor(b,"b","greater");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Greater,inputs)}var greater=op({greater_});function greaterEqual_(a,b){let $a=convertToTensor(a,"a","greaterEqual"),$b=convertToTensor(b,"b","greaterEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(GreaterEqual,inputs)}var greaterEqual=op({greaterEqual_});function imag_(input2){let inputs={input:convertToTensor(input2,"input","imag")};return ENGINE.runKernel(Imag,inputs)}var imag=op({imag_});function isFinite_(x){let inputs={x:convertToTensor(x,"x","isFinite")};return ENGINE.runKernel(IsFinite,inputs)}var isFinite2=op({isFinite_});function isInf_(x){let inputs={x:convertToTensor(x,"x","isInf")};return ENGINE.runKernel(IsInf,inputs)}var isInf=op({isInf_});function isNaN_(x){let inputs={x:convertToTensor(x,"x","isNaN")};return ENGINE.runKernel(IsNan,inputs)}var isNaN2=op({isNaN_});function leakyRelu_(x,alpha=.2){let inputs={x:convertToTensor(x,"x","leakyRelu")},attrs={alpha};return ENGINE.runKernel(LeakyRelu,inputs,attrs)}var leakyRelu=op({leakyRelu_});function less_(a,b){let $a=convertToTensor(a,"a","less"),$b=convertToTensor(b,"b","less");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Less,inputs)}var less=op({less_});function lessEqual_(a,b){let $a=convertToTensor(a,"a","lessEqual"),$b=convertToTensor(b,"b","lessEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LessEqual,inputs)}var lessEqual=op({lessEqual_});function linspace(start,stop,num){if(num<=0)throw new Error("The number of values should be positive.");let attrs={start,stop,num};return ENGINE.runKernel(LinSpace,{},attrs)}function localResponseNormalization_(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor(x,"x","localResponseNormalization");assert($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
                rank ${$x.rank}.`),assert(isInt(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE.runKernel(LRN,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization=op({localResponseNormalization_});function log_(x){let inputs={x:convertToTensor(x,"x","log")};return ENGINE.runKernel(Log,inputs)}var log=op({log_});function log1p_(x){let inputs={x:convertToTensor(x,"x","log1p")};return ENGINE.runKernel(Log1p,inputs)}var log1p=op({log1p_});function grad(f){return assert(isFunction(f),()=>"The f passed in grad(f) must be a function"),(x,dy)=>{let $x=convertToTensor(x,"x","tf.grad","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grad"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f($x),[$x],$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(grads2),grads2[0]})}}function grads(f){return assert(isFunction(f),()=>"The f passed in grads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let $args=convertToTensorArray(args,"args","tf.grads","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grads"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f(...$args),$args,$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(grads2),grads2})}}function valueAndGrad(f){return assert(isFunction(f),()=>"The f passed in valueAndGrad(f) must be a function"),(x,dy)=>{assert(x instanceof Tensor,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:grads2,value}=ENGINE.gradients(()=>f(x),[x],dy);return checkGrads(grads2),{grad:grads2[0],value}}}function valueAndGrads(f){return assert(isFunction(f),()=>"The f passed in valueAndGrads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args)&&args.every(arg=>arg instanceof Tensor),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let res=ENGINE.gradients(()=>f(...args),args,dy);return dy!=null&&assertShapesMatch(res.value.shape,dy.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(res.grads),res}}function variableGrads(f,varList){assert(isFunction(f),()=>"The f passed in variableGrads(f) must be a function"),assert(varList==null||Array.isArray(varList)&&varList.every(v=>v instanceof Variable),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let specifiedVarList=varList!=null;if(!specifiedVarList){varList=[];for(let varName in ENGINE.registeredVariables)varList.push(ENGINE.registeredVariables[varName])}let specifiedNonTrainable=specifiedVarList?varList.filter(variable2=>!variable2.trainable):null,originalVarCount=varList.length;varList=varList.filter(variable2=>variable2.trainable),assert(varList.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);let allowNoGradients=!0,{value,grads:grads2}=ENGINE.gradients(f,varList,null,allowNoGradients);assert(grads2.some(g=>g!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(value.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);let namedGrads={};return varList.forEach((v,i)=>{grads2[i]!=null&&(namedGrads[v.name]=grads2[i])}),specifiedNonTrainable!=null&&specifiedNonTrainable.forEach(v=>namedGrads[v.name]=null),{value,grads:namedGrads}}function customGrad(f){return ENGINE.customGrad(f)}function checkGrads(grads2){if(grads2.filter(g=>g==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
-    the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function logSumExp_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","logSumExp"),axes=parseAxisParam(axis,$x.shape),xMax=max($x,axes,!0),a=sub($x,xMax),b=exp(a),c=sum2(b,axes),d=log(c),res=add2(reshape(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,axes);return reshape(res,newShape)}return res}var logSumExp=op({logSumExp_});function logicalAnd_(a,b){let $a=convertToTensor(a,"a","logicalAnd","bool"),$b=convertToTensor(b,"b","logicalAnd","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalAnd,inputs)}var logicalAnd=op({logicalAnd_});function logicalNot_(x){let inputs={x:convertToTensor(x,"x","logicalNot","bool")};return ENGINE.runKernel(LogicalNot,inputs)}var logicalNot=op({logicalNot_});function logicalOr_(a,b){let $a=convertToTensor(a,"a","logicalOr","bool"),$b=convertToTensor(b,"b","logicalOr","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalOr,inputs)}var logicalOr=op({logicalOr_});function logicalXor_(a,b){let $a=convertToTensor(a,"a","logicalXor","bool"),$b=convertToTensor(b,"b","logicalXor","bool");return assertAndGetBroadcastShape($a.shape,$b.shape),logicalAnd(logicalOr(a,b),logicalNot(logicalAnd(a,b)))}var logicalXor=op({logicalXor_});function maxPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data,c,h){let $data=convertToTensor(data,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i<lstmCells.length;i++){let output=lstmCells[i](input2,$c[i],$h[i]);newStates.push(output[0]),newStates.push(output[1]),input2=output[1]}let newC=[],newH=[];for(let i=0;i<newStates.length;i+=2)newC.push(newStates[i]),newH.push(newStates[i+1]);return[newC,newH]}var multiRNNCell=op({multiRNNCell_});function multinomial_(logits,numSamples,seed,normalized=!1){let $logits=convertToTensor(logits,"logits","multinomial"),numOutcomes=$logits.size,origRank=$logits.rank;if(numOutcomes<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);if(origRank>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]===0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i<size;i++)values[i]=randFunction();return ENGINE.makeTensor(values,shape,dtype)}var rand=op({rand_}),seedrandom=__toModule2(require_seedrandom2()),MPRandGauss=class{constructor(mean4,stdDeviation,dtype,truncated,seed){this.mean=mean4,this.stdDev=stdDeviation,this.dtype=dtype,this.nextVal=NaN,this.truncated=truncated,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let seedValue=seed||Math.random();this.random=seedrandom.alea(seedValue.toString())}nextValue(){if(!isNaN(this.nextVal)){let value=this.nextVal;return this.nextVal=NaN,value}let resultX,resultY,isValid=!1;for(;!isValid;){let v1,v2,s;do v1=2*this.random()-1,v2=2*this.random()-1,s=v1*v1+v2*v2;while(s>=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),u<v0||Math.log(u)<v1)break}return v=1/this.beta*this.d*v,this.alpha<1&&(v*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(v)}convertValue(value){return this.dtype==="float32"?value:Math.round(value)}},UniformRandom=class{constructor(min6=0,max6=1,dtype,seed){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=rgamma.nextValue();return res.toTensor()}var randomGamma=op({randomGamma_});function randomNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error(`Unsupported data type ${dtype}`);let randGauss=new MPRandGauss(mean4,stdDev,dtype,!1,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var randomNormal=op({randomNormal_});function randomUniform_(shape,minval=0,maxval=1,dtype="float32",seed){let res=buffer(shape,dtype),random=new UniformRandom(minval,maxval,null,seed);for(let i=0;i<res.values.length;i++)res.values[i]=random.nextValue();return res.toTensor()}var randomUniform=op({randomUniform_});function range(start,stop,step4=1,dtype="float32"){if(step4===0)throw new Error("Cannot have a step of zero");let attrs={start,stop,step:step4,dtype};return ENGINE.runKernel(Range,{},attrs)}function real_(input2){let inputs={input:convertToTensor(input2,"input","real")};return ENGINE.runKernel(Real,inputs)}var real=op({real_});function reciprocal_(x){let inputs={x:convertToTensor(x,"x","reciprocal")};return ENGINE.runKernel(Reciprocal,inputs)}var reciprocal=op({reciprocal_});function relu_(x){let inputs={x:convertToTensor(x,"x","relu")};return ENGINE.runKernel(Relu,inputs)}var relu=op({relu_});function relu6_(x){let inputs={x:convertToTensor(x,"x","relu6")};return ENGINE.runKernel(Relu6,inputs)}var relu6=op({relu6_});function reverse_(x,axis){let inputs={x:convertToTensor(x,"x","reverse")},attrs={dims:axis};return ENGINE.runKernel(Reverse,inputs,attrs)}var reverse=op({reverse_});function reverse1d_(x){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i<xVals.length;i++)ySet.has(xVals[i])||outputSize++;let buffer2=new TensorBuffer([outputSize],$x.dtype),indices=new TensorBuffer([outputSize],"int32");for(let i=0,p2=0;i<xVals.length;i++)ySet.has(xVals[i])||(buffer2.values[p2]=xVals[i],indices.values[p2]=i,p2++);return[buffer2.toTensor(),indices.toTensor()]}var setdiff1dAsync=setdiff1dAsync_;function sign_(x){let inputs={x:convertToTensor(x,"x","sign")};return ENGINE.runKernel(Sign,inputs)}var sign=op({sign_});function sin_(x){let inputs={x:convertToTensor(x,"x","sin")};return ENGINE.runKernel(Sin,inputs)}var sin=op({sin_});function sinh_(x){let inputs={x:convertToTensor(x,"x","sinh")};return ENGINE.runKernel(Sinh,inputs)}var sinh=op({sinh_});function slice1d_(x,begin,size){let $x=convertToTensor(x,"x","slice1d");return assert($x.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var truncatedNormal=op({truncatedNormal_});function unique_(x,axis=0){let $x=convertToTensor(x,"x","unique","string_or_numeric");assert($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i<condVals.length;i++)condVals[i]&&indices.push(i);let inBuffer=buffer(condShape,"int32"),out=buffer([indices.length,condShape.length],"int32");for(let i=0;i<indices.length;i++){let loc=inBuffer.indexToLoc(indices[i]),offset=i*condShape.length;out.values.set(loc,offset)}return out.toTensor()}async function whereAsync_(condition){let $condition=convertToTensor(condition,"condition","whereAsync","bool"),vals=await $condition.data(),res=whereImpl($condition.shape,vals);return condition!==$condition&&$condition.dispose(),res}var whereAsync=whereAsync_;async function booleanMaskAsync_(tensor2,mask,axis){let $tensor=convertToTensor(tensor2,"tensor","boolMask"),$mask=convertToTensor(mask,"mask","boolMask","bool"),axisFrom=axis==null?0:axis,maskDim=$mask.rank,tensorShape=$tensor.shape;assert(maskDim>0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i<axisFrom+maskDim;i++)leadingSize*=tensorShape[i];let targetTensorShape=tensorShape.slice(0,axisFrom).concat([leadingSize],tensorShape.slice(axisFrom+maskDim)),reshapedTensor=reshape($tensor,targetTensorShape),reshapedMask=reshape($mask,[-1]),positivePositions=await whereAsync(reshapedMask),indices=squeeze(positivePositions,[1]),res=gather(reshapedTensor,indices,axisFrom);return tensor2!==$tensor&&$tensor.dispose(),mask!==$mask&&$mask.dispose(),indices.dispose(),reshapedTensor.dispose(),reshapedMask.dispose(),positivePositions.dispose(),res}var booleanMaskAsync=booleanMaskAsync_;function notEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","notEqualStrict"),$b=convertToTensor(b,"b","notEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual($a,$b)}function lessStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessStrict"),$b=convertToTensor(b,"b","lessStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessStrict: "),less($a,$b)}function equalStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","equalStrict"),$b=convertToTensor(b,"b","equalStrict");return assertShapesMatch($a.shape,$b.shape,"Error in equalStrict: "),equal($a,$b)}function lessEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessEqualStrict"),$b=convertToTensor(b,"b","lessEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual($a,$b)}function greaterStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterStrict"),$b=convertToTensor(b,"b","greaterStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterStrict: "),greater($a,$b)}function greaterEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterEqualStrict"),$b=convertToTensor(b,"b","greaterEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual($a,$b)}var equalStrict=op({equalStrict_}),greaterEqualStrict=op({greaterEqualStrict_}),greaterStrict=op({greaterStrict_}),lessEqualStrict=op({lessEqualStrict_}),lessStrict=op({lessStrict_}),notEqualStrict=op({notEqualStrict_});function addStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","addStrict"),$b=convertToTensor(b,"b","addStrict");return assertShapesMatch($a.shape,$b.shape,"Error in addStrict: "),add2($a,$b)}function subStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","subStrict"),$b=convertToTensor(b,"b","subStrict");return assertShapesMatch($a.shape,$b.shape,"Error in subStrict: "),sub($a,$b)}function powStrict_(base2,exp4){return deprecationWarn("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch(base2.shape,exp4.shape,"Error in powStrict: "),pow(base2,exp4)}function mulStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");return assertShapesMatch($a.shape,$b.shape,"Error in multiplyStrict: "),mul($a,$b)}function divStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");return assertShapesMatch($a.shape,$b.shape,"Error in divideStrict: "),div($a,$b)}function modStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","modStrict"),$b=convertToTensor(b,"b","modStrict");return assertShapesMatch($a.shape,$b.shape,"Error in modStrict: "),mod($a,$b)}function minimumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","minimumStrict"),$b=convertToTensor(b,"b","minimumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in minimumStrict: "),minimum($a,$b)}function maximumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","maximumStrict"),$b=convertToTensor(b,"b","maximumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in maximumStrict: "),maximum($a,$b)}function squaredDifferenceStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","squaredDifferenceStrict"),$b=convertToTensor(b,"b","squaredDifferenceStrict");return assertShapesMatch($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference($a,$b)}var addStrict=op({addStrict_}),divStrict=op({divStrict_}),maximumStrict=op({maximumStrict_}),minimumStrict=op({minimumStrict_}),modStrict=op({modStrict_}),mulStrict=op({mulStrict_}),powStrict=op({powStrict_}),squaredDifferenceStrict=op({squaredDifferenceStrict_}),subStrict=op({subStrict_});function norm_(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor(x,"x","norm");let norm2=normImpl(x,ord,axis),keepDimsShape=norm2.shape;if(keepDims){let axes=parseAxisParam(axis,x.shape);keepDimsShape=expandShapeToKeepDim(norm2.shape,axes)}return reshape(norm2,keepDimsShape)}function normImpl(x,p2,axis=null){if(x.rank===0)return abs(x);if(x.rank!==1&&axis===null)return normImpl(reshape(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum2(abs(x),axis);if(p2===Infinity)return max(abs(x),axis);if(p2===-Infinity)return min(abs(x),axis);if(p2==="euclidean"||p2===2)return sqrt(sum2(pow(abs(x),scalar(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max(sum2(abs(x),axis[0]),axis[1]-1);if(p2===Infinity)return max(sum2(abs(x),axis[1]),axis[0]);if(p2===-Infinity)return min(sum2(abs(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt(sum2(square(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm=op({norm_});function movingAverage_(v,x,decay,step4,zeroDebias=!0){let $v=convertToTensor(v,"v","movingAverage"),$x=convertToTensor(x,"x","movingAverage"),$decay=convertToTensor(decay,"decay","movingAverage");assertTypesMatch($v,$x),assert(arraysEqual($v.shape,$x.shape),()=>"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i<x.shape.length;i++)noiseShape[i]==null&&x.shape[i]!=null?newDimension.push(x.shape[i]):newDimension.push(noiseShape[i]);return newDimension}return noiseShape}function dropout_(x,rate,noiseShape,seed){let $x=convertToTensor(x,"x","dropout");if(assert($x.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d(newValues,"float32")}async function inTopKAsync_(predictions,targets,k=1){let $predictions=convertToTensor(predictions,"predictions","inTopK"),$targets=convertToTensor(targets,"targets","inTopK");assert($predictions.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;b<batch;b++){let offset=b*size,vals=predictionsVals.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value),precision3[b]=0;for(let i=0;i<k;i++)if(valAndInd[i].index===targetsVals[b]){precision3[b]=1;break}}return predictions!==$predictions&&$predictions.dispose(),targets!==$targets&&$targets.dispose(),tensor(precision3,$targets.shape,"bool")}var inTopKAsync=inTopKAsync_,fused_ops_exports={};__export2(fused_ops_exports,{conv2d:()=>conv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal2.size;){let padLen=start+frameLength-signal2.size,pad3=concat([slice(signal2,start,frameLength-padLen),fill([padLen],padValue)]);output.push(pad3),start+=frameStep}return output.length===0?tensor2d([],[0,frameLength]):reshape(concat(output),[output.length,frameLength])}var frame=op({frame_});function stft_(signal2,frameLength,frameStep,fftLength,windowFn=hannWindow){fftLength==null&&(fftLength=enclosingPowerOfTwo(frameLength));let framedSignal=frame(signal2,frameLength,frameStep),windowedSignal=mul(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft(slice(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat(output)}var stft=op({stft_});function cropAndResize_(image3,boxes,boxInd,cropSize,method="bilinear",extrapolationValue=0){let $image=convertToTensor(image3,"image","cropAndResize"),$boxes=convertToTensor(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor(boxInd,"boxInd","cropAndResize","int32"),numBoxes=$boxes.shape[0];assert($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a<b?-1:0}function binarySearch_(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale22=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou2=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou2>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale22,iou2),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale22,iou2){let weight=Math.exp(scale22*iou2*iou2);return iou2<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1===0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1===0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul(sum2(mul(ys[j],x)),ys[j]);x=sub(x,proj)}return div(x,norm(x,"euclidean"))}));return inputIsTensor2D?stack(ys,0):ys}var gramSchmidt=op({gramSchmidt_});function qr_(x,fullMatrices=!1){if(assert(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE.tidy(()=>{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon32=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon32),l13=neg(mul($labels,log(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon32=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon32,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.rho,config.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config){return new cls(config.learningRate,config.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon,config.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config){return new cls(config.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config){return new cls(config.learningRate,config.momentum,config.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon32,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config){return new cls(config.learningRate,config.decay,config.momentum,config.epsilon,config.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon32,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon32=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon32)}static adadelta(learningRate=.001,rho=.95,epsilon32=null){return new AdadeltaOptimizer(learningRate,rho,epsilon32)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon32=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon32,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log2,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(inSize){return inSize<=PARALLELIZE_THRESHOLD?inSize:nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)))}function getImageCenter(center,imageHeight,imageWidth){let centerX=imageWidth*(typeof center=="number"?center:center[0]),centerY=imageHeight*(typeof center=="number"?center:center[1]);return[centerX,centerY]}function getReshaped(inputShape,blockShape,prod5,batchToSpace=!0){let reshaped=[];if(batchToSpace)reshaped=reshaped.concat(blockShape.slice(0)),reshaped.push(inputShape[0]/prod5),reshaped=reshaped.concat(inputShape.slice(1));else{reshaped=reshaped.concat(inputShape[0]);let spatialLength=blockShape.length;for(let i=0;i<spatialLength;++i)reshaped=reshaped.concat([inputShape[i+1]/blockShape[i],blockShape[i]]);reshaped=reshaped.concat(inputShape.slice(spatialLength+1))}return reshaped}function getPermuted(reshapedRank,blockShapeRank,batchToSpace=!0){let permuted=[];if(batchToSpace){permuted.push(blockShapeRank);for(let i=blockShapeRank+1;i<reshapedRank;++i)i<=2*blockShapeRank?(permuted.push(i),permuted.push(i-(blockShapeRank+1))):permuted.push(i)}else{let permutedBeforeBatch=[],permutedAfterBatch=[];for(let i=1;i<reshapedRank;++i)i>=blockShapeRank*2+1||i%2===1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i<inputShape.length;++i)i<=blockShape.length?batchToSpace?reshapedPermuted.push(blockShape[i-1]*inputShape[i]):reshapedPermuted.push(inputShape[i]/blockShape[i-1]):reshapedPermuted.push(inputShape[i]);return reshapedPermuted}function getSliceBeginCoords(crops,blockShape){let sliceBeginCoords=[0];for(let i=0;i<blockShape;++i)sliceBeginCoords.push(crops[i][0]);return sliceBeginCoords}function getSliceSize(uncroppedShape,crops,blockShape){let sliceSize=uncroppedShape.slice(0,1);for(let i=0;i<blockShape;++i)sliceSize.push(uncroppedShape[i+1]-crops[i][0]-crops[i][1]);return sliceSize}var SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429;function warn(...msg){env().getBool("IS_TEST")||console.warn(...msg)}function log2(...msg){env().getBool("IS_TEST")||console.log(...msg)}function mergeRealAndImagArrays(real4,imag4){if(real4.length!==imag4.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`);let result=new Float32Array(real4.length*2);for(let i=0;i<result.length;i+=2)result[i]=real4[i/2],result[i+1]=imag4[i/2];return result}function splitRealAndImagArrays(complex4){let real4=new Float32Array(complex4.length/2),imag4=new Float32Array(complex4.length/2);for(let i=0;i<complex4.length;i+=2)real4[i/2]=complex4[i],imag4[i/2]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithEvenIndex(complex4){let len=Math.ceil(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=0;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithOddIndex(complex4){let len=Math.floor(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=2;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function getComplexWithIndex(complex4,index){let real4=complex4[index*2],imag4=complex4[index*2+1];return{real:real4,imag:imag4}}function assignToTypedArray(data,real4,imag4,index){data[index*2]=real4,data[index*2+1]=imag4}function exponents(n,inverse){let real4=new Float32Array(n/2),imag4=new Float32Array(n/2);for(let i=0;i<Math.ceil(n/2);i++){let x=(inverse?2:-2)*Math.PI*(i/n);real4[i]=Math.cos(x),imag4[i]=Math.sin(x)}return{real:real4,imag:imag4}}function exponent(k,n,inverse){let x=(inverse?2:-2)*Math.PI*(k/n),real4=Math.cos(x),imag4=Math.sin(x);return{real:real4,imag:imag4}}function prepareSplitSize(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert(x.shape[axis]%numOrSizeSplits===0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)dim!==axis?outShape.push(aShape[dim]):outShape.push(numSegments);return outShape}function collectGatherOpShapeInfo(x,indices,axis,batchDims){let indicesRank=indices.shape.length,xRank=x.shape.length;if(batchDims!==0&&(batchDims<-indicesRank||batchDims>indicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
+    the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function logSumExp_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","logSumExp"),axes=parseAxisParam(axis,$x.shape),xMax=max($x,axes,!0),a=sub($x,xMax),b=exp(a),c=sum2(b,axes),d=log(c),res=add2(reshape(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,axes);return reshape(res,newShape)}return res}var logSumExp=op({logSumExp_});function logicalAnd_(a,b){let $a=convertToTensor(a,"a","logicalAnd","bool"),$b=convertToTensor(b,"b","logicalAnd","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalAnd,inputs)}var logicalAnd=op({logicalAnd_});function logicalNot_(x){let inputs={x:convertToTensor(x,"x","logicalNot","bool")};return ENGINE.runKernel(LogicalNot,inputs)}var logicalNot=op({logicalNot_});function logicalOr_(a,b){let $a=convertToTensor(a,"a","logicalOr","bool"),$b=convertToTensor(b,"b","logicalOr","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalOr,inputs)}var logicalOr=op({logicalOr_});function logicalXor_(a,b){let $a=convertToTensor(a,"a","logicalXor","bool"),$b=convertToTensor(b,"b","logicalXor","bool");return assertAndGetBroadcastShape($a.shape,$b.shape),logicalAnd(logicalOr(a,b),logicalNot(logicalAnd(a,b)))}var logicalXor=op({logicalXor_});function maxPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data,c,h){let $data=convertToTensor(data,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i<lstmCells.length;i++){let output=lstmCells[i](input2,$c[i],$h[i]);newStates.push(output[0]),newStates.push(output[1]),input2=output[1]}let newC=[],newH=[];for(let i=0;i<newStates.length;i+=2)newC.push(newStates[i]),newH.push(newStates[i+1]);return[newC,newH]}var multiRNNCell=op({multiRNNCell_});function multinomial_(logits,numSamples,seed,normalized=!1){let $logits=convertToTensor(logits,"logits","multinomial"),numOutcomes=$logits.size,origRank=$logits.rank;if(numOutcomes<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);if(origRank>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]==0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i<size;i++)values[i]=randFunction();return ENGINE.makeTensor(values,shape,dtype)}var rand=op({rand_}),seedrandom=__toModule2(require_seedrandom2()),MPRandGauss=class{constructor(mean4,stdDeviation,dtype,truncated,seed){this.mean=mean4,this.stdDev=stdDeviation,this.dtype=dtype,this.nextVal=NaN,this.truncated=truncated,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let seedValue=seed||Math.random();this.random=seedrandom.alea(seedValue.toString())}nextValue(){if(!isNaN(this.nextVal)){let value=this.nextVal;return this.nextVal=NaN,value}let resultX,resultY,isValid=!1;for(;!isValid;){let v1,v2,s;do v1=2*this.random()-1,v2=2*this.random()-1,s=v1*v1+v2*v2;while(s>=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),u<v0||Math.log(u)<v1)break}return v=1/this.beta*this.d*v,this.alpha<1&&(v*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(v)}convertValue(value){return this.dtype==="float32"?value:Math.round(value)}},UniformRandom=class{constructor(min6=0,max6=1,dtype,seed){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=rgamma.nextValue();return res.toTensor()}var randomGamma=op({randomGamma_});function randomNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error(`Unsupported data type ${dtype}`);let randGauss=new MPRandGauss(mean4,stdDev,dtype,!1,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var randomNormal=op({randomNormal_});function randomUniform_(shape,minval=0,maxval=1,dtype="float32",seed){let res=buffer(shape,dtype),random=new UniformRandom(minval,maxval,null,seed);for(let i=0;i<res.values.length;i++)res.values[i]=random.nextValue();return res.toTensor()}var randomUniform=op({randomUniform_});function range(start,stop,step4=1,dtype="float32"){if(step4===0)throw new Error("Cannot have a step of zero");let attrs={start,stop,step:step4,dtype};return ENGINE.runKernel(Range,{},attrs)}function real_(input2){let inputs={input:convertToTensor(input2,"input","real")};return ENGINE.runKernel(Real,inputs)}var real=op({real_});function reciprocal_(x){let inputs={x:convertToTensor(x,"x","reciprocal")};return ENGINE.runKernel(Reciprocal,inputs)}var reciprocal=op({reciprocal_});function relu_(x){let inputs={x:convertToTensor(x,"x","relu")};return ENGINE.runKernel(Relu,inputs)}var relu=op({relu_});function relu6_(x){let inputs={x:convertToTensor(x,"x","relu6")};return ENGINE.runKernel(Relu6,inputs)}var relu6=op({relu6_});function reverse_(x,axis){let inputs={x:convertToTensor(x,"x","reverse")},attrs={dims:axis};return ENGINE.runKernel(Reverse,inputs,attrs)}var reverse=op({reverse_});function reverse1d_(x){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i<xVals.length;i++)ySet.has(xVals[i])||outputSize++;let buffer2=new TensorBuffer([outputSize],$x.dtype),indices=new TensorBuffer([outputSize],"int32");for(let i=0,p2=0;i<xVals.length;i++)ySet.has(xVals[i])||(buffer2.values[p2]=xVals[i],indices.values[p2]=i,p2++);return[buffer2.toTensor(),indices.toTensor()]}var setdiff1dAsync=setdiff1dAsync_;function sign_(x){let inputs={x:convertToTensor(x,"x","sign")};return ENGINE.runKernel(Sign,inputs)}var sign=op({sign_});function sin_(x){let inputs={x:convertToTensor(x,"x","sin")};return ENGINE.runKernel(Sin,inputs)}var sin=op({sin_});function sinh_(x){let inputs={x:convertToTensor(x,"x","sinh")};return ENGINE.runKernel(Sinh,inputs)}var sinh=op({sinh_});function slice1d_(x,begin,size){let $x=convertToTensor(x,"x","slice1d");return assert($x.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var truncatedNormal=op({truncatedNormal_});function unique_(x,axis=0){let $x=convertToTensor(x,"x","unique","string_or_numeric");assert($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i<condVals.length;i++)condVals[i]&&indices.push(i);let inBuffer=buffer(condShape,"int32"),out=buffer([indices.length,condShape.length],"int32");for(let i=0;i<indices.length;i++){let loc=inBuffer.indexToLoc(indices[i]),offset=i*condShape.length;out.values.set(loc,offset)}return out.toTensor()}async function whereAsync_(condition){let $condition=convertToTensor(condition,"condition","whereAsync","bool"),vals=await $condition.data(),res=whereImpl($condition.shape,vals);return condition!==$condition&&$condition.dispose(),res}var whereAsync=whereAsync_;async function booleanMaskAsync_(tensor2,mask,axis){let $tensor=convertToTensor(tensor2,"tensor","boolMask"),$mask=convertToTensor(mask,"mask","boolMask","bool"),axisFrom=axis==null?0:axis,maskDim=$mask.rank,tensorShape=$tensor.shape;assert(maskDim>0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i<axisFrom+maskDim;i++)leadingSize*=tensorShape[i];let targetTensorShape=tensorShape.slice(0,axisFrom).concat([leadingSize],tensorShape.slice(axisFrom+maskDim)),reshapedTensor=reshape($tensor,targetTensorShape),reshapedMask=reshape($mask,[-1]),positivePositions=await whereAsync(reshapedMask),indices=squeeze(positivePositions,[1]),res=gather(reshapedTensor,indices,axisFrom);return tensor2!==$tensor&&$tensor.dispose(),mask!==$mask&&$mask.dispose(),indices.dispose(),reshapedTensor.dispose(),reshapedMask.dispose(),positivePositions.dispose(),res}var booleanMaskAsync=booleanMaskAsync_;function notEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","notEqualStrict"),$b=convertToTensor(b,"b","notEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual($a,$b)}function lessStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessStrict"),$b=convertToTensor(b,"b","lessStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessStrict: "),less($a,$b)}function equalStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","equalStrict"),$b=convertToTensor(b,"b","equalStrict");return assertShapesMatch($a.shape,$b.shape,"Error in equalStrict: "),equal($a,$b)}function lessEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessEqualStrict"),$b=convertToTensor(b,"b","lessEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual($a,$b)}function greaterStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterStrict"),$b=convertToTensor(b,"b","greaterStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterStrict: "),greater($a,$b)}function greaterEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterEqualStrict"),$b=convertToTensor(b,"b","greaterEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual($a,$b)}var equalStrict=op({equalStrict_}),greaterEqualStrict=op({greaterEqualStrict_}),greaterStrict=op({greaterStrict_}),lessEqualStrict=op({lessEqualStrict_}),lessStrict=op({lessStrict_}),notEqualStrict=op({notEqualStrict_});function addStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","addStrict"),$b=convertToTensor(b,"b","addStrict");return assertShapesMatch($a.shape,$b.shape,"Error in addStrict: "),add2($a,$b)}function subStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","subStrict"),$b=convertToTensor(b,"b","subStrict");return assertShapesMatch($a.shape,$b.shape,"Error in subStrict: "),sub($a,$b)}function powStrict_(base2,exp4){return deprecationWarn("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch(base2.shape,exp4.shape,"Error in powStrict: "),pow(base2,exp4)}function mulStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");return assertShapesMatch($a.shape,$b.shape,"Error in multiplyStrict: "),mul($a,$b)}function divStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");return assertShapesMatch($a.shape,$b.shape,"Error in divideStrict: "),div($a,$b)}function modStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","modStrict"),$b=convertToTensor(b,"b","modStrict");return assertShapesMatch($a.shape,$b.shape,"Error in modStrict: "),mod($a,$b)}function minimumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","minimumStrict"),$b=convertToTensor(b,"b","minimumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in minimumStrict: "),minimum($a,$b)}function maximumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","maximumStrict"),$b=convertToTensor(b,"b","maximumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in maximumStrict: "),maximum($a,$b)}function squaredDifferenceStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","squaredDifferenceStrict"),$b=convertToTensor(b,"b","squaredDifferenceStrict");return assertShapesMatch($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference($a,$b)}var addStrict=op({addStrict_}),divStrict=op({divStrict_}),maximumStrict=op({maximumStrict_}),minimumStrict=op({minimumStrict_}),modStrict=op({modStrict_}),mulStrict=op({mulStrict_}),powStrict=op({powStrict_}),squaredDifferenceStrict=op({squaredDifferenceStrict_}),subStrict=op({subStrict_});function norm_(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor(x,"x","norm");let norm2=normImpl(x,ord,axis),keepDimsShape=norm2.shape;if(keepDims){let axes=parseAxisParam(axis,x.shape);keepDimsShape=expandShapeToKeepDim(norm2.shape,axes)}return reshape(norm2,keepDimsShape)}function normImpl(x,p2,axis=null){if(x.rank===0)return abs(x);if(x.rank!==1&&axis===null)return normImpl(reshape(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum2(abs(x),axis);if(p2===Infinity)return max(abs(x),axis);if(p2===-Infinity)return min(abs(x),axis);if(p2==="euclidean"||p2===2)return sqrt(sum2(pow(abs(x),scalar(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max(sum2(abs(x),axis[0]),axis[1]-1);if(p2===Infinity)return max(sum2(abs(x),axis[1]),axis[0]);if(p2===-Infinity)return min(sum2(abs(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt(sum2(square(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm=op({norm_});function movingAverage_(v,x,decay,step4,zeroDebias=!0){let $v=convertToTensor(v,"v","movingAverage"),$x=convertToTensor(x,"x","movingAverage"),$decay=convertToTensor(decay,"decay","movingAverage");assertTypesMatch($v,$x),assert(arraysEqual($v.shape,$x.shape),()=>"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i<x.shape.length;i++)noiseShape[i]==null&&x.shape[i]!=null?newDimension.push(x.shape[i]):newDimension.push(noiseShape[i]);return newDimension}return noiseShape}function dropout_(x,rate,noiseShape,seed){let $x=convertToTensor(x,"x","dropout");if(assert($x.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d(newValues,"float32")}async function inTopKAsync_(predictions,targets,k=1){let $predictions=convertToTensor(predictions,"predictions","inTopK"),$targets=convertToTensor(targets,"targets","inTopK");assert($predictions.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;b<batch;b++){let offset=b*size,vals=predictionsVals.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value),precision3[b]=0;for(let i=0;i<k;i++)if(valAndInd[i].index===targetsVals[b]){precision3[b]=1;break}}return predictions!==$predictions&&$predictions.dispose(),targets!==$targets&&$targets.dispose(),tensor(precision3,$targets.shape,"bool")}var inTopKAsync=inTopKAsync_,fused_ops_exports={};__export2(fused_ops_exports,{conv2d:()=>conv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal2.size;){let padLen=start+frameLength-signal2.size,pad3=concat([slice(signal2,start,frameLength-padLen),fill([padLen],padValue)]);output.push(pad3),start+=frameStep}return output.length===0?tensor2d([],[0,frameLength]):reshape(concat(output),[output.length,frameLength])}var frame=op({frame_});function stft_(signal2,frameLength,frameStep,fftLength,windowFn=hannWindow){fftLength==null&&(fftLength=enclosingPowerOfTwo(frameLength));let framedSignal=frame(signal2,frameLength,frameStep),windowedSignal=mul(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft(slice(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat(output)}var stft=op({stft_});function cropAndResize_(image3,boxes,boxInd,cropSize,method="bilinear",extrapolationValue=0){let $image=convertToTensor(image3,"image","cropAndResize"),$boxes=convertToTensor(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor(boxInd,"boxInd","cropAndResize","int32"),numBoxes=$boxes.shape[0];assert($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a<b?-1:0}function binarySearch_(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale22=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou2=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou2>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale22,iou2),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale22,iou2){let weight=Math.exp(scale22*iou2*iou2);return iou2<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1==0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1==0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul(sum2(mul(ys[j],x)),ys[j]);x=sub(x,proj)}return div(x,norm(x,"euclidean"))}));return inputIsTensor2D?stack(ys,0):ys}var gramSchmidt=op({gramSchmidt_});function qr_(x,fullMatrices=!1){if(assert(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE.tidy(()=>{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon32=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon32),l13=neg(mul($labels,log(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon32=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon32,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.rho,config.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config){return new cls(config.learningRate,config.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon,config.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config){return new cls(config.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config){return new cls(config.learningRate,config.momentum,config.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon32,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config){return new cls(config.learningRate,config.decay,config.momentum,config.epsilon,config.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon32,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon32=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon32)}static adadelta(learningRate=.001,rho=.95,epsilon32=null){return new AdadeltaOptimizer(learningRate,rho,epsilon32)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon32=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon32,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log2,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(inSize){return inSize<=PARALLELIZE_THRESHOLD?inSize:nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)))}function getImageCenter(center,imageHeight,imageWidth){let centerX=imageWidth*(typeof center=="number"?center:center[0]),centerY=imageHeight*(typeof center=="number"?center:center[1]);return[centerX,centerY]}function getReshaped(inputShape,blockShape,prod5,batchToSpace=!0){let reshaped=[];if(batchToSpace)reshaped=reshaped.concat(blockShape.slice(0)),reshaped.push(inputShape[0]/prod5),reshaped=reshaped.concat(inputShape.slice(1));else{reshaped=reshaped.concat(inputShape[0]);let spatialLength=blockShape.length;for(let i=0;i<spatialLength;++i)reshaped=reshaped.concat([inputShape[i+1]/blockShape[i],blockShape[i]]);reshaped=reshaped.concat(inputShape.slice(spatialLength+1))}return reshaped}function getPermuted(reshapedRank,blockShapeRank,batchToSpace=!0){let permuted=[];if(batchToSpace){permuted.push(blockShapeRank);for(let i=blockShapeRank+1;i<reshapedRank;++i)i<=2*blockShapeRank?(permuted.push(i),permuted.push(i-(blockShapeRank+1))):permuted.push(i)}else{let permutedBeforeBatch=[],permutedAfterBatch=[];for(let i=1;i<reshapedRank;++i)i>=blockShapeRank*2+1||i%2==1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i<inputShape.length;++i)i<=blockShape.length?batchToSpace?reshapedPermuted.push(blockShape[i-1]*inputShape[i]):reshapedPermuted.push(inputShape[i]/blockShape[i-1]):reshapedPermuted.push(inputShape[i]);return reshapedPermuted}function getSliceBeginCoords(crops,blockShape){let sliceBeginCoords=[0];for(let i=0;i<blockShape;++i)sliceBeginCoords.push(crops[i][0]);return sliceBeginCoords}function getSliceSize(uncroppedShape,crops,blockShape){let sliceSize=uncroppedShape.slice(0,1);for(let i=0;i<blockShape;++i)sliceSize.push(uncroppedShape[i+1]-crops[i][0]-crops[i][1]);return sliceSize}var SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429;function warn(...msg){env().getBool("IS_TEST")||console.warn(...msg)}function log2(...msg){env().getBool("IS_TEST")||console.log(...msg)}function mergeRealAndImagArrays(real4,imag4){if(real4.length!==imag4.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`);let result=new Float32Array(real4.length*2);for(let i=0;i<result.length;i+=2)result[i]=real4[i/2],result[i+1]=imag4[i/2];return result}function splitRealAndImagArrays(complex4){let real4=new Float32Array(complex4.length/2),imag4=new Float32Array(complex4.length/2);for(let i=0;i<complex4.length;i+=2)real4[i/2]=complex4[i],imag4[i/2]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithEvenIndex(complex4){let len=Math.ceil(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=0;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithOddIndex(complex4){let len=Math.floor(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=2;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function getComplexWithIndex(complex4,index){let real4=complex4[index*2],imag4=complex4[index*2+1];return{real:real4,imag:imag4}}function assignToTypedArray(data,real4,imag4,index){data[index*2]=real4,data[index*2+1]=imag4}function exponents(n,inverse){let real4=new Float32Array(n/2),imag4=new Float32Array(n/2);for(let i=0;i<Math.ceil(n/2);i++){let x=(inverse?2:-2)*Math.PI*(i/n);real4[i]=Math.cos(x),imag4[i]=Math.sin(x)}return{real:real4,imag:imag4}}function exponent(k,n,inverse){let x=(inverse?2:-2)*Math.PI*(k/n),real4=Math.cos(x),imag4=Math.sin(x);return{real:real4,imag:imag4}}function prepareSplitSize(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert(x.shape[axis]%numOrSizeSplits==0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)dim!==axis?outShape.push(aShape[dim]):outShape.push(numSegments);return outShape}function collectGatherOpShapeInfo(x,indices,axis,batchDims){let indicesRank=indices.shape.length,xRank=x.shape.length;if(batchDims!==0&&(batchDims<-indicesRank||batchDims>indicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
     ${xRank}).`);if(axis<batchDims)throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);for(let i=0;i<batchDims;++i)if(x.shape[i]!==indices.shape[i])throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`);let dimSize=x.shape[axis],outputShape=[],batchSize=1,outerSize=1,sliceSize=1;for(let i=0;i<batchDims;++i)outputShape.push(x.shape[i]),batchSize*=x.shape[i];for(let i=batchDims;i<axis;i++)outputShape.push(x.shape[i]),outerSize*=x.shape[i];for(let i=batchDims;i<indicesRank;i++)outputShape.push(indices.shape[i]);for(let i=axis+1;i<xRank;i++)outputShape.push(x.shape[i]),sliceSize*=x.shape[i];return{batchSize,sliceSize,outerSize,dimSize,outputShape}}function castTensor(x,dtype,backend2){if(dtype==="complex64"){if(x.dtype==="complex64")return x.clone();let zerosTensor=zeros(x.shape),floatX=cast(x,"float32"),result=backend2.complex(floatX,zerosTensor);return zerosTensor.dispose(),floatX.dispose(),result}if(!hasEncodingLoss(x.dtype,dtype))return ENGINE.makeTensorFromDataId(x.dataId,x.shape,dtype);if(x.dtype==="complex64"){let real4=backend2.real(x),result=cast(real4,dtype);return real4.dispose(),result}if(dtype==="int32")return backend2.int(x);if(dtype==="bool"){let zero=scalar(0,x.dtype),result=backend2.notEqual(x,zero);return zero.dispose(),result}else throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}function reshapeTensor(x,shape){return ENGINE.makeTensorFromDataId(x.dataId,shape,x.dtype)}function fromUint8ToStringArray(vals){try{return vals.map(val=>decodeString(val))}catch(err){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`)}}function fromStringArrayToUint8(strings){return strings.map(s=>encodeString(s))}var kernel_impls_exports={};__export2(kernel_impls_exports,{nonMaxSuppressionV3Impl:()=>nonMaxSuppressionV3Impl,nonMaxSuppressionV4Impl:()=>nonMaxSuppressionV4Impl,nonMaxSuppressionV5Impl:()=>nonMaxSuppressionV5Impl,whereImpl:()=>whereImpl});var absGradConfig={kernelName:Abs,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,step(cast(x,"float32"),-1))}}},acosGradConfig={kernelName:Acos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square(cast(x,"float32")),b=sqrt(sub(scalar(1),a));return neg(div(dy,b))}}}},acoshGradConfig={kernelName:Acosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(sub(square(cast(x,"float32")),1));return div(dy,a)}}}},addGradConfig={kernelName:Add,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}},addNGradConfig={kernelName:AddN,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}},argMaxGradConfig={kernelName:ArgMax,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},argMinGradConfig={kernelName:ArgMin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},asinGradConfig={kernelName:Asin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sqrt(sub(scalar(1),square(cast(x,"float32")))))}}},asinhGradConfig={kernelName:Asinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(add2(scalar(1),square(cast(x,"float32"))));return div(dy,a)}}}},atan2GradConfig={kernelName:Atan2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let d=add2(square(a),square(b)),res=mul(dy,div(b,d)),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let d=add2(square(a),square(b)),res=neg(mul(dy,div(a,d))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}},atanGradConfig={kernelName:Atan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(square(cast(x,"float32")),1))}}},atanhGradConfig={kernelName:Atanh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sub(scalar(1),square(cast(x,"float32"))))}}};function avgPool3dGrad_(dy,input2,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","avgPool3dGrad"),$input=convertToTensor(input2,"input","avgPool3dGrad"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert(dy5D.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dGrad=op({avgPool3dGrad_}),avgPool3DGradConfig={kernelName:AvgPool3D,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dGrad(dy,x,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function avgPoolGrad_(dy,input2,filterSize,strides,pad3){let $dy=convertToTensor(dy,"dy","avgPoolGrad"),$input=convertToTensor(input2,"input","avgPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert(dy4D.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`),assert(input4D.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);let inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad3},res=ENGINE.runKernel(AvgPoolGrad,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolGrad=op({avgPoolGrad_}),avgPoolGradConfig={kernelName:AvgPool,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>avgPoolGrad(dy,x,filterSize,strides,pad3)}}},batchMatMulGradConfig={kernelName:BatchMatMul,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul(dy,b,!1,!0),b:()=>matMul(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul(dy,b,!1,!1),b:()=>matMul(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul(b,dy,!1,!0),b:()=>matMul(a,dy,!1,!1)}:{a:()=>matMul(b,dy,!0,!0),b:()=>matMul(dy,a,!0,!0)}}},batchToSpaceNDGradConfig={kernelName:BatchToSpaceND,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND(dy,blockShape,crops)}}},broadcastToGradConfig={kernelName:BroadcastTo,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i<reps.length;i++)reps[i]>1&&axes.push(i);return{x:()=>sum2(dy,axes,!0)}}},castGradConfig={kernelName:Cast,gradFunc:dy=>({x:()=>dy.clone()})},ceilGradConfig={kernelName:Ceil,gradFunc:dy=>({x:()=>zerosLike(dy)})},clipByValueGradConfig={kernelName:ClipByValue,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where(logicalAnd(greaterEqual(x,clipValueMin),lessEqual(x,clipValueMax)),dy,zerosLike(dy))}}},complexAbsGradConfig={kernelName:ComplexAbs,inputsToSave:["x"],gradFunc:absGradConfig.gradFunc},concatGradConfig={kernelName:Concat,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]);return split(dy,sizeSplits,$axis).map(t=>()=>t)}},conv2DGradConfig={kernelName:Conv2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad3,dataFormat}=attrs;return assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput(x4D.shape,dy,$filter,strides,pad3,dataFormat),filter:()=>conv2DBackpropFilter(x4D,dy,$filter.shape,strides,pad3,dataFormat)}}},conv2DBackpropInputGradConfig={kernelName:Conv2DBackpropInput,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d(ddx,filter,strides,pad3,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter(ddx,dy,filter.shape,strides,pad3,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_(x,dy,filterShape,strides,pad3){let x5D=x;x.rank===4&&(x5D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad3,filterShape};return ENGINE.runKernel(Conv3DBackpropFilterV2,inputs,attrs)}var conv3DBackpropFilter=op({conv3DBackpropFilter_}),conv3DGradConfig={kernelName:Conv3D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3}=attrs;assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput(x5D.shape,dy,$filter,strides,pad3),filter:()=>conv3DBackpropFilter(x5D,dy,$filter.shape,strides,pad3)}}},cosGradConfig={kernelName:Cos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(neg(sin(cast(x,"float32"))),dy)}}},coshGradConfig={kernelName:Cosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(sinh(cast(x,"float32")),dy)}}},cumsumGradConfig={kernelName:Cumsum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse5}=attrs;return{x:()=>{let permutation=getAxesPermutation([axis],x.rank),out=cumsum(dy,axis,exclusive,!reverse5);return permutation!=null&&(out=transpose(out,permutation)),out}}}},depthwiseConv2dNativeGradConfig={kernelName:DepthwiseConv2dNative,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert(tupleValuesAreOne($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),{x:()=>depthwiseConv2dNativeBackpropInput(x.shape,dy,filter,strides,pad3,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter(x,dy,filter.shape,strides,pad3,dilations,dimRoundingMode)}}},dilation2dGradConfig={kernelName:Dilation2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE.runKernel(Dilation2DBackpropInput,inputInputs,attrs),filter:()=>ENGINE.runKernel(Dilation2DBackpropFilter,filterInputs,attrs)}}},eluGradConfig={kernelName:Elu,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,inputs={dy,y};return{x:()=>ENGINE.runKernel(EluGrad,inputs)}}},erfGradConfig={kernelName:Erf,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul(exp(neg(square(x))),2/Math.sqrt(Math.PI));return{x:()=>mul(dy,a)}}},expGradConfig={kernelName:Exp,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,y)}}},expandDimsGradConfig={kernelName:ExpandDims,inputsToSave:["input"],gradFunc:(dy,saved)=>{let[input2]=saved;return{input:()=>reshape(dy,input2.shape)}}},expm1GradConfig={kernelName:Expm1,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,exp(x))}}},floorGradConfig={kernelName:Floor,gradFunc:dy=>({x:()=>zerosLike(dy)})},floorDivGradConfig={kernelName:FloorDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))}}}},fusedBatchNormGradConfig={kernelName:FusedBatchNorm,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean4,variance,scale22]=saved,scaleValue=scale22==null?scalar(1):scale22,reductionAxes=getReductionAxes(mean4.shape,x.shape),tileShape=[];if(mean4.rank===1){for(let i=0;i<x.shape.length-1;++i)tileShape.push(x.shape[i]);tileShape.push(1)}let xMinusMean=sub(x,mean4),dyTimesScaleValue=mul(dy,scaleValue),oneOverSqrtVariance=rsqrt(add2(variance,scalar(varianceEpsilon))),minusHalfRCube=mul(mul(mul(oneOverSqrtVariance,oneOverSqrtVariance),oneOverSqrtVariance),scalar(-.5));return{x:()=>mean4.rank===1?reshape(mul(mul(dy,tile(reshape(oneOverSqrtVariance,[1,1,1,mean4.shape[0]]),tileShape)),scaleValue),x.shape):reshape(mul(mul(dy,oneOverSqrtVariance),scaleValue),x.shape),mean:()=>{let meanDer=mul(mul(oneOverSqrtVariance,scalar(-1)),dyTimesScaleValue);return mean4.rank===1&&(meanDer=sum2(meanDer,reductionAxes)),reshape(meanDer,mean4.shape)},variance:()=>{let varianceDer=mul(mul(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean4.rank===1&&(varianceDer=sum2(varianceDer,reductionAxes)),reshape(varianceDer,mean4.shape)},scale:()=>{let xMinusMean2TimesRsqrt=mul(xMinusMean,oneOverSqrtVariance),scaleDer=mul(dy,xMinusMean2TimesRsqrt);return mean4.rank===1&&(scaleDer=sum2(scaleDer,reductionAxes)),reshape(scaleDer,mean4.shape)},offset:()=>{let offsetDer=dy;return mean4.rank===1&&(offsetDer=sum2(offsetDer,reductionAxes)),reshape(offsetDer,mean4.shape)}}}},gatherGradConfig={kernelName:GatherV2,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam(axis,x.shape)[0];return{x:()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange(0,outerDims),innerAxesIndices=arrayRange(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat([outerShape,[indicesSize],innerShape]),values=reshape(dy,valuesShape),reshapedIndices=reshape(indices,[indicesSize]),transposeDims=arrayConcat([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose(values,transposeDims),paramsGrad=unsortedSegmentSum(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation(transposeDims);return paramsGrad=transpose(paramsGrad,invertTransposeDims),paramsGrad},indices:()=>indices}}};function arrayRange(start,stop){let result=[];for(let i=start;i<stop;++i)result.push(i);return result}function arrayConcat(arrays){let result=[];for(let i=0;i<arrays.length;++i)for(let j=0;j<arrays[i].length;++j)result.push(arrays[i][j]);return result}var greaterEqualGradConfig={kernelName:GreaterEqual,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>zerosLike(a),b:()=>zerosLike(b)}}},identityGradConfig={kernelName:Identity,gradFunc:dy=>({x:()=>cast(dy,"float32")})},isFiniteGradConfig={kernelName:IsFinite,gradFunc:dy=>({x:()=>zerosLike(dy)})},isInfGradConfig={kernelName:IsInf,gradFunc:dy=>({x:()=>zerosLike(dy)})},isNanGradConfig={kernelName:IsNan,gradFunc:dy=>({x:()=>zerosLike(dy)})},leakyReluGradConfig={kernelName:LeakyRelu,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{alpha}=attrs,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha))}}},log1pGradConfig={kernelName:Log1p,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(x,1))}}},logGradConfig={kernelName:Log,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,cast(x,"float32"))}}},logSoftmaxGradConfig={kernelName:LogSoftmax,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax6=exp(value);return sub(dy,mul(sum2(dy,axis,keepDims),softmax6))}}}};function localResponseNormalizationBackprop_(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE.runKernel(LRNGrad,inputs,attrs)}var localResponseNormalizationBackprop=op({localResponseNormalizationBackprop_}),lrnGradConfig={kernelName:LRN,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax(dy,y,xOrig,origAxes){return y.rank<xOrig.rank&&(y=reshape(y,expandShapeToKeepDim(y.shape,origAxes))),dy.rank<xOrig.rank&&(dy=reshape(dy,expandShapeToKeepDim(dy.shape,origAxes))),{x:()=>mul(dy,cast(equal(xOrig,y),dy.dtype))}}var maxGradConfig={kernelName:Max,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam(reductionIndices,x.shape),maxGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}},maximumGradConfig={kernelName:Maximum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(greaterEqual(a,b),"float32")),b:()=>mul(dy,cast(less(a,b),"float32"))}}};function maxPool3dGrad_(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPool3dGrad"),$input=convertToTensor(input2,"input","maxPool3dGrad"),$output=convertToTensor(output,"output","maxPool3dGrad"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert(dy5D.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(output5D.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dGrad=op({maxPool3dGrad_}),maxPool3DGradConfig={kernelName:MaxPool3D,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dGrad(dy,x,y,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function maxPoolGrad_(dy,input2,output,filterSize,strides,pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPoolGrad"),$input=convertToTensor(input2,"input","maxPoolGrad"),$output=convertToTensor(output,"output","maxPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert($dy.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`),assert($input.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad3,dimRoundingMode};return ENGINE.runKernel(MaxPoolGrad,inputs,attrs)}var maxPoolGrad=op({maxPoolGrad_}),maxPoolGradConfig={kernelName:MaxPool,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>maxPoolGrad(dy,x,y,filterSize,strides,pad3)}}},meanGradConfig={kernelName:Mean,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis}=attrs,axes=parseAxisParam(axis,x.shape),reduceShape=computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=sizeFromShape(reduceShape);return{x:()=>{let expandedDyShape=x.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape);return div(mul(expandedDy,ones2(x.shape,"float32")),reduceSize)}}}},minGradConfig={kernelName:Min,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam(axis,x.shape),minGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>minGrad.x()}}},minimumGradConfig={kernelName:Minimum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(lessEqual(a,b),"float32")),b:()=>mul(dy,cast(greater(a,b),"float32"))}}},mirrorPadGradConfig={kernelName:MirrorPad,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},modGradConfig={kernelName:Mod,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(dy,reduceAxes),a.shape):dy},b:()=>{let res=mul(dy,neg(floor(div(a,b)))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}},multiplyGradConfig={kernelName:Multiply,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=mul(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}},negGradConfig={kernelName:Neg,gradFunc:dy=>({x:()=>neg(dy)})},oneHotGradConfig={kernelName:OneHot,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros(indices.shape,"float32")}}},onesLikeGradConfig={kernelName:OnesLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},packGradConfig={kernelName:Pack,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return 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mask=greater(x,scalar(0)),scaleAlpha2=scalar(SELU_SCALEALPHA),scale22=scalar(SELU_SCALE),greaterThanZeroDer=mul(dy,scale22),lessEqualZeroDer=mul(mul(dy,scaleAlpha2),exp(cast(x,"float32")));return where(mask,greaterThanZeroDer,lessEqualZeroDer)}}}},sigmoidGradConfig={kernelName:Sigmoid,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,mul(y,sub(scalar(1),y)))}}},signGradConfig={kernelName:Sign,gradFunc:dy=>({x:()=>zerosLike(dy)})},sinGradConfig={kernelName:Sin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cos(cast(x,"float32")),dy)}}},sinhGradConfig={kernelName:Sinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cosh(cast(x,"float32")),dy)}}},sliceGradConfig={kernelName:Slice,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams(x,begin,size),paddings=[];for(let 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res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(neg(res),b.shape)}}}},sumGradConfig={kernelName:Sum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs;parseAxisParam(axis,x.shape).forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=mul(expandedDy,ones2(x.shape,"float32"));return{x:()=>derX}}},tanGradConfig={kernelName:Tan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,square(cos(x)))}}},tanhGradConfig={kernelName:Tanh,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(sub(scalar(1),square(y)),dy)}}},tileGradConfig={kernelName:Tile,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs;return{x:()=>{let 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transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation(perm);return{x:()=>transpose(dy,undoPerm)}}},unpackGradConfig={kernelName:Unpack,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack(dy,axis)}}},unsortedSegmentSumGradConfig={kernelName:UnsortedSegmentSum,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved;return{x:()=>gatherDropNegatives(dy,segmentIds)}}};function gatherDropNegatives(x,indices){let zeroClippedIndices=maximum(indices,zerosLike(indices)),gathered=gather(x,zeroClippedIndices),isPositive=greaterEqual(indices,scalar(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i<numIters;++i)isPositive=expandDims(isPositive,i+1);isPositive=logicalAnd(isPositive,ones2(gathered.shape,"bool"));let zeroSlice=zerosLike(gathered);return where(isPositive,gathered,zeroSlice)}var zerosLikeGradConfig={kernelName:ZerosLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},gradConfigs=[absGradConfig,acosGradConfig,acoshGradConfig,addGradConfig,addNGradConfig,argMaxGradConfig,argMinGradConfig,asinGradConfig,asinhGradConfig,atan2GradConfig,atanGradConfig,atanhGradConfig,avgPool3DGradConfig,avgPoolGradConfig,batchMatMulGradConfig,batchToSpaceNDGradConfig,broadcastToGradConfig,castGradConfig,ceilGradConfig,clipByValueGradConfig,complexAbsGradConfig,concatGradConfig,conv2DBackpropInputGradConfig,conv2DGradConfig,conv3DGradConfig,cosGradConfig,coshGradConfig,cumsumGradConfig,depthwiseConv2dNativeGradConfig,dilation2dGradConfig,divGradConfig,eluGradConfig,erfGradConfig,expGradConfig,expandDimsGradConfig,expm1GradConfig,floorDivGradConfig,floorGradConfig,fusedBatchNormGradConfig,gatherGradConfig,greaterEqualGradConfig,identityGradConfig,isFiniteGradConfig,isInfGradConfig,isNanGradConfig,leakyReluGradConfig,log1pGradConfig,logGradConfig,logSoftmaxGradConfig,lrnGradConfig,maxGradConfig,maxGradConfig,maximumGradConfig,maxPool3DGradConfig,maxPoolGradConfig,meanGradConfig,minGradConfig,minimumGradConfig,mirrorPadGradConfig,modGradConfig,multiplyGradConfig,negGradConfig,oneHotGradConfig,onesLikeGradConfig,packGradConfig,padV2GradConfig,padV2GradConfig,powGradConfig,preluGradConfig,reciprocalGradConfig,relu6GradConfig,reluGradConfig,reshapeGradConfig,resizeBilinearGradConfig,resizeNearestNeighborGradConfig,reverseGradConfig,roundGradConfig,rsqrtGradConfig,selectGradConfig,seluGradConfig,sigmoidGradConfig,signGradConfig,sinGradConfig,sinhGradConfig,sliceGradConfig,softmaxGradConfig,softplusGradConfig,spaceToBatchNDGradConfig,spaceToBatchNDGradConfig,splitVGradConfig,splitVGradConfig,sqrtGradConfig,squaredDifferenceGradConfig,squareGradConfig,stepGradConfig,subGradConfig,sumGradConfig,tanGradConfig,tanhGradConfig,tileGradConfig,transposeGradConfig,unpackGradConfig,unsortedSegmentSumGradConfig,zerosLikeGradConfig];for(let gradientConfig of gradConfigs)registerGradient(gradientConfig);Tensor.prototype.abs=function(){return this.throwIfDisposed(),abs(this)};Tensor.prototype.acos=function(){return this.throwIfDisposed(),acos(this)};Tensor.prototype.acosh=function(){return this.throwIfDisposed(),acosh(this)};Tensor.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict(this,x)};Tensor.prototype.add=function(b){return this.throwIfDisposed(),add2(this,b)};Tensor.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all(this,axis,keepDims)};Tensor.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any(this,axis,keepDims)};Tensor.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax(this,axis)};Tensor.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin(this,axis)};Tensor.prototype.asScalar=function(){return this.throwIfDisposed(),assert(this.size===1,()=>"The array must have only 1 element."),reshape(this,[])};Tensor.prototype.asType=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.as1D=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape(this,[rows,columns])};Tensor.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape(this,[rows,columns,depth])};Tensor.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2])};Tensor.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2,depth3])};Tensor.prototype.asin=function(){return this.throwIfDisposed(),asin(this)};Tensor.prototype.asinh=function(){return this.throwIfDisposed(),asinh(this)};Tensor.prototype.atan=function(){return this.throwIfDisposed(),atan(this)};Tensor.prototype.atan2=function(b){return this.throwIfDisposed(),atan2(this,b)};Tensor.prototype.atanh=function(){return this.throwIfDisposed(),atanh(this)};Tensor.prototype.avgPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),avgPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND(this,blockShape,crops)};Tensor.prototype.batchNorm=function(mean4,variance,offset,scale22,varianceEpsilon){return this.throwIfDisposed(),batchNorm(this,mean4,variance,offset,scale22,varianceEpsilon)};Tensor.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo(this,shape)};Tensor.prototype.cast=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.ceil=function(){return this.throwIfDisposed(),ceil(this)};Tensor.prototype.clipByValue=function(min6,max6){return this.throwIfDisposed(),clipByValue(this,min6,max6)};Tensor.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor&&(x=[x]),concat([this,...x],axis)};Tensor.prototype.conv1d=function(filter,stride,pad3,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d(this,filter,stride,pad3,dataFormat,dilation,dimRoundingMode)};Tensor.prototype.conv2dTranspose=function(filter,outputShape,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose(this,filter,outputShape,strides,pad3,dimRoundingMode)};Tensor.prototype.conv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.cos=function(){return this.throwIfDisposed(),cos(this)};Tensor.prototype.cosh=function(){return this.throwIfDisposed(),cosh(this)};Tensor.prototype.cumsum=function(axis,exclusive,reverse5){return this.throwIfDisposed(),cumsum(this,axis,exclusive,reverse5)};Tensor.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace(this,blockSize,dataFormat)};Tensor.prototype.depthwiseConv2D=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return deprecationWarn("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.depthwiseConv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.dilation2d=function(filter,strides,pad3,dilations,dataFormat){return this.throwIfDisposed(),dilation2d(this,filter,strides,pad3,dilations,dataFormat)};Tensor.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan(this,b)};Tensor.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict(this,x)};Tensor.prototype.div=function(b){return this.throwIfDisposed(),div(this,b)};Tensor.prototype.dot=function(b){return this.throwIfDisposed(),dot(this,b)};Tensor.prototype.elu=function(){return this.throwIfDisposed(),elu(this)};Tensor.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict(this,x)};Tensor.prototype.equal=function(b){return this.throwIfDisposed(),equal(this,b)};Tensor.prototype.erf=function(){return this.throwIfDisposed(),erf(this)};Tensor.prototype.exp=function(){return this.throwIfDisposed(),exp(this)};Tensor.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims(this,axis)};Tensor.prototype.expm1=function(){return this.throwIfDisposed(),expm1(this)};Tensor.prototype.fft=function(){return this.throwIfDisposed(),fft(this)};Tensor.prototype.flatten=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.floor=function(){return this.throwIfDisposed(),floor(this)};Tensor.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv(this,b)};Tensor.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather(this,indices,axis)};Tensor.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict(this,x)};Tensor.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual(this,b)};Tensor.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict(this,x)};Tensor.prototype.greater=function(b){return this.throwIfDisposed(),greater(this,b)};Tensor.prototype.ifft=function(){return this.throwIfDisposed(),ifft(this)};Tensor.prototype.irfft=function(){return this.throwIfDisposed(),irfft(this)};Tensor.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite2(this)};Tensor.prototype.isInf=function(){return this.throwIfDisposed(),isInf(this)};Tensor.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN2(this)};Tensor.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu(this,alpha)};Tensor.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict(this,x)};Tensor.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual(this,b)};Tensor.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict(this,x)};Tensor.prototype.less=function(b){return this.throwIfDisposed(),less(this,b)};Tensor.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization(this,depthRadius,bias,alpha,beta)};Tensor.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid(this)};Tensor.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax(this,axis)};Tensor.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp(this,axis,keepDims)};Tensor.prototype.log=function(){return this.throwIfDisposed(),log(this)};Tensor.prototype.log1p=function(){return this.throwIfDisposed(),log1p(this)};Tensor.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd(this,b)};Tensor.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot(this)};Tensor.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr(this,b)};Tensor.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor(this,b)};Tensor.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul(this,b,transposeA,transposeB)};Tensor.prototype.maxPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),maxPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max(this,axis,keepDims)};Tensor.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict(this,x)};Tensor.prototype.maximum=function(b){return this.throwIfDisposed(),maximum(this,b)};Tensor.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean(this,axis,keepDims)};Tensor.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min(this,axis,keepDims)};Tensor.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict(this,x)};Tensor.prototype.minimum=function(b){return this.throwIfDisposed(),minimum(this,b)};Tensor.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad(this,paddings,mode)};Tensor.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict(this,x)};Tensor.prototype.mod=function(b){return this.throwIfDisposed(),mod(this,b)};Tensor.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict(this,x)};Tensor.prototype.mul=function(b){return this.throwIfDisposed(),mul(this,b)};Tensor.prototype.neg=function(){return this.throwIfDisposed(),neg(this)};Tensor.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm(this,ord,axis,keepDims)};Tensor.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict(this,x)};Tensor.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual(this,b)};Tensor.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot(this,depth,onValue,offValue)};Tensor.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike(this)};Tensor.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad(this,paddings,constantValue)};Tensor.prototype.pool=function(windowShape,poolingType,padding,dilationRate,strides){return this.throwIfDisposed(),pool(this,windowShape,poolingType,padding,dilationRate,strides)};Tensor.prototype.powStrict=function(exp4){return this.throwIfDisposed(),powStrict(this,exp4)};Tensor.prototype.pow=function(exp4){return this.throwIfDisposed(),pow(this,exp4)};Tensor.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu(this,alpha)};Tensor.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod(this,axis,keepDims)};Tensor.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal(this)};Tensor.prototype.relu=function(){return this.throwIfDisposed(),relu(this)};Tensor.prototype.relu6=function(){return this.throwIfDisposed(),relu6(this)};Tensor.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape(this,x.shape)};Tensor.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape(this,shape)};Tensor.prototype.resizeBilinear=function(newShape2D,alignCorners,halfPixelCenters){return this.throwIfDisposed(),resizeBilinear(this,newShape2D,alignCorners,halfPixelCenters)};Tensor.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners,halfFloatCenters){return this.throwIfDisposed(),resizeNearestNeighbor(this,newShape2D,alignCorners,halfFloatCenters)};Tensor.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse(this,axis)};Tensor.prototype.rfft=function(){return this.throwIfDisposed(),rfft(this)};Tensor.prototype.round=function(){return this.throwIfDisposed(),round2(this)};Tensor.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt(this)};Tensor.prototype.selu=function(){return this.throwIfDisposed(),selu(this)};Tensor.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d(this,depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat)};Tensor.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid(this)};Tensor.prototype.sign=function(){return this.throwIfDisposed(),sign(this)};Tensor.prototype.sin=function(){return this.throwIfDisposed(),sin(this)};Tensor.prototype.sinh=function(){return this.throwIfDisposed(),sinh(this)};Tensor.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice(this,begin,size)};Tensor.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax(this,dim)};Tensor.prototype.softplus=function(){return this.throwIfDisposed(),softplus(this)};Tensor.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND(this,blockShape,paddings)};Tensor.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split(this,numOrSizeSplits,axis)};Tensor.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt(this)};Tensor.prototype.square=function(){return this.throwIfDisposed(),square(this)};Tensor.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference(this,b)};Tensor.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict(this,x)};Tensor.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze(this,axis)};Tensor.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor?[this,x]:[this,...x];return stack(tensorsToBeStacked,axis)};Tensor.prototype.step=function(alpha){return this.throwIfDisposed(),step(this,alpha)};Tensor.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict(this,x)};Tensor.prototype.sub=function(b){return this.throwIfDisposed(),sub(this,b)};Tensor.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum2(this,axis,keepDims)};Tensor.prototype.tan=function(){return this.throwIfDisposed(),tan(this)};Tensor.prototype.tanh=function(){return this.throwIfDisposed(),tanh2(this)};Tensor.prototype.tile=function(reps){return this.throwIfDisposed(),tile(this,reps)};Tensor.prototype.toBool=function(){return this.throwIfDisposed(),cast(this,"bool")};Tensor.prototype.toFloat=function(){return this.throwIfDisposed(),cast(this,"float32")};Tensor.prototype.toInt=function(){return this.throwIfDisposed(),cast(this,"int32")};Tensor.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk(this,k,sorted)};Tensor.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose(this,perm)};Tensor.prototype.unique=function(axis){return this.throwIfDisposed(),unique(this,axis)};Tensor.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum(this,segmentIds,numSegments)};Tensor.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack(this,axis)};Tensor.prototype.where=function(condition,x){return this.throwIfDisposed(),where(condition,this,x)};Tensor.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike(this)};var exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon(){return _epsilon==null&&(_epsilon=backend().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;i<numValues;i++)newArray=newArray.concat(value);return newArray}else{let newArray=new Array(numValues);return newArray.fill(value),newArray}}function assert2(val,message){if(!val)throw new AssertionError(message)}function count(array2,refernce){let counter=0;for(let item of array2)item===refernce&&counter++;return counter}function singletonOrArray(xs){return xs.length===1?xs[0]:xs}function toList(x){return Array.isArray(x)?x:[x]}function toSnakeCase(name){let insecure=name.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return insecure[0]!=="_"?insecure:"private"+insecure}function toCamelCase(identifier){return identifier.length<=1||identifier.indexOf("_")===-1?identifier:identifier.replace(/[_]+(\w|$)/g,(m,p1)=>p1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;let dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config){if(!(config==null||typeof config!="object"))if(Array.isArray(config))config.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config);for(let field of fields){let value=config[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
 2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config=identifier;if(config.className==null||config.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.
 'className' and 'config' must set.`);let className=config.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
-2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config.config);let returnObj=fromConfig(cls,config.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return a<b?-1:a>b?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique2(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now2=util_exports.now();return now2-lastTime<waitMs||(lastTime=now2,lastResult=f(...args)),lastResult}}function mapActivationToFusedKernel(activationName){return activationName==="relu"?"relu":activationName==="linear"?"linear":activationName==="elu"?"elu":null}function calcL2Norms(w,axis){return tidy(()=>sqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config){return new MinMaxNorm(config)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros2});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i<end;++i)prod5*=array2[i];return prod5}function toArray1D(array2){return array2=Array.isArray(array2)?new Float32Array(array2):array2,tensor1d(array2)}function min2(array2){return min(toArray1D(array2)).dataSync()[0]}function max2(array2){return max(toArray1D(array2)).dataSync()[0]}function range2(begin,end){if(end<begin)throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);let out=[];for(let i=begin;i<end;++i)out.push(i);return out}function cast2(x,dtype){return x.asType(dtype)}function expandDims2(x,axis=-1){let outShape=x.shape.slice();return axis<0&&(axis=outShape.length+axis+1),outShape.splice(axis,0,1),x.reshape(outShape)}function repeat(x,n){return tidy(()=>{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims2(x,1);return tile2(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile2(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square2(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu2(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale22=this.scale;if(this.mode==="fanIn"?scale22/=Math.max(1,fanIn):this.mode==="fanOut"?scale22/=Math.max(1,fanOut):scale22/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale22);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale22);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config={};return config.className=className,config.config={},deserializeInitializer(config)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros2(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity(args){return new Identity2(args)}function varianceScaling(config){return new VarianceScaling(config)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add3,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d3,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d4,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu3,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum2,minimum:()=>minimum2,multiply:()=>multiply,permute:()=>permute,prelu:()=>prelu2,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape2,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax2,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),!(this.inputSpec==null||this.inputSpec.length===0)){let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndex<inputs.length;inputIndex++){let x=inputs[inputIndex],spec=inputSpec[inputIndex];if(spec!=null){let ndim=x.rank;if(spec.ndim!=null&&ndim!==spec.ndim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);if(spec.maxNDim!=null&&ndim>spec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim<spec.minNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);if(spec.dtype!=null&&x.dtype!==spec.dtype)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);if(spec.axes){let xShape=x.shape;for(let key in spec.axes){let axis=Number(key),value=spec.axes[key],xShapeAtAxis=axis>=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i<spec.shape.length;++i){let specDim=spec.shape[i],dim=x.shape[i];if(specDim!=null&&dim!=null&&specDim!==dim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`)}}}}}call(inputs,kwargs){return inputs}invokeCallHook(inputs,kwargs){this._callHook!=null&&this._callHook(inputs,kwargs)}setCallHook(callHook){this._callHook=callHook}clearCallHook(){this._callHook=null}apply(inputs,kwargs){kwargs=kwargs||{},this.assertNotDisposed();let inputsList=toList(inputs),allAreSymbolic=!0;for(let input2 of inputsList)if(!(input2 instanceof SymbolicTensor)){allAreSymbolic=!1;break}let noneAreSymbolic=!0;for(let input2 of inputsList)if(input2 instanceof SymbolicTensor){noneAreSymbolic=!1;break}if(allAreSymbolic===noneAreSymbolic)throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return nameScope(this.name,()=>{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node2 of this.inboundNodes){let shapeString=JSON.stringify(node2.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length!==0){let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;i<paramValues.length;++i){let pv=paramValues[i],p2=params[i],w=weights[i];if(!util_exports.arraysEqual(pv.shape,w.shape))throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);weightValueTuples.push([p2,w])}batchSetValue(weightValueTuples)}})}addWeight(name,shape,dtype,initializer,regularizer,trainable,constraint){if(this._addedWeightNames.indexOf(name)!==-1)throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);this._addedWeightNames.push(name),dtype==null&&(dtype="float32"),this.fastWeightInitDuringBuild&&(initializer=getInitializer("zeros"));let initValue=initializer.apply(shape,dtype),weight=new LayerVariable(initValue,dtype,name,trainable,constraint);return initValue.dispose(),regularizer!=null&&this.addLoss(()=>regularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;i<outputTensors.length;i++)outputTensors[i].sourceLayer=this,outputTensors[i].nodeIndex=this.inboundNodes.length-1,outputTensors[i].tensorIndex=i}getConfig(){let config={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(config.batchInputShape=this.batchInputShape),this.dtype!=null&&(config.dtype=this.dtype),config}disposeWeights(){return this.weights.forEach(weight=>weight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount===0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node2=layer.inboundNodes[nodeIndex];if(node2.inboundLayers.length===0)return node2.inputTensors;{let sourceTensors=[];for(let i=0;i<node2.inboundLayers.length;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],previousSources=getSourceInputs(x,layer2,nodeIndex2);for(let x2 of previousSources)sourceTensors.indexOf(x2)===-1&&sourceTensors.push(x2)}return sourceTensors}}}var InputLayer=class extends Layer{constructor(args){super({dtype:args.dtype,name:args.name!=null?args.name:getUid("input").toString()});if(args.batchSize==null&&(args.batchSize=null),args.sparse==null&&(args.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=args.sparse,args.inputShape!=null&&args.batchInputShape!=null)throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let batchInputShape=args.batchInputShape;if(batchInputShape==null){if(args.inputShape==null)throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");batchInputShape=[args.batchSize].concat(args.inputShape)}else if(args.batchSize!=null)throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let dtype=args.dtype||"float32";this.batchInputShape=batchInputShape,this.dtype=dtype,this.inputSpec=[{shape:batchInputShape}];let inputTensor=new SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);inputTensor.nodeIndex=0,inputTensor.tensorIndex=0,new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[inputTensor],outputTensors:[inputTensor],inputMasks:[null],outputMasks:[null],inputShapes:[batchInputShape],outputShapes:[batchInputShape]})}apply(inputs,kwargs){throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};InputLayer.className="InputLayer";serialization_exports.registerClass(InputLayer);function Input(config){if(config.batchShape==null&&config.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(config.batchShape!=null&&config.shape!=null)throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let batchShape=config.batchShape;config.shape!=null&&batchShape==null&&(batchShape=[null].concat(config.shape));let dtype=config.dtype;return dtype==null&&(dtype="float32"),new InputLayer({batchInputShape:batchShape,name:config.name,dtype,sparse:config.sparse}).inboundNodes[0].outputTensors[0]}async function resolveScalarsInLogs(logs){if(logs!=null){let promises=[],keys=[],scalarsToDispose=[];for(let key in logs){let value=logs[key];if(typeof value!="number"){let valueScalar=value;promises.push(valueScalar.data()),keys.push(key),scalarsToDispose.push(valueScalar)}}if(promises.length>0){let values=await Promise.all(promises);for(let i=0;i<values.length;++i)logs[keys[i]]=values[i][0];dispose(scalarsToDispose)}}}function disposeTensorsInLogs(logs){if(logs!=null)for(let key in logs){let value=logs[key];typeof value!="number"&&value.dispose()}}var ModelLoggingVerbosity;(function(ModelLoggingVerbosity2){ModelLoggingVerbosity2[ModelLoggingVerbosity2.SILENT=0]="SILENT",ModelLoggingVerbosity2[ModelLoggingVerbosity2.VERBOSE=1]="VERBOSE"})(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));var DEFAULT_YIELD_EVERY_MS=125,BaseCallback=class{constructor(){this.validationData=null}setParams(params){this.params=params}async onEpochBegin(epoch,logs){}async onEpochEnd(epoch,logs){}async onBatchBegin(batch,logs){}async onBatchEnd(batch,logs){}async onTrainBegin(logs){}async onTrainEnd(logs){}setModel(model2){}},CallbackList=class{constructor(callbacks2,queueLength=10){callbacks2==null&&(callbacks2=[]),this.callbacks=callbacks2,this.queueLength=queueLength}append(callback){this.callbacks.push(callback)}setParams(params){for(let callback of this.callbacks)callback.setParams(params)}setModel(model2){for(let callback of this.callbacks)callback.setModel(model2)}async onEpochBegin(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochBegin(epoch,logs)}async onEpochEnd(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochEnd(epoch,logs)}async onBatchBegin(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchBegin(batch,logs)}async onBatchEnd(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchEnd(batch,logs)}async onTrainBegin(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainBegin(logs)}async onTrainEnd(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainEnd(logs)}},BaseLogger=class extends BaseCallback{constructor(){super()}async onEpochBegin(epoch){this.seen=0,this.totals={}}async onBatchEnd(batch,logs){logs==null&&(logs={});let batchSize=logs.size==null?0:logs.size;this.seen+=batchSize;for(let key in logs){let value=logs[key];if(typeof value=="number")this.totals.hasOwnProperty(key)||(this.totals[key]=0),this.totals[key]=this.totals[key]+value*batchSize;else{let oldTotalsToDispose;key in this.totals?oldTotalsToDispose=this.totals[key]:this.totals[key]=0;let total=tidy(()=>add2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log5=mul(div(1,this.seen),this.totals[key]);logs[key]=log5,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;i<valueArray.length;++i)if(typeof valueArray[i]!="number"){let valueScalar=valueArray[i];promises.push(valueScalar.data()),keys.push(key),indices.push(i)}}let values=await Promise.all(promises);for(let n=0;n<values.length;++n)this.history[keys[n]][indices[n]].dispose(),this.history[keys[n]][indices[n]]=values[n][0]}},CustomCallback=class extends BaseCallback{constructor(args,yieldEvery){super();if(this.currentEpoch=0,this.yieldEvery=yieldEvery||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=DEFAULT_YIELD_EVERY_MS),this.yieldEvery==="never"&&args.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");util_exports.isNumber(this.yieldEvery)&&(this.maybeWait=debounce(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=args.onTrainBegin,this.trainEnd=args.onTrainEnd,this.epochBegin=args.onEpochBegin,this.epochEnd=args.onEpochEnd,this.batchBegin=args.onBatchBegin,this.batchEnd=args.onBatchEnd,this.yield=args.onYield}async maybeWait(epoch,batch,logs){let ps=[];this.yield!=null&&(await resolveScalarsInLogs(logs),ps.push(this.yield(epoch,batch,logs))),ps.push(nextFrame()),await Promise.all(ps)}async onEpochBegin(epoch,logs){this.currentEpoch=epoch,this.epochBegin!=null&&(await resolveScalarsInLogs(logs),await this.epochBegin(epoch,logs))}async onEpochEnd(epoch,logs){let ps=[];this.epochEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.epochEnd(epoch,logs))),this.yieldEvery==="epoch"&&ps.push(nextFrame()),await Promise.all(ps)}async onBatchBegin(batch,logs){this.batchBegin!=null&&(await resolveScalarsInLogs(logs),await this.batchBegin(batch,logs))}async onBatchEnd(batch,logs){let ps=[];this.batchEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.batchEnd(batch,logs))),this.yieldEvery==="batch"?ps.push(nextFrame()):util_exports.isNumber(this.yieldEvery)&&ps.push(this.maybeWait(this.currentEpoch,batch,logs)),await Promise.all(ps)}async onTrainBegin(logs){this.trainBegin!=null&&(await resolveScalarsInLogs(logs),await this.trainBegin(logs))}async onTrainEnd(logs){this.trainEnd!=null&&(await resolveScalarsInLogs(logs),await this.trainEnd(logs))}};function standardizeCallbacks(callbacks2,yieldEvery){return callbacks2==null&&(callbacks2={}),callbacks2 instanceof BaseCallback?[callbacks2]:Array.isArray(callbacks2)&&callbacks2[0]instanceof BaseCallback?callbacks2:toList(callbacks2).map(callbackConfig=>new CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square2(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square2(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon(),Number.MAX_VALUE),firstLog=log(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon(),Number.MAX_VALUE),secondLog=log(add2(1,clippedTrue));return mean(square2(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square2(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log22=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log22);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon(),1-epsilon()),neg(sum2(mul(target.toFloat(),log(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon(),1-epsilon());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon(),1-epsilon()),y=log(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon(),1),clippedPred=clipByValue(yPred,epsilon(),1);return sum2(mul(yTrue,log(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log(add2(epsilon(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast2(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast2(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon()),Adam:()=>train.adam(.001,.9,.999,epsilon()),Adamax:()=>train.adamax(.002,.9,.999,epsilon(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys){if(typeof key!="string")return!1;if(!plainObjectCheck(x[key]))return!1}return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i<layers.length;++i)sequentialLike?printLayerSummary(layers[i],positions,printFn):printLayerSummaryWithConnections(layers[i],positions,relevantNodes,printFn),printFn((i===layers.length-1?"=":"_").repeat(lineLength));model2.checkTrainableWeightsConsistency();let trainableCount=countTrainableParams(model2),nonTrainableCount=countParamsInWeights(model2.nonTrainableWeights);printFn(`Total params: ${trainableCount+nonTrainableCount}`),printFn(`Trainable params: ${trainableCount}`),printFn(`Non-trainable params: ${nonTrainableCount}`),printFn("_".repeat(lineLength))}function countTrainableParams(model2){let trainableCount;return model2.collectedTrainableWeights!=null?trainableCount=countParamsInWeights(model2.collectedTrainableWeights):trainableCount=countParamsInWeights(model2.trainableWeights),trainableCount}function isModelSequentialLike(model2){let sequentialLike=!0,nodesByDepth=[],nodes=[];for(let depth in model2.nodesByDepth)nodesByDepth.push(model2.nodesByDepth[depth]);for(let depthNodes of nodesByDepth){if(depthNodes.length>1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node2 of layer.inboundNodes)if(nodes.indexOf(node2)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i<fields.length;++i)i>0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node2 of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node2)===-1))for(let i=0;i<node2.inboundLayers.length;++i){let inboundLayer=node2.inboundLayers[i].name,inboundLayerIndex=node2.nodeIndices[i],inboundTensorIndex=node2.tensorIndices[i];connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`)}let name=layer.name,className=layer.getClassName(),firstConnection=connections.length===0?"":connections[0],fields=[`${name} (${className})`,outputShape,layer.countParams().toString(),firstConnection];printRow(fields,positions,printFn);for(let i=1;i<connections.length;++i)printRow(["","","",connections[i]],positions,printFn)}function isArrayItemInputOrOutputName(key,index,value){return(key==="inboundNodes"||key==="outputLayers"||key==="inputLayers")&&index===0&&typeof value=="string"}function convertPythonicToTs(pythonicConfig,key){if(pythonicConfig===null)return null;if(typeof pythonicConfig=="string")return toCamelCase(pythonicConfig);if(typeof pythonicConfig=="number"||typeof pythonicConfig=="boolean")return pythonicConfig;if(pythonicConfig instanceof Array){let tsArray=[],arrayLength=pythonicConfig.length;for(let i=0;i<arrayLength;++i){let item=pythonicConfig[i];isArrayItemInputOrOutputName(key,i,item)?tsArray.push(item):tsArray.push(convertPythonicToTs(item,key))}return tsArray}else{let tsDict={};for(let pythonicKey of Object.keys(pythonicConfig)){let pythonicValue=pythonicConfig[pythonicKey];if(pythonicKey==="name"&&typeof pythonicValue=="string")tsDict[pythonicKey]=pythonicValue;else{let tsKey=toCamelCase(pythonicKey);tsDict[tsKey]=convertPythonicToTs(pythonicValue,tsKey)}}return tsDict}}function convertTsToPythonic(tsConfig,key){if(tsConfig==null)return null;if(typeof tsConfig=="string")return toSnakeCase(tsConfig);if(typeof tsConfig=="number"||typeof tsConfig=="boolean")return tsConfig;if(tsConfig instanceof Array){let pyArray=[],arrayLength=tsConfig.length;for(let i=0;i<arrayLength;++i){let item=tsConfig[i];isArrayItemInputOrOutputName(key,i,item)?pyArray.push(item):pyArray.push(convertTsToPythonic(item,key))}return pyArray}else{let pyDict={};for(let tsKey of Object.keys(tsConfig)){let tsValue=tsConfig[tsKey],pyKey=toSnakeCase(tsKey);(tsKey==="name"||tsKey==="className")&&typeof tsValue=="string"?pyDict[pyKey]=tsValue:pyDict[pyKey]=convertTsToPythonic(tsValue,tsKey)}return pyDict}}var version2="2.8.2";function assertFeedCompatibility(key,val){if(key.dtype==null||key.dtype===val.dtype)return val;try{return cast(val,key.dtype)}catch(err){throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`)}}var FeedDict=class{constructor(feeds){if(this.id2Value={},this.id2Mask={},this.name2Id={},feeds instanceof FeedDict)for(let id in feeds.id2Value)this.id2Value[id]=feeds.id2Value[id],id in feeds.id2Mask&&(this.id2Mask[id]=feeds.id2Mask[id]);else{if(feeds==null)return;for(let feed of feeds)this.add(feed.key,feed.value)}}add(key,value,mask){if(this.id2Value[key.id]==null)this.id2Value[key.id]=assertFeedCompatibility(key,value),this.name2Id[key.name]=key.id,mask!=null&&(this.id2Mask[key.id]=mask);else throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);return this}addFeed(feed){this.add(feed.key,feed.value)}hasKey(key){return this.id2Value[key.id]!=null}names(){return Object.keys(this.name2Id)}getValue(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Value[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Value[id]}}getMask(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Mask[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Mask[id]}}disposeMasks(){this.id2Mask!=null&&dispose(this.id2Mask)}},cachedSorted={},cachedRecipientCounts={};function execute(fetches,feedDict,kwargs,probe){let training=kwargs==null?!1:kwargs.training,arrayFetches=Array.isArray(fetches),fetchArray=arrayFetches?fetches:[fetches],outputNames=fetchArray.map(t=>t.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;i<sorted.length;++i){if(probe!=null){let numTensors=memory().numTensors;numTensors>probe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors<probe.minNumTensors&&(probe.minNumTensors=numTensors)}let symbolic=sorted[i],srcLayer=symbolic.sourceLayer;if(!(srcLayer instanceof InputLayer)){let inputValues=[],inputMasks=[],tensorsToDispose=[],maskExists=!1;for(let input2 of symbolic.inputs){let value=internalFeedDict.getValue(input2),mask=internalFeedDict.getMask(input2);inputValues.push(value),inputMasks.push(mask),mask!=null&&(maskExists=!0),training||(recipientCounts[input2.name]--,recipientCounts[input2.name]===0&&!feedDict.hasKey(input2)&&outputNames.indexOf(input2.name)===-1&&!value.isDisposed&&input2.sourceLayer.stateful!==!0&&tensorsToDispose.push(value))}maskExists&&(kwargs=kwargs||{},kwargs.mask=inputMasks[0]);let outputTensors=toList(srcLayer.apply(inputValues,kwargs)),outputMask=null;srcLayer.supportsMasking&&(outputMask=srcLayer.computeMask(inputValues,inputMasks));let layerOutputs=getNodeOutputs(symbolic),outputSymbolicTensors=Array.isArray(layerOutputs)?layerOutputs:[layerOutputs];for(let i2=0;i2<outputSymbolicTensors.length;++i2){internalFeedDict.hasKey(outputSymbolicTensors[i2])||internalFeedDict.add(outputSymbolicTensors[i2],outputTensors[i2],Array.isArray(outputMask)?outputMask[0]:outputMask);let index=outputNames.indexOf(outputSymbolicTensors[i2].name);index!==-1&&(finalOutputs[index]=outputTensors[i2])}training||dispose(tensorsToDispose)}}return internalFeedDict.disposeMasks(),arrayFetches?finalOutputs:finalOutputs[0]}function getTopologicalSortAndRecipientCounts(fetches,feedDict){util_exports.assert(fetches!=null&&fetches.length>0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)(recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name))&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;i<fetch3.sourceLayer.inboundNodes.length;++i)for(let outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors)if(outputTensor.id===fetch3.id){nodeIndex=i;break}layerOutputs=fetch3.sourceLayer.getOutputAt(nodeIndex)}return layerOutputs}var Container=class extends Layer{constructor(args){super({});if(this.containerNodes=new Set,this.name=args.name,this.name==null){let prefix=this.getClassName().toLowerCase();this.name=getUid(prefix)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(args.inputs)?this.inputs=args.inputs.slice():this.inputs=[args.inputs],Array.isArray(args.outputs)?this.outputs=args.outputs.slice():this.outputs=[args.outputs],unique2(this.inputs).length!==this.inputs.length)throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(x=>x.name)}`);unique2(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i];if(!(layer instanceof InputLayer))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);this.inputNames.push(layer.name),this.feedInputShapes.push(layer.batchInputShape),this.feedInputNames.push(layer.name)}for(let layer of this.outputLayers)this.outputNames.push(layer.name);this.internalInputShapes=this.inputs.map(x=>x.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node2=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node2)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node2)===-1){this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node2)===-1&&nodesInProgress2.push(node2);let numInboundLayers=node2.inboundLayers.length;for(let i=0;i<numInboundLayers;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],tensorIndex2=node2.tensorIndices[i];buildMapOfGraph(x,finishedNodes2,nodesInProgress2,layer2,nodeIndex2,tensorIndex2)}for(finishedNodes2.push(node2);nodesInProgress2.indexOf(node2)>=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node2),1);nodesInDecreasingDepth.push(node2)}},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node2 of reversedNodesInDecreasingDepth){nodeIDToNode[node2.id]=node2,node2.id in nodesDepths||(nodesDepths[node2.id]=0);let depth=nodesDepths[node2.id],previousDepth=layersDepths[node2.outboundLayer.id]==null?0:layersDepths[node2.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node2.outboundLayer.id]=depth,layerIDToLayer[node2.outboundLayer.id]=node2.outboundLayer,nodesDepths[node2.id]=depth;for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],inboundNode=inboundLayer.inboundNodes[nodeIndex],previousDepth2=nodesDepths[inboundNode.id]==null?0:nodesDepths[inboundNode.id];nodesDepths[inboundNode.id]=Math.max(depth+1,previousDepth2),nodeIDToNode[inboundNode.id]=inboundNode}}let nodesByDepth={};for(let nodeID in nodesDepths){let depth=nodesDepths[nodeID];depth in nodesByDepth||(nodesByDepth[depth]=[]),nodesByDepth[depth].push(nodeIDToNode[nodeID])}let layersByDepth={};for(let layerID in layersDepths){let depth=layersDepths[layerID];depth in layersByDepth||(layersByDepth[depth]=[]),layersByDepth[depth].push(layerIDToLayer[layerID])}let depthKeys=Object.keys(layersByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndex<bIndex?-1:aIndex>bIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node2 of nodesByDepth[depth]){let layer=node2.outboundLayer;if(layer!=null){for(let x of node2.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node2.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version2}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i]);return execute(this.outputs,feedDict,kwargs)})}computeMask(inputs,mask){return tidy(()=>{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;i<inputShapes.length;i++){let layer=this.inputLayers[i],inputShape2=inputShapes[i],shapeKey=layer.name+"_0_0";layersToOutputShapes[shapeKey]=inputShape2}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)===-1){let inputShapes2=[];for(let j=0;j<node2.inboundLayers.length;j++){let inboundLayer=node2.inboundLayers[j],nodeIndex2=node2.nodeIndices[j],tensorIndex=node2.tensorIndices[j],shapeKey=`${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`,inputShape2=layersToOutputShapes[shapeKey];inputShapes2.push(inputShape2)}let outputShape=layer.computeOutputShape(singletonOrArray(inputShapes2)),outputShapes2=normalizeShapeList(outputShape),nodeIndex=layer.inboundNodes.indexOf(node2);for(let j=0;j<outputShapes2.length;j++){let shapeKey=`${layer.name}_${nodeIndex}_${j}`;layersToOutputShapes[shapeKey]=outputShapes2[j]}}}}let outputShapes=[],outputShapeKeys=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],tensorIndex=this.outputLayersTensorIndices[i],shapeKey=`${layer.name}_${nodeIndex}_${tensorIndex}`;outputShapeKeys.push(shapeKey)}for(let i=0;i<outputShapeKeys.length;i++){let key=outputShapeKeys[i];assert2(key in layersToOutputShapes),outputShapes.push(layersToOutputShapes[key])}return singletonOrArray(outputShapes)}runInternalGraph(inputs,masks){masks==null&&(masks=pyListRepeat(null,inputs.length));let tensorMap={};for(let i=0;i<this.inputs.length;++i){let x=this.inputs[i],y=inputs[i],mask=masks[i];tensorMap[x.id]=[y,mask]}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer,referenceInputTensors=node2.inputTensors,referenceOutputTensors=node2.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node2.callArgs!=null&&(kwargs=node2.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i<referenceOutputTensors.length;++i){let x=referenceOutputTensors[i],y=outputTensors2[i],mask=outputMasks2[i];tensorMap[x.id]=[y,mask]}}}}let outputTensors=[],outputMasks=[],outputShapes=[];for(let x of this.outputs){assert2(x.id in tensorMap,`Could not compute output ${x.name} : ${x.id}`);let[tensor2,mask]=tensorMap[x.id];outputShapes.push(tensor2.shape),outputTensors.push(tensor2),outputMasks.push(mask)}return[outputTensors,outputMasks,outputShapes]}buildNodeConversionMap(layers){let nodeConversionMap={},keptNodes;for(let layer of this.layers){keptNodes=layer instanceof Container?1:0;for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let nodeKey=Container.nodeKey(layer,originalNodeIndex);this.containerNodes.has(nodeKey)&&(nodeConversionMap[nodeKey]=keptNodes,keptNodes+=1)}}return nodeConversionMap}getLayer(name,index){if(index!=null){if(this.layers.length<=index)throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);return this.layers[index]}else if(name==null)throw new ValueError("Provide either a layer name or layer index");for(let layer of this.layers)if(layer.name===name)return layer;throw new ValueError(`No such layer: ${name}`)}calculateLosses(){return tidy(()=>{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex<layer.inboundNodes.length;++nodeIndex){let nodeKey=Container.nodeKey(layer,nodeIndex);this.containerNodes.has(nodeKey)&&losses4.push(...layer.calculateLosses())}return losses4})}getConfig(){let config={name:this.name},nodeConversionMap=this.buildNodeConversionMap(this.layers),layerConfigs=[];for(let layer of this.layers){let layerClassName=layer.getClassName(),layerConfig=layer.getConfig(),filteredInboundNodes=[];for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let node2=layer.inboundNodes[originalNodeIndex],nodeKey=Container.nodeKey(layer,originalNodeIndex),kwargs={};if(this.containerNodes.has(nodeKey)){if(node2.callArgs)try{JSON.stringify(node2.callArgs),kwargs=node2.callArgs}catch(err){console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node2.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),kwargs={}}if(node2.inboundLayers.length>0){let nodeData=[];for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],tensorIndex=node2.tensorIndices[i],nodeKey2=Container.nodeKey(inboundLayer,nodeIndex),newNodeIndex=nodeConversionMap[nodeKey2];newNodeIndex==null&&(newNodeIndex=0),nodeData.push([inboundLayer.name,newNodeIndex,tensorIndex,kwargs])}filteredInboundNodes.push(nodeData)}}}let dict={};dict.name=layer.name,dict.className=layerClassName,dict.config=layerConfig,dict.inboundNodes=filteredInboundNodes,layerConfigs.push(dict)}config.layers=layerConfigs;let modelInputs=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i],nodeIndex=this.inputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(this.containerNodes.has(nodeKey)){let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.inputLayersTensorIndices[i];modelInputs.push([layer.name,newNodeIndex,tensorIndex])}}config.inputLayers=modelInputs;let modelOutputs=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(this.containerNodes.has(nodeKey)){let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.outputLayersTensorIndices[i];modelOutputs.push([layer.name,newNodeIndex,tensorIndex])}}return config.outputLayers=modelOutputs,config}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let createdLayers={},unprocessedNodes={};function addUnprocessedNode(layer,nodeData){layer.name in unprocessedNodes?unprocessedNodes[layer.name].push(nodeData):unprocessedNodes[layer.name]=[nodeData]}function processNode(layer,nodeData){let inputTensors2=[],kwargs;for(let inputData of nodeData){let inboundLayerName=inputData[0],inboundNodeIndex=inputData[1],inboundTensorIndex=inputData[2];if(kwargs=inputData[3]==null?{}:inputData[3],!(inboundLayerName in createdLayers)){addUnprocessedNode(layer,nodeData);return}let inboundLayer=createdLayers[inboundLayerName];if(inboundLayer.inboundNodes.length<=inboundNodeIndex){addUnprocessedNode(layer,nodeData);return}let inboundNode=inboundLayer.inboundNodes[inboundNodeIndex];inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex])}inputTensors2.length>0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config.customObjects!=null?config.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config.name,layersFromConfig=config.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex<flattenedXs.length;xIndex++)util_exports.assert(flattenedXs[xIndex].shape[0]===batchSize,()=>`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex<flattenedYs.length;yIndex++)util_exports.assert(flattenedYs[yIndex].shape[0]===batchSize,()=>`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data){if(data.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data[0],ys:data[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch<args.epochs;){let epochLogs={};await callbackList.onEpochBegin(epoch);let stepsDone=0,batchIndex=0;for(hasBatchesPerEpoch||(dataIterator=await dataset.iterator());hasBatchesPerEpoch?stepsDone<args.batchesPerEpoch:!0;){let iteratorOut=await dataIterator.next();if(hasBatchesPerEpoch&&iteratorOut.done){console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch*args.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(iteratorOut.value!=null){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),batchLogs={};batchLogs.batch=batchIndex,batchLogs.size=xs[0].shape[0],await callbackList.onBatchBegin(batchIndex,batchLogs);let sampleWeights=[];if(args.classWeight!=null){let standardClassWeights=standardizeClassWeights(args.classWeight,model2.outputNames);for(let i=0;i<standardClassWeights.length;++i)sampleWeights.push(await standardizeWeights(ys[i],null,standardClassWeights[i]))}let ins=xs.concat(ys).concat(sampleWeights),outs=trainFunction(ins);dispose(ins);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),batchIndex++,stepsDone++}if(hasBatchesPerEpoch?stepsDone>=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i<model2.metricsNames.length;++i)epochLogs[`val_${model2.metricsNames[i]}`]=valOuts[i]}break}if(model2.stopTraining_)break}if(await callbackList.onEpochEnd(epoch,epochLogs),epoch++,model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}finally{model2.isTraining=!1}}function getStepsPerEpoch(dataset,args){let stepsPerEpoch=null;return args.batchesPerEpoch!=null?stepsPerEpoch=args.batchesPerEpoch:Number.isFinite(dataset.size)&&(stepsPerEpoch=dataset.size),stepsPerEpoch}function isDatasetObject(dataset){return typeof dataset.iterator=="function"}function isLazyIteratorObject(iterator){return typeof iterator.next=="function"}async function evaluateDataset(model2,dataset,args){args=args||{};let hasBatches=args.batches!=null,f=model2.testFunction,outs=[];if(args.verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch<args.batches:!0;){let iteratorOut=await dataIterator.next();if(outs=tidy(()=>{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));let batchSize=xsAndYs[0].shape[0];for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i],oldScalar=outs[i];outs[i]=tidy(()=>add2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i<outs.length;++i){let oldScalar=outs[i];outs[i]=div(outs[i],numExamples),dispose(oldScalar)}return singletonOrArray(outs)}function checkBatchSize(batchSize){util_exports.assert(batchSize>0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart<size;)batchEnd=batchStart+batchSize,batchEnd>=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range2(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch<epochs;++epoch){await callbackList.onEpochBegin(epoch);let epochLogs={};if(stepsPerEpoch!=null)throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");{if(shuffle2==="batch")throw new NotImplementedError("batch shuffling is not implemneted yet");shuffle2&&util_exports.shuffle(indexArray);let epochIndexArray1D=tensor1d(indexArray),batches=makeBatches(numTrainSamples,batchSize);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchLogs={};if(await callbackList.onBatchBegin(batchIndex,batchLogs),tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}if(batchIndex===batches.length-1&&doValidation){let valOuts=model2.testLoop(valF,valIns,batchSize);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=valOuts[i];keep(out),epochLogs["val_"+label]=out}}}),await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),model2.stopTraining_)break}epochIndexArray1D.dispose()}if(await callbackList.onEpochEnd(epoch,epochLogs),model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}async function fitTensors(model2,x,y,args={}){if(model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;let inputs,targets,inputValX,inputValY,valX,valY,sampleWeights;try{let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!1,standardizedOuts=await model2.standardizeUserData(x,y,args.sampleWeight,args.classWeight,checkBatchAxis,batchSize);inputs=standardizedOuts[0],targets=standardizedOuts[1],sampleWeights=standardizedOuts[2];let doValidation=!1,valIns;if(args.validationData!=null&&args.validationData.length>0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;i<tensors.length;++i){let tensor2=tensors[i];if(tensor2.rank===1)outs.push(expandDims2(tensor2,1));else{if(tensor2.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");outs.push(tensor2)}}return outs}function disposeNewTensors(tensors,refTensors){if(tensors!=null){let oldTensorIds=[];if(refTensors instanceof Tensor)oldTensorIds.push(refTensors.id);else if(Array.isArray(refTensors))refTensors.forEach(t=>oldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data!=null){let gotUnexpectedData=!1;if(isDataArray(data)&&data.length>0)gotUnexpectedData=!0;else if(isDataDict(data)){for(let key in data)if(data.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`)}return[]}if(data==null)return names.map(name=>null);let arrays;if(isDataDict(data)){data=data,arrays=[];for(let name of names){if(data[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data[name])}}else if(isDataArray(data)){if(data=data,data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);arrays=data}else{if(data=data,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);arrays=[data]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null){for(let i=0;i<names.length;++i)if(shapes[i]!=null){let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);for(let j=0;j<shapes[i].length;++j)if(!(j===0&&!checkBatchAxis)){let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim>=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique2(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique2(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i<targets.length;++i){let y=targets[i],loss=lossFns[i],shape=outputShapes[i];if(loss!=null){if(loss===categoricalCrossentropy&&y.shape[y.shape.length-1]===1)throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(keyLosses.indexOf(loss)!==-1){let slicedYShape=y.shape.slice(1),slicedShape=shape.slice(1);for(let j=0;j<slicedYShape.length;++j){let targetDim=slicedYShape[j],outDim=slicedShape[j];if(outDim!=null&&targetDim!==outDim)throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function checkInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){let arrays;if(Array.isArray(data)){if(data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);arrays=data}else{if(names.length>1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);arrays=[data]}if(shapes!=null){for(let i=0;i<names.length;++i)if(shapes[i]!=null){let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);for(let j=0;j<shapes[i].length;++j)if(!(j===0&&!checkBatchAxis)){let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim!==dim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`)}}}}function collectMetrics(metrics2,outputNames){if(metrics2==null||Array.isArray(metrics2)&&metrics2.length===0)return outputNames.map(name=>[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let shape=this.internalOutputShapes[i],name=this.outputNames[i];this.feedOutputNames.push(name),this.feedOutputShapes.push(shape),this.feedLossFns.push(this.lossFunctions[i])}let skipTargetIndices=[];this.metrics=args.metrics,this.metricsNames=["loss"],this.metricsTensors=[],nameScope("loss",()=>{for(let i=0;i<this.outputs.length;++i)if(skipTargetIndices.indexOf(i)===-1){let weightedLoss=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i<this.outputs.length;++i)if(skipTargetIndices.indexOf(i)===-1){let outputMetrics=nestedMetrics[i];(metrics2=>{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i])}else for(let input2 of this.inputs){let tensorValue=inputs[input2.name];if(tensorValue==null)throw new ValueError(`No value is provided for the model's input ${input2.name}`);feedDict.add(input2,tensorValue)}let executeOutputs=execute(outputSymbolicTensors,feedDict);return outputsIsArray?executeOutputs:executeOutputs[0]}retrieveSymbolicTensors(symbolicTensorNames){let outputSymbolicTensors=pyListRepeat(null,symbolicTensorNames.length),outputsRemaining=symbolicTensorNames.length;for(let layer of this.layers){let layerOutputs=Array.isArray(layer.output)?layer.output:[layer.output],layerOutputNames=layerOutputs.map(output=>output.name);for(let i=0;i<symbolicTensorNames.length;++i){let index=layerOutputNames.indexOf(symbolicTensorNames[i]);if(index!==-1&&(outputSymbolicTensors[i]=layerOutputs[index],outputsRemaining--),outputsRemaining===0)break}if(outputsRemaining===0)break}if(outputsRemaining>0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex<batches.length;++batchIndex)tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;i<insBatch.length;++i)feeds.push({key:this.inputs[i],value:insBatch[i]});else feeds.push({key:this.inputs[0],value:insBatch});let feedDict=new FeedDict(feeds);return execute(this.outputs,feedDict)}).forEach((batchOut,i)=>outsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i<this.feedOutputShapes.length;++i){let outputShape=this.feedOutputShapes[i];this.feedLossFns[i]===sparseCategoricalCrossentropy?outputShapes.push(outputShape.slice(0,outputShape.length-1).concat([1])):outputShapes.push(outputShape)}if(x=standardizeInputData(x,this.feedInputNames,this.feedInputShapes,!1,"input"),y=standardizeInputData(y,this.feedOutputNames,outputShapes,!1,"target"),checkArrayLengths(x,y,null),checkLossAndTargetCompatibility(y,this.feedLossFns,this.feedOutputShapes),this.stateful&&batchSize!=null&&batchSize>0&&x[0].shape[0]%batchSize!==0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i<classWeights.length;++i)standardSampleWeights.push(await standardizeWeights(standardYs[i],null,classWeights[i]))}return[standardXs,standardYs,standardSampleWeights]}testLoop(f,ins,batchSize,verbose=0,steps){return tidy(()=>{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range2(0,numSamples));for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(indexArray,batchStart,batchEnd-batchStart),insBatch=sliceArraysByIndices(ins,batchIds),batchOuts=f(insBatch);if(batchIndex===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i];outs[i]=add2(outs[i],mul(batchEnd-batchStart,batchOut))}}for(let i=0;i<outs.length;++i)outs[i]=div(outs[i],numSamples)}return outs})}getDedupedMetricsNames(){let outLabels=this.metricsNames,dedupedOutLabels=[];for(let i=0;i<outLabels.length;++i){let label=outLabels[i],newLabel=label;count(outLabels,label)>1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data=>{let lossValues=[],inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict,{training:!0}),totalLoss;for(let i=0;i<this.lossFunctions.length;++i){let loss=this.lossFunctions[i](targets[i],outputs[i]);sampleWeights[i]!=null&&(loss=computeWeightedLoss2(loss,sampleWeights[i]));let meanLoss=mean(loss);lossValues.push(meanLoss),i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss)}for(let i=0;i<this.metricsTensors.length;++i){let weightedMetric;if(this.outputs.length>1&&i<this.outputs.length)weightedMetric=lossValues[i];else{let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1];weightedMetric=mean(metric(targets[outputIndex],outputs[outputIndex]))}keep(weightedMetric),metricsValues.push(weightedMetric)}return totalLoss=mean(totalLoss),this.calculateLosses().forEach(regularizerLoss=>{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict);for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=mean(lossFunction(targets[i],outputs[i]));i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss),valOutputs.push(totalLoss)}for(let i=0;i<this.metricsTensors.length;++i){let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1],meanMetric=mean(metric(targets[outputIndex],outputs[outputIndex]));valOutputs.push(meanMetric)}return valOutputs})}async fit(x,y,args={}){return fitTensors(this,x,y,args)}async fitDataset(dataset,args){return fitDataset(this,dataset,args)}async trainOnBatch(x,y){let standardizeOut=await this.standardizeUserData(x,y),inputs=standardizeOut[0],targets=standardizeOut[1],losses4=this.makeTrainFunction()(inputs.concat(targets)),lossValues=[];for(let loss of losses4){let v=await loss.data();lossValues.push(v[0])}return dispose(losses4),singletonOrArray(lossValues)}getNamedWeights(config){let namedWeights=[],trainableOnly=config!=null&&config.trainableOnly,weights=trainableOnly?this.trainableWeights:this.weights,weightValues=this.getWeights(trainableOnly);for(let i=0;i<weights.length;++i)trainableOnly&&!weights[i].trainable||namedWeights.push({name:weights[i].originalName,tensor:weightValues[i]});return namedWeights}set stopTraining(stop){this.stopTraining_=stop}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(optimizer){this.optimizer_!==optimizer&&(this.optimizer_=optimizer,this.isOptimizerOwned=!1)}dispose(){let result=super.dispose();if(result.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let numTensorsBeforeOptmizerDisposal=memory().numTensors;this.optimizer_.dispose(),result.numDisposedVariables+=numTensorsBeforeOptmizerDisposal-memory().numTensors}return result}getLossIdentifiers(){let lossNames;if(typeof this.loss=="string")lossNames=toSnakeCase(this.loss);else if(Array.isArray(this.loss)){for(let loss of this.loss)if(typeof loss!="string")throw new Error("Serialization of non-string loss is not supported.");lossNames=this.loss.map(name=>toSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version2}`,convertedBy:null};if((config==null?!1:config.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config instanceof Array){if(config[0].className==null||config[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config}else util_exports.assert(config.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config.layers,delete config.layers,extraModelConfig=config;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config){return new Sequential(config)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config){return Input(config)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu2(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config={};return config.className="linear",config.config={},deserializeActivation(config)}if(typeof identifier=="string"){let config={};return config.className=identifier,config.config={},deserializeActivation(config)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square2(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config){return new cls({l1:config.l1,l2:config.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i<inputShape.length;++i)axes[i]=inputShape[i];this.inputSpec=[new InputSpec({ndim:inputShape.length,axes})],this.built=!0}call(inputs,kwargs){return inputs=getExactlyOneTensor(inputs),prelu(inputs,this.alpha.read())}getConfig(){let config={alphaInitializer:serializeInitializer(this.alphaInitializer),alphaRegularizer:serializeRegularizer(this.alphaRegularizer),alphaConstraint:serializeConstraint(this.alphaConstraint),sharedAxes:this.sharedAxes},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};PReLU.className="PReLU";serialization_exports.registerClass(PReLU);var ELU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA=1,args==null&&(args={}),args.alpha!=null&&args.alpha!==this.DEFAULT_ALPHA)throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return elu(x)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ELU.className="ELU";serialization_exports.registerClass(ELU);var ThresholdedReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_THETA=1,args==null&&(args={}),this.theta=args.theta==null?this.DEFAULT_THETA:args.theta}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return x.mul(cast2(x.greater(this.theta),"float32"))}computeOutputShape(inputShape){return inputShape}getConfig(){let config={theta:this.theta},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ThresholdedReLU.className="ThresholdedReLU";serialization_exports.registerClass(ThresholdedReLU);var Softmax3=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_AXIS=1,args==null&&(args={}),this.softmax=new Softmax2().apply,this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return this.softmax(x,this.axis)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Softmax3.className="Softmax";serialization_exports.registerClass(Softmax3);function normalizeArray(value,n,name){if(typeof value=="number")return pyListRepeat(value,n);if(value.length!==n)throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);for(let i=0;i<n;++i){let singleValue=value[i];if(!isInteger(singleValue))throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`)}return value}function convOutputLength(inputLength,filterSize,padding,stride,dilation=1){if(inputLength==null)return inputLength;let dilatedFilterSize=filterSize+(filterSize-1)*(dilation-1),outputLength;return padding==="same"?outputLength=inputLength:outputLength=inputLength-dilatedFilterSize+1,Math.floor((outputLength+stride-1)/stride)}function deconvLength(dimSize,strideSize,kernelSize,padding){if(dimSize==null)return null;if(padding==="valid")dimSize=dimSize*strideSize+max2([kernelSize-strideSize,0]);else if(padding==="same")dimSize=dimSize*strideSize;else throw new ValueError(`Unsupport padding mode: ${padding}.`);return dimSize}function preprocessConv2DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i<space.length;++i){let newDim=convOutputLength(space[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);newSpace.push(newDim)}let outputShape=[inputShape[0]];return this.dataFormat==="channelsLast"?(outputShape=outputShape.concat(newSpace),outputShape.push(this.filters)):(outputShape.push(this.filters),outputShape=outputShape.concat(newSpace)),outputShape}getConfig(){let config={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static verifyArgs(args){if(!("filters"in args)||typeof args.filters!="number"||args.filters<1)throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config=super.getConfig();return delete config.dilationRate,config}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config){super(rank,config);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config.kernelInitializer!=null||config.kernelRegularizer!=null||config.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config.padding!=null&&config.padding!=="same"&&config.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);this.depthMultiplier=config.depthMultiplier==null?1:config.depthMultiplier,this.depthwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<this.rank+2)throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(inputShape)}`);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=this.kernelSize.concat([inputDim,this.depthMultiplier]),pointwiseKernelShape=[];for(let i=0;i<this.rank;++i)pointwiseKernelShape.push(1);pointwiseKernelShape.push(inputDim*this.depthMultiplier,this.filters);let trainable=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,trainable,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",pointwiseKernelShape,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,trainable,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,trainable,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config=super.getConfig();return delete config.rank,delete config.kernelInitializer,delete config.kernelRegularizer,delete config.kernelConstraint,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config=super.getConfig();return delete config.rank,delete config.dataFormat,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d3(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d3(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config=super.getConfig();return config.depthMultiplier=this.depthMultiplier,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range2(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;t<timeSteps;++t){let currentInput=perStepInputs[t],stepOutputs=tidy(()=>stepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range2(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;i<numStates;++i)output.push(null);return output}else return this.states_}set states(s){this.states_=s}build(inputShape){let constantShape=null;if(this.numConstants!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let batchSize=this.stateful?inputShape[0]:null,inputDim=inputShape.slice(2);this.inputSpec[0]=new InputSpec({shape:[batchSize,null,...inputDim]});let stepInputShape=[inputShape[0]].concat(inputShape.slice(2));if(constantShape!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");this.cell.build(stepInputShape);let stateSize;if(Array.isArray(this.cell.stateSize)?stateSize=this.cell.stateSize:stateSize=[this.cell.stateSize],this.stateSpec!=null){if(!util_exports.arraysEqual(this.stateSpec.map(spec=>spec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],dim=Array.isArray(this.cell.stateSize)?this.cell.stateSize[index]:this.cell.stateSize,expectedShape=[batchSize,dim];if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims2(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile2(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile2(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cellConfig=config.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return new cls(config)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let matrixX=dot2(inputs,this.kernel.read());this.useBias&&(matrixX=biasAdd(matrixX,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0]));let recurrentKernelValue=this.recurrentKernel.read(),[rk1,rk2]=split(recurrentKernelValue,[2*this.units,this.units],recurrentKernelValue.rank-1),matrixInner=dot2(hTMinus1,rk1),[xZ,xR,xH]=split(matrixX,3,matrixX.rank-1),[recurrentZ,recurrentR]=split(matrixInner,2,matrixInner.rank-1);z=this.recurrentActivation.apply(add2(xZ,recurrentZ)),r=this.recurrentActivation.apply(add2(xR,recurrentR));let recurrentH=dot2(mul(r,hTMinus1),rk2);hh=this.activation.apply(add2(xH,recurrentH));let h=add2(mul(z,hTMinus1),mul(add2(1,neg(z)),hh));return[h,h]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},baseConfig,config)}};GRUCell.className="GRUCell";serialization_exports.registerClass(GRUCell);var GRU=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new GRUCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let z=dot2(inputs,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0])),z=add2(z,dot2(hTMinus1,this.recurrentKernel.read())),this.useBias&&(z=biasAdd(z,this.bias.read()));let[z0,z1,z2,z3]=split(z,4,z.rank-1);i=this.recurrentActivation.apply(z0),f=this.recurrentActivation.apply(z1),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(z2))),o=this.recurrentActivation.apply(z3);let h=mul(o,this.activation.apply(c));return[h,h,c]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},baseConfig,config)}};LSTMCell.className="LSTMCell";serialization_exports.registerClass(LSTMCell);var LSTM=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new LSTMCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i<this.cells.length;++i){let cell=this.cells[i];states=nestedStates[i],i===0?callInputs=[inputs[0]].concat(states):callInputs=[callInputs[0]].concat(states),callInputs=cell.call(callInputs,kwargs),newNestedStates.push(callInputs.slice(1))}states=[];for(let cellStates of newNestedStates.slice().reverse())states.push(...cellStates);return[callInputs[0]].concat(states)})}build(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let outputDim;this.cells.forEach((cell,i)=>{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cells=[];for(let cellConfig of config.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;i<cell.weights.length;++i)tuples.push([cell.weights[i],inputWeights[i]])}batchSetValue(tuples)}};StackedRNNCells.className="StackedRNNCells";serialization_exports.registerClass(StackedRNNCells);function generateDropoutMask(args){let{ones:ones4,rate,training=!1,count:count2=1}=args,droppedInputs=()=>dropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i<p2.length;i++)e.indexOf(p2[i])<0&&Object.prototype.propertyIsEnumerable.call(s,p2[i])&&(t[p2[i]]=s[p2[i]]);return t},ConvRNN2D=class extends RNN{constructor(args){if(args.unroll)throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(args.cell))throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(args);this.inputSpec=[new InputSpec({ndim:5})]}call(inputs,kwargs){return tidy(()=>{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],expectedShape=stateShape;if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config){return new cls(config)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i<this.noiseShape.length;++i)noiseShape.push(this.noiseShape[i]==null?inputShape[i]:this.noiseShape[i]);return noiseShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0<this.rate&&this.rate<1){let training=kwargs.training==null?!1:kwargs.training,noiseShape=this.getNoiseShape(input2);return inTrainPhase(()=>dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i<input2.rank;++i)permutation.push(i);permutation.push(1),input2=input2.transpose(permutation)}return batchFlatten(input2)})}getConfig(){let config={};this.dataFormat!=null&&(config.dataFormat=this.dataFormat);let baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Flatten.className="Flatten";serialization_exports.registerClass(Flatten);var Activation2=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.activation=getActivation(args.activation)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config={n:this.n},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i<this.targetShape.length;++i)this.isUnknown(this.targetShape[i])&&(this.targetShape[i]=null)}isUnknown(dim){return dim<0||dim==null}fixUnknownDimension(inputShape,outputShape){let errorMsg="Total size of new array must be unchanged.",finalShape=outputShape.slice(),known=1,unknown=null;for(let i=0;i<finalShape.length;++i){let dim=finalShape[i];if(this.isUnknown(dim))if(unknown===null)unknown=i;else throw new ValueError("Can only specifiy one unknown dimension.");else known*=dim}let originalSize=arrayProd(inputShape);if(unknown!==null){if(known===0||originalSize%known!==0)throw new ValueError(errorMsg);finalShape[unknown]=originalSize/known}else if(originalSize!==known)throw new ValueError(errorMsg);return finalShape}computeOutputShape(inputShape){let anyUnknownDims=!1;for(let i=0;i<inputShape.length;++i)if(this.isUnknown(inputShape[i])){anyUnknownDims=!0;break}return anyUnknownDims?inputShape.slice(0,1).concat(this.targetShape):inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape))}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range2(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={maskValue:this.maskValue};return Object.assign(config,baseConfig),config}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k<inLens.length;++k){let s1=inLens[k],s2=inputShape[k+1];if(s1!=null&&s2!=null&&s1!==s2)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);s1==null&&(inLens[i]=s2),i++}}return[inputShape[0],...inLens,this.outputDim]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast2(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length<shape2.length)return this.computeElementwiseOpOutputShape(shape2,shape1);if(shape2.length===0)return shape1;let outputShape=shape1.slice(0,shape1.length-shape2.length);for(let k=0;k<shape2.length;++k){let i=shape1[shape1.length-shape2.length+k],j=shape2[k];if(i==null||j==null||i<0||j<0)outputShape.push(null);else if(i===1)outputShape.push(j);else if(j===1)outputShape.push(i);else{if(i!==j)throw new ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(shape1)+" "+JSON.stringify(shape2));outputShape.push(i)}}return outputShape}build(inputShape){if(Array.isArray(inputShape)&&!Array.isArray(inputShape[0])&&(inputShape=[getExactlyOneShape(inputShape)]),inputShape=inputShape,inputShape.length<2)throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);if(batchSizes=unique2(batchSizes),batchSizes.length>1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let allRanks=inputShape.map(shape=>shape.length);inputShape.indexOf(null)===-1&&unique2(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max2(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k<maxNDim-xNDim;++k)x=expandDims2(x,1);reshapedInputs.push(x)}return this.mergeFunction(reshapedInputs)}else{let transposed=!1;for(let x of inputs){let xNDim=x.rank;if(xNDim==null){let xShape=x.shape,batchSize=xShape[0],newShape=xShape.slice(1).concat([batchSize]),xTransposed=x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));xTransposed=transpose(xTransposed,[1,0]),xTransposed=xTransposed.reshape(newShape),reshapedInputs.push(xTransposed),transposed=!0}else if(xNDim>1){let dims=range2(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range2(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);return batchSizes=unique2(batchSizes),batchSizes.length===1?outputShape=batchSizes.concat(outputShape):outputShape=[null].concat(outputShape),outputShape}computeMask(inputs,mask){return tidy(()=>{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i<mask.length-1;++i)output=logicalAnd(output,mask[i]);return output})}},Add2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return output})}};Add2.className="Add";serialization_exports.registerClass(Add2);var Multiply2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=mul(output,inputs[i]);return output})}};Multiply2.className="Multiply";serialization_exports.registerClass(Multiply2);var Average=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return mul(1/inputs.length,output)})}};Average.className="Average";serialization_exports.registerClass(Average);var Maximum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=maximum(output,inputs[i]);return output})}};Maximum2.className="Maximum";serialization_exports.registerClass(Maximum2);var Minimum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=minimum(output,inputs[i]);return output})}};Minimum2.className="Minimum";serialization_exports.registerClass(Minimum2);var Concatenate=class extends Merge{constructor(args){super(args);this.DEFAULT_AXIS=-1,args==null&&(args={}),this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0]))||inputShape.length===1)throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");inputShape=inputShape;let allNoneShape=!0;for(let shape of inputShape)if(shape!=null){allNoneShape=!1;break}if(!allNoneShape){let shapeSet=[];for(let i=0;i<inputShape.length;++i){let shapeWithoutConcatAxis=inputShape[i].slice();shapeWithoutConcatAxis.splice(this.axis,1);let exists=!1;for(let shape of shapeSet)if(util_exports.arraysEqual(shape,shapeWithoutConcatAxis)){exists=!0;break}exists||shapeSet.push(shapeWithoutConcatAxis)}if(shapeSet.length>1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i<inputs.length;++i)mask[i]==null?outputMasks.push(onesLike(inputs[i]).asType("bool")):mask[i].rank<inputs[i].rank?outputMasks.push(expandDims(mask[i],-1)):outputMasks.push(mask[i]);let concatenatedMasks=concat(outputMasks,this.axis);return all(concatenatedMasks,-1,!1)})}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Concatenate.className="Concatenate";serialization_exports.registerClass(Concatenate);function interpretAxis(axis,dim){for(;axis<0;)axis+=dim;return axis}function batchDot(x,y,axes){if(x.shape.length>3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);y=y.reshape(y.shape.concat(diffShape))}else if(yNDim>xNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);x=x.reshape(x.shape.concat(diffShape))}else diff=0;let out;if(x.shape.length===2&&y.shape.length===2)axesArray[0]===axesArray[1]?out=x.mul(y).sum(axesArray[0]):out=x.transpose([1,0]).mul(y).sum(axesArray[1]);else{let adjX=axesArray[0]!==x.shape.length-1,adjY=axesArray[1]===y.shape.length-1;out=x.matMul(y,adjX,adjY)}if(diff>0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i<idx+diff;++i)squeezeAxes.push(i);out=out.squeeze(squeezeAxes)}return out.shape.length===1&&(out=out.expandDims(1)),out})}var Dot=class extends Merge{constructor(args){super(args);this.axes=args.axes,this.normalize=args.normalize==null?!1:args.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={stddev:this.stddev};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale22=1.0507009873554805,alphaP=-alpha*scale22,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast2(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon32=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon32);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon32);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon32);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon32),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range2(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon32),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range2(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range2(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range2(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=nDims);for(let axis of this.axis)if(axis<0||axis>=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique2(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale22=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i<nDims;++i)this.axis.indexOf(i)!==-1?(momentsTiling.push(inputShape[i]),scaleOffsetTiling.push(1)):(momentsTiling.push(1),scaleOffsetTiling.push(inputShape[i]));return mean4=mean4.tile(momentsTiling),variance=variance.tile(momentsTiling),scale22=scale22.tile(scaleOffsetTiling),offset=offset.tile(scaleOffsetTiling),batchNormalization(input2,mean4,variance,offset,scale22,this.epsilon)})}getConfig(){let config={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LayerNormalization.className="LayerNormalization";serialization_exports.registerClass(LayerNormalization);function spatial2dPadding(x,padding,dataFormat){return tidy(()=>{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims2(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config,customObjects={}){let layerConfig=config.layer,layer=deserialize(layerConfig,customObjects);delete config.layer;let newConfig={layer};return Object.assign(newConfig,config),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static fromConfig(cls,config){let rnnLayer=deserialize(config.layer);if(delete config.layer,config.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu3(args){return new ELU(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu2(args){return new PReLU(args)}function softmax2(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d3(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d4(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape2(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add3(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum2(args){return new Maximum2(args)}function minimum2(args){return new Minimum2(args)}function multiply(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config){return new L1L2(config)}function l12(config){return l1(config)}function l22(config){return l2(config)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less2(currVal,prevVal){return currVal<prevVal}function greater2(currVal,prevVal){return currVal>prevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less2:this.mode==="max"?this.monitorFunc=greater2:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater2:this.monitorFunc=less2,this.monitorFunc===less2&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less2?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node2,tensorMap,context,resourceManager){let inputParam=node2.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node2.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node2.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node2.inputNames.slice(start)[0],tensorMap,context,resourceManager),data=tensor2.dataSync();return inputParam.type==="number"?data[0]:util_exports.toNestedArray(tensor2.shape,data)}let attrParam=node2.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node2,tensorMap,context){let pad3=getParamValue("pad",node2,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node2,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});var 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json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op.startsWith("Placeholder")?placeholders.push(map[node2.name]):node2.op==="Const"?weights.push(map[node2.name]):(node2.input==null||node2.input.length===0)&&initNodes.push(map[node2.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node2=nodes[key];node2.children.length===0&&outputs.push(node2)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2!=null&&(node2.signatureKey=outputNodeNameToKey[name],outputs.push(node2))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2&&(node2.signatureKey=inputNodeNameToKey[name],inputs.push(node2))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node2){let mapper=getRegisteredOp(node2.op)||this.opMappers[node2.op]||{};node2.attr==null&&(node2.attr={});let newNode={name:node2.name,op:node2.op,category:mapper.category,inputNames:(node2.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node2.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node2.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node2.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op==="Const"&&weights.push(map[node2.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node2={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node2.signatureKey=arg.name,inputs.push(node2),nodes[nodeName]=node2}),Object.keys(nodes).forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node2=nodes[nodeName];node2!=null&&(node2.defaultOutput=index,outputs.push(node2))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){typeof value=="string"&&(value=DataType[value]);switch(value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&&param.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&&param.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&&param.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&&param.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&&param.list&&param.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node2,tensorMap,context){this.node=node2,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node2.inputNames.map(name=>this.getInput(name)),node2.rawAttrs!=null&&(this.attrs=Object.keys(node2.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node2,tensorMap,context)=>{switch(node2.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node2,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp2=(node2,tensorMap,context)=>{switch(node2.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node2,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node2,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node2,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node2,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node2,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node2,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node2,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node2,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node2,tensorMap,context),getParamValue("imag",node2,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node2,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node2,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node2,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node2,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node2,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node2,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node2,tensorMap,context))];case"Log":return[log(getParamValue("x",node2,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node2,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node2,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node2,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node2,tensorMap,context))];case"Real":return[real(getParamValue("x",node2,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node2,tensorMap,context))];case"Round":return[round2(getParamValue("x",node2,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node2,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node2,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node2,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node2,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node2,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node2,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node2,tensorMap,context))];case"Square":return[square(getParamValue("x",node2,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node2,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node2,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node2,tensorMap,context),getParamValue("clipValueMin",node2,tensorMap,context),getParamValue("clipValueMax",node2,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node2,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node2.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node2,tensorMap,context),getParamValue("axes",node2,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==-1&&n2[i]!==-1&&n1[i]!==n2[i])return!1;return!0}var TensorArray=class{constructor(name,dtype,maxSize,elementShape,identicalElementShapes,dynamicSize,clearAfterRead){this.name=name,this.dtype=dtype,this.maxSize=maxSize,this.elementShape=elementShape,this.identicalElementShapes=identicalElementShapes,this.dynamicSize=dynamicSize,this.clearAfterRead=clearAfterRead,this.tensors=[],this.closed_=!1,this.idTensor=scalar(0),keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config.config);let returnObj=fromConfig(cls,config.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return a<b?-1:a>b?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique2(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now2=util_exports.now();return now2-lastTime<waitMs||(lastTime=now2,lastResult=f(...args)),lastResult}}function mapActivationToFusedKernel(activationName){return activationName==="relu"?"relu":activationName==="linear"?"linear":activationName==="elu"?"elu":null}function calcL2Norms(w,axis){return tidy(()=>sqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config){return new MinMaxNorm(config)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros2});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i<end;++i)prod5*=array2[i];return prod5}function toArray1D(array2){return array2=Array.isArray(array2)?new Float32Array(array2):array2,tensor1d(array2)}function min2(array2){return min(toArray1D(array2)).dataSync()[0]}function max2(array2){return max(toArray1D(array2)).dataSync()[0]}function range2(begin,end){if(end<begin)throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);let out=[];for(let i=begin;i<end;++i)out.push(i);return out}function cast2(x,dtype){return x.asType(dtype)}function expandDims2(x,axis=-1){let outShape=x.shape.slice();return axis<0&&(axis=outShape.length+axis+1),outShape.splice(axis,0,1),x.reshape(outShape)}function repeat(x,n){return tidy(()=>{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims2(x,1);return tile2(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile2(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square2(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu2(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale22=this.scale;if(this.mode==="fanIn"?scale22/=Math.max(1,fanIn):this.mode==="fanOut"?scale22/=Math.max(1,fanOut):scale22/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale22);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale22);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config={};return config.className=className,config.config={},deserializeInitializer(config)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros2(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity(args){return new Identity2(args)}function varianceScaling(config){return new VarianceScaling(config)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add3,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d3,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d4,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu3,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum2,minimum:()=>minimum2,multiply:()=>multiply,permute:()=>permute,prelu:()=>prelu2,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape2,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax2,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndex<inputs.length;inputIndex++){let x=inputs[inputIndex],spec=inputSpec[inputIndex];if(spec==null)continue;let ndim=x.rank;if(spec.ndim!=null&&ndim!==spec.ndim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);if(spec.maxNDim!=null&&ndim>spec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim<spec.minNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);if(spec.dtype!=null&&x.dtype!==spec.dtype)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);if(spec.axes){let xShape=x.shape;for(let key in spec.axes){let axis=Number(key),value=spec.axes[key],xShapeAtAxis=axis>=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i<spec.shape.length;++i){let specDim=spec.shape[i],dim=x.shape[i];if(specDim!=null&&dim!=null&&specDim!==dim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`)}}}call(inputs,kwargs){return inputs}invokeCallHook(inputs,kwargs){this._callHook!=null&&this._callHook(inputs,kwargs)}setCallHook(callHook){this._callHook=callHook}clearCallHook(){this._callHook=null}apply(inputs,kwargs){kwargs=kwargs||{},this.assertNotDisposed();let inputsList=toList(inputs),allAreSymbolic=!0;for(let input2 of inputsList)if(!(input2 instanceof SymbolicTensor)){allAreSymbolic=!1;break}let noneAreSymbolic=!0;for(let input2 of inputsList)if(input2 instanceof SymbolicTensor){noneAreSymbolic=!1;break}if(allAreSymbolic===noneAreSymbolic)throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return nameScope(this.name,()=>{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node2 of this.inboundNodes){let shapeString=JSON.stringify(node2.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;i<paramValues.length;++i){let pv=paramValues[i],p2=params[i],w=weights[i];if(!util_exports.arraysEqual(pv.shape,w.shape))throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);weightValueTuples.push([p2,w])}batchSetValue(weightValueTuples)})}addWeight(name,shape,dtype,initializer,regularizer,trainable,constraint){if(this._addedWeightNames.indexOf(name)!==-1)throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);this._addedWeightNames.push(name),dtype==null&&(dtype="float32"),this.fastWeightInitDuringBuild&&(initializer=getInitializer("zeros"));let initValue=initializer.apply(shape,dtype),weight=new LayerVariable(initValue,dtype,name,trainable,constraint);return initValue.dispose(),regularizer!=null&&this.addLoss(()=>regularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;i<outputTensors.length;i++)outputTensors[i].sourceLayer=this,outputTensors[i].nodeIndex=this.inboundNodes.length-1,outputTensors[i].tensorIndex=i}getConfig(){let config={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(config.batchInputShape=this.batchInputShape),this.dtype!=null&&(config.dtype=this.dtype),config}disposeWeights(){return this.weights.forEach(weight=>weight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount==0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node2=layer.inboundNodes[nodeIndex];if(node2.inboundLayers.length===0)return node2.inputTensors;{let sourceTensors=[];for(let i=0;i<node2.inboundLayers.length;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],previousSources=getSourceInputs(x,layer2,nodeIndex2);for(let x2 of previousSources)sourceTensors.indexOf(x2)===-1&&sourceTensors.push(x2)}return sourceTensors}}}var InputLayer=class extends Layer{constructor(args){super({dtype:args.dtype,name:args.name!=null?args.name:getUid("input").toString()});if(args.batchSize==null&&(args.batchSize=null),args.sparse==null&&(args.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=args.sparse,args.inputShape!=null&&args.batchInputShape!=null)throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let batchInputShape=args.batchInputShape;if(batchInputShape==null){if(args.inputShape==null)throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");batchInputShape=[args.batchSize].concat(args.inputShape)}else if(args.batchSize!=null)throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let dtype=args.dtype||"float32";this.batchInputShape=batchInputShape,this.dtype=dtype,this.inputSpec=[{shape:batchInputShape}];let inputTensor=new SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);inputTensor.nodeIndex=0,inputTensor.tensorIndex=0,new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[inputTensor],outputTensors:[inputTensor],inputMasks:[null],outputMasks:[null],inputShapes:[batchInputShape],outputShapes:[batchInputShape]})}apply(inputs,kwargs){throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};InputLayer.className="InputLayer";serialization_exports.registerClass(InputLayer);function Input(config){if(config.batchShape==null&&config.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(config.batchShape!=null&&config.shape!=null)throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let batchShape=config.batchShape;config.shape!=null&&batchShape==null&&(batchShape=[null].concat(config.shape));let dtype=config.dtype;return dtype==null&&(dtype="float32"),new InputLayer({batchInputShape:batchShape,name:config.name,dtype,sparse:config.sparse}).inboundNodes[0].outputTensors[0]}async function resolveScalarsInLogs(logs){if(logs==null)return;let promises=[],keys=[],scalarsToDispose=[];for(let key in logs){let value=logs[key];if(typeof value!="number"){let valueScalar=value;promises.push(valueScalar.data()),keys.push(key),scalarsToDispose.push(valueScalar)}}if(promises.length>0){let values=await Promise.all(promises);for(let i=0;i<values.length;++i)logs[keys[i]]=values[i][0];dispose(scalarsToDispose)}}function disposeTensorsInLogs(logs){if(logs!=null)for(let key in logs){let value=logs[key];typeof value!="number"&&value.dispose()}}var ModelLoggingVerbosity;(function(ModelLoggingVerbosity2){ModelLoggingVerbosity2[ModelLoggingVerbosity2.SILENT=0]="SILENT",ModelLoggingVerbosity2[ModelLoggingVerbosity2.VERBOSE=1]="VERBOSE"})(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));var DEFAULT_YIELD_EVERY_MS=125,BaseCallback=class{constructor(){this.validationData=null}setParams(params){this.params=params}async onEpochBegin(epoch,logs){}async onEpochEnd(epoch,logs){}async onBatchBegin(batch,logs){}async onBatchEnd(batch,logs){}async onTrainBegin(logs){}async onTrainEnd(logs){}setModel(model2){}},CallbackList=class{constructor(callbacks2,queueLength=10){callbacks2==null&&(callbacks2=[]),this.callbacks=callbacks2,this.queueLength=queueLength}append(callback){this.callbacks.push(callback)}setParams(params){for(let callback of this.callbacks)callback.setParams(params)}setModel(model2){for(let callback of this.callbacks)callback.setModel(model2)}async onEpochBegin(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochBegin(epoch,logs)}async onEpochEnd(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochEnd(epoch,logs)}async onBatchBegin(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchBegin(batch,logs)}async onBatchEnd(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchEnd(batch,logs)}async onTrainBegin(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainBegin(logs)}async onTrainEnd(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainEnd(logs)}},BaseLogger=class extends BaseCallback{constructor(){super()}async onEpochBegin(epoch){this.seen=0,this.totals={}}async onBatchEnd(batch,logs){logs==null&&(logs={});let batchSize=logs.size==null?0:logs.size;this.seen+=batchSize;for(let key in logs){let value=logs[key];if(typeof value=="number")this.totals.hasOwnProperty(key)||(this.totals[key]=0),this.totals[key]=this.totals[key]+value*batchSize;else{let oldTotalsToDispose;key in this.totals?oldTotalsToDispose=this.totals[key]:this.totals[key]=0;let total=tidy(()=>add2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log5=mul(div(1,this.seen),this.totals[key]);logs[key]=log5,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;i<valueArray.length;++i)if(typeof valueArray[i]!="number"){let valueScalar=valueArray[i];promises.push(valueScalar.data()),keys.push(key),indices.push(i)}}let values=await Promise.all(promises);for(let n=0;n<values.length;++n)this.history[keys[n]][indices[n]].dispose(),this.history[keys[n]][indices[n]]=values[n][0]}},CustomCallback=class extends BaseCallback{constructor(args,yieldEvery){super();if(this.currentEpoch=0,this.yieldEvery=yieldEvery||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=DEFAULT_YIELD_EVERY_MS),this.yieldEvery==="never"&&args.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");util_exports.isNumber(this.yieldEvery)&&(this.maybeWait=debounce(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=args.onTrainBegin,this.trainEnd=args.onTrainEnd,this.epochBegin=args.onEpochBegin,this.epochEnd=args.onEpochEnd,this.batchBegin=args.onBatchBegin,this.batchEnd=args.onBatchEnd,this.yield=args.onYield}async maybeWait(epoch,batch,logs){let ps=[];this.yield!=null&&(await resolveScalarsInLogs(logs),ps.push(this.yield(epoch,batch,logs))),ps.push(nextFrame()),await Promise.all(ps)}async onEpochBegin(epoch,logs){this.currentEpoch=epoch,this.epochBegin!=null&&(await resolveScalarsInLogs(logs),await this.epochBegin(epoch,logs))}async onEpochEnd(epoch,logs){let ps=[];this.epochEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.epochEnd(epoch,logs))),this.yieldEvery==="epoch"&&ps.push(nextFrame()),await Promise.all(ps)}async onBatchBegin(batch,logs){this.batchBegin!=null&&(await resolveScalarsInLogs(logs),await this.batchBegin(batch,logs))}async onBatchEnd(batch,logs){let ps=[];this.batchEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.batchEnd(batch,logs))),this.yieldEvery==="batch"?ps.push(nextFrame()):util_exports.isNumber(this.yieldEvery)&&ps.push(this.maybeWait(this.currentEpoch,batch,logs)),await Promise.all(ps)}async onTrainBegin(logs){this.trainBegin!=null&&(await resolveScalarsInLogs(logs),await this.trainBegin(logs))}async onTrainEnd(logs){this.trainEnd!=null&&(await resolveScalarsInLogs(logs),await this.trainEnd(logs))}};function standardizeCallbacks(callbacks2,yieldEvery){return callbacks2==null&&(callbacks2={}),callbacks2 instanceof BaseCallback?[callbacks2]:Array.isArray(callbacks2)&&callbacks2[0]instanceof BaseCallback?callbacks2:toList(callbacks2).map(callbackConfig=>new CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square2(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square2(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon(),Number.MAX_VALUE),firstLog=log(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon(),Number.MAX_VALUE),secondLog=log(add2(1,clippedTrue));return mean(square2(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square2(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log22=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log22);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon(),1-epsilon()),neg(sum2(mul(target.toFloat(),log(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon(),1-epsilon());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon(),1-epsilon()),y=log(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon(),1),clippedPred=clipByValue(yPred,epsilon(),1);return sum2(mul(yTrue,log(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log(add2(epsilon(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast2(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast2(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon()),Adam:()=>train.adam(.001,.9,.999,epsilon()),Adamax:()=>train.adamax(.002,.9,.999,epsilon(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys)if(typeof key!="string"||!plainObjectCheck(x[key]))return!1;return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i<layers.length;++i)sequentialLike?printLayerSummary(layers[i],positions,printFn):printLayerSummaryWithConnections(layers[i],positions,relevantNodes,printFn),printFn((i===layers.length-1?"=":"_").repeat(lineLength));model2.checkTrainableWeightsConsistency();let trainableCount=countTrainableParams(model2),nonTrainableCount=countParamsInWeights(model2.nonTrainableWeights);printFn(`Total params: ${trainableCount+nonTrainableCount}`),printFn(`Trainable params: ${trainableCount}`),printFn(`Non-trainable params: ${nonTrainableCount}`),printFn("_".repeat(lineLength))}function countTrainableParams(model2){let trainableCount;return model2.collectedTrainableWeights!=null?trainableCount=countParamsInWeights(model2.collectedTrainableWeights):trainableCount=countParamsInWeights(model2.trainableWeights),trainableCount}function isModelSequentialLike(model2){let sequentialLike=!0,nodesByDepth=[],nodes=[];for(let depth in model2.nodesByDepth)nodesByDepth.push(model2.nodesByDepth[depth]);for(let depthNodes of nodesByDepth){if(depthNodes.length>1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node2 of layer.inboundNodes)if(nodes.indexOf(node2)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i<fields.length;++i)i>0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node2 of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node2)===-1))for(let i=0;i<node2.inboundLayers.length;++i){let inboundLayer=node2.inboundLayers[i].name,inboundLayerIndex=node2.nodeIndices[i],inboundTensorIndex=node2.tensorIndices[i];connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`)}let name=layer.name,className=layer.getClassName(),firstConnection=connections.length===0?"":connections[0],fields=[`${name} (${className})`,outputShape,layer.countParams().toString(),firstConnection];printRow(fields,positions,printFn);for(let i=1;i<connections.length;++i)printRow(["","","",connections[i]],positions,printFn)}function isArrayItemInputOrOutputName(key,index,value){return(key==="inboundNodes"||key==="outputLayers"||key==="inputLayers")&&index===0&&typeof value=="string"}function convertPythonicToTs(pythonicConfig,key){if(pythonicConfig===null)return null;if(typeof pythonicConfig=="string")return toCamelCase(pythonicConfig);if(typeof pythonicConfig=="number"||typeof pythonicConfig=="boolean")return pythonicConfig;if(pythonicConfig instanceof Array){let tsArray=[],arrayLength=pythonicConfig.length;for(let i=0;i<arrayLength;++i){let item=pythonicConfig[i];isArrayItemInputOrOutputName(key,i,item)?tsArray.push(item):tsArray.push(convertPythonicToTs(item,key))}return tsArray}else{let tsDict={};for(let pythonicKey of Object.keys(pythonicConfig)){let pythonicValue=pythonicConfig[pythonicKey];if(pythonicKey==="name"&&typeof pythonicValue=="string")tsDict[pythonicKey]=pythonicValue;else{let tsKey=toCamelCase(pythonicKey);tsDict[tsKey]=convertPythonicToTs(pythonicValue,tsKey)}}return tsDict}}function convertTsToPythonic(tsConfig,key){if(tsConfig==null)return null;if(typeof tsConfig=="string")return toSnakeCase(tsConfig);if(typeof tsConfig=="number"||typeof tsConfig=="boolean")return tsConfig;if(tsConfig instanceof Array){let pyArray=[],arrayLength=tsConfig.length;for(let i=0;i<arrayLength;++i){let item=tsConfig[i];isArrayItemInputOrOutputName(key,i,item)?pyArray.push(item):pyArray.push(convertTsToPythonic(item,key))}return pyArray}else{let pyDict={};for(let tsKey of Object.keys(tsConfig)){let tsValue=tsConfig[tsKey],pyKey=toSnakeCase(tsKey);(tsKey==="name"||tsKey==="className")&&typeof tsValue=="string"?pyDict[pyKey]=tsValue:pyDict[pyKey]=convertTsToPythonic(tsValue,tsKey)}return pyDict}}var version2="2.8.3";function assertFeedCompatibility(key,val){if(key.dtype==null||key.dtype===val.dtype)return val;try{return cast(val,key.dtype)}catch(err){throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`)}}var FeedDict=class{constructor(feeds){if(this.id2Value={},this.id2Mask={},this.name2Id={},feeds instanceof FeedDict)for(let id in feeds.id2Value)this.id2Value[id]=feeds.id2Value[id],id in feeds.id2Mask&&(this.id2Mask[id]=feeds.id2Mask[id]);else{if(feeds==null)return;for(let feed of feeds)this.add(feed.key,feed.value)}}add(key,value,mask){if(this.id2Value[key.id]==null)this.id2Value[key.id]=assertFeedCompatibility(key,value),this.name2Id[key.name]=key.id,mask!=null&&(this.id2Mask[key.id]=mask);else throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);return this}addFeed(feed){this.add(feed.key,feed.value)}hasKey(key){return this.id2Value[key.id]!=null}names(){return Object.keys(this.name2Id)}getValue(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Value[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Value[id]}}getMask(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Mask[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Mask[id]}}disposeMasks(){this.id2Mask!=null&&dispose(this.id2Mask)}},cachedSorted={},cachedRecipientCounts={};function execute(fetches,feedDict,kwargs,probe){let training=kwargs==null?!1:kwargs.training,arrayFetches=Array.isArray(fetches),fetchArray=arrayFetches?fetches:[fetches],outputNames=fetchArray.map(t=>t.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;i<sorted.length;++i){if(probe!=null){let numTensors=memory().numTensors;numTensors>probe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors<probe.minNumTensors&&(probe.minNumTensors=numTensors)}let symbolic=sorted[i],srcLayer=symbolic.sourceLayer;if(srcLayer instanceof InputLayer)continue;let inputValues=[],inputMasks=[],tensorsToDispose=[],maskExists=!1;for(let input2 of symbolic.inputs){let value=internalFeedDict.getValue(input2),mask=internalFeedDict.getMask(input2);inputValues.push(value),inputMasks.push(mask),mask!=null&&(maskExists=!0),training||(recipientCounts[input2.name]--,recipientCounts[input2.name]===0&&!feedDict.hasKey(input2)&&outputNames.indexOf(input2.name)===-1&&!value.isDisposed&&input2.sourceLayer.stateful!==!0&&tensorsToDispose.push(value))}maskExists&&(kwargs=kwargs||{},kwargs.mask=inputMasks[0]);let outputTensors=toList(srcLayer.apply(inputValues,kwargs)),outputMask=null;srcLayer.supportsMasking&&(outputMask=srcLayer.computeMask(inputValues,inputMasks));let layerOutputs=getNodeOutputs(symbolic),outputSymbolicTensors=Array.isArray(layerOutputs)?layerOutputs:[layerOutputs];for(let i2=0;i2<outputSymbolicTensors.length;++i2){internalFeedDict.hasKey(outputSymbolicTensors[i2])||internalFeedDict.add(outputSymbolicTensors[i2],outputTensors[i2],Array.isArray(outputMask)?outputMask[0]:outputMask);let index=outputNames.indexOf(outputSymbolicTensors[i2].name);index!==-1&&(finalOutputs[index]=outputTensors[i2])}training||dispose(tensorsToDispose)}return internalFeedDict.disposeMasks(),arrayFetches?finalOutputs:finalOutputs[0]}function getTopologicalSortAndRecipientCounts(fetches,feedDict){util_exports.assert(fetches!=null&&fetches.length>0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name)&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;i<fetch3.sourceLayer.inboundNodes.length;++i)for(let outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors)if(outputTensor.id===fetch3.id){nodeIndex=i;break}layerOutputs=fetch3.sourceLayer.getOutputAt(nodeIndex)}return layerOutputs}var Container=class extends Layer{constructor(args){super({});if(this.containerNodes=new Set,this.name=args.name,this.name==null){let prefix=this.getClassName().toLowerCase();this.name=getUid(prefix)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(args.inputs)?this.inputs=args.inputs.slice():this.inputs=[args.inputs],Array.isArray(args.outputs)?this.outputs=args.outputs.slice():this.outputs=[args.outputs],unique2(this.inputs).length!==this.inputs.length)throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(x=>x.name)}`);unique2(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i];if(!(layer instanceof InputLayer))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);this.inputNames.push(layer.name),this.feedInputShapes.push(layer.batchInputShape),this.feedInputNames.push(layer.name)}for(let layer of this.outputLayers)this.outputNames.push(layer.name);this.internalInputShapes=this.inputs.map(x=>x.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node2=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node2)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node2)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node2)===-1&&nodesInProgress2.push(node2);let numInboundLayers=node2.inboundLayers.length;for(let i=0;i<numInboundLayers;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],tensorIndex2=node2.tensorIndices[i];buildMapOfGraph(x,finishedNodes2,nodesInProgress2,layer2,nodeIndex2,tensorIndex2)}for(finishedNodes2.push(node2);nodesInProgress2.indexOf(node2)>=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node2),1);nodesInDecreasingDepth.push(node2)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node2 of reversedNodesInDecreasingDepth){nodeIDToNode[node2.id]=node2,node2.id in nodesDepths||(nodesDepths[node2.id]=0);let depth=nodesDepths[node2.id],previousDepth=layersDepths[node2.outboundLayer.id]==null?0:layersDepths[node2.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node2.outboundLayer.id]=depth,layerIDToLayer[node2.outboundLayer.id]=node2.outboundLayer,nodesDepths[node2.id]=depth;for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],inboundNode=inboundLayer.inboundNodes[nodeIndex],previousDepth2=nodesDepths[inboundNode.id]==null?0:nodesDepths[inboundNode.id];nodesDepths[inboundNode.id]=Math.max(depth+1,previousDepth2),nodeIDToNode[inboundNode.id]=inboundNode}}let nodesByDepth={};for(let nodeID in nodesDepths){let depth=nodesDepths[nodeID];depth in nodesByDepth||(nodesByDepth[depth]=[]),nodesByDepth[depth].push(nodeIDToNode[nodeID])}let layersByDepth={};for(let layerID in layersDepths){let depth=layersDepths[layerID];depth in layersByDepth||(layersByDepth[depth]=[]),layersByDepth[depth].push(layerIDToLayer[layerID])}let depthKeys=Object.keys(layersByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndex<bIndex?-1:aIndex>bIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node2 of nodesByDepth[depth]){let layer=node2.outboundLayer;if(layer!=null){for(let x of node2.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node2.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version2}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i]);return execute(this.outputs,feedDict,kwargs)})}computeMask(inputs,mask){return tidy(()=>{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;i<inputShapes.length;i++){let layer=this.inputLayers[i],inputShape2=inputShapes[i],shapeKey=layer.name+"_0_0";layersToOutputShapes[shapeKey]=inputShape2}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;j<node2.inboundLayers.length;j++){let inboundLayer=node2.inboundLayers[j],nodeIndex2=node2.nodeIndices[j],tensorIndex=node2.tensorIndices[j],shapeKey=`${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`,inputShape2=layersToOutputShapes[shapeKey];inputShapes2.push(inputShape2)}let outputShape=layer.computeOutputShape(singletonOrArray(inputShapes2)),outputShapes2=normalizeShapeList(outputShape),nodeIndex=layer.inboundNodes.indexOf(node2);for(let j=0;j<outputShapes2.length;j++){let shapeKey=`${layer.name}_${nodeIndex}_${j}`;layersToOutputShapes[shapeKey]=outputShapes2[j]}}}let outputShapes=[],outputShapeKeys=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],tensorIndex=this.outputLayersTensorIndices[i],shapeKey=`${layer.name}_${nodeIndex}_${tensorIndex}`;outputShapeKeys.push(shapeKey)}for(let i=0;i<outputShapeKeys.length;i++){let key=outputShapeKeys[i];assert2(key in layersToOutputShapes),outputShapes.push(layersToOutputShapes[key])}return singletonOrArray(outputShapes)}runInternalGraph(inputs,masks){masks==null&&(masks=pyListRepeat(null,inputs.length));let tensorMap={};for(let i=0;i<this.inputs.length;++i){let x=this.inputs[i],y=inputs[i],mask=masks[i];tensorMap[x.id]=[y,mask]}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer,referenceInputTensors=node2.inputTensors,referenceOutputTensors=node2.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node2.callArgs!=null&&(kwargs=node2.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i<referenceOutputTensors.length;++i){let x=referenceOutputTensors[i],y=outputTensors2[i],mask=outputMasks2[i];tensorMap[x.id]=[y,mask]}}}}let outputTensors=[],outputMasks=[],outputShapes=[];for(let x of this.outputs){assert2(x.id in tensorMap,`Could not compute output ${x.name} : ${x.id}`);let[tensor2,mask]=tensorMap[x.id];outputShapes.push(tensor2.shape),outputTensors.push(tensor2),outputMasks.push(mask)}return[outputTensors,outputMasks,outputShapes]}buildNodeConversionMap(layers){let nodeConversionMap={},keptNodes;for(let layer of this.layers){keptNodes=layer instanceof Container?1:0;for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let nodeKey=Container.nodeKey(layer,originalNodeIndex);this.containerNodes.has(nodeKey)&&(nodeConversionMap[nodeKey]=keptNodes,keptNodes+=1)}}return nodeConversionMap}getLayer(name,index){if(index!=null){if(this.layers.length<=index)throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);return this.layers[index]}else if(name==null)throw new ValueError("Provide either a layer name or layer index");for(let layer of this.layers)if(layer.name===name)return layer;throw new ValueError(`No such layer: ${name}`)}calculateLosses(){return tidy(()=>{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex<layer.inboundNodes.length;++nodeIndex){let nodeKey=Container.nodeKey(layer,nodeIndex);this.containerNodes.has(nodeKey)&&losses4.push(...layer.calculateLosses())}return losses4})}getConfig(){let config={name:this.name},nodeConversionMap=this.buildNodeConversionMap(this.layers),layerConfigs=[];for(let layer of this.layers){let layerClassName=layer.getClassName(),layerConfig=layer.getConfig(),filteredInboundNodes=[];for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let node2=layer.inboundNodes[originalNodeIndex],nodeKey=Container.nodeKey(layer,originalNodeIndex),kwargs={};if(this.containerNodes.has(nodeKey)){if(node2.callArgs)try{JSON.stringify(node2.callArgs),kwargs=node2.callArgs}catch(err){console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node2.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),kwargs={}}if(node2.inboundLayers.length>0){let nodeData=[];for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],tensorIndex=node2.tensorIndices[i],nodeKey2=Container.nodeKey(inboundLayer,nodeIndex),newNodeIndex=nodeConversionMap[nodeKey2];newNodeIndex==null&&(newNodeIndex=0),nodeData.push([inboundLayer.name,newNodeIndex,tensorIndex,kwargs])}filteredInboundNodes.push(nodeData)}}}let dict={};dict.name=layer.name,dict.className=layerClassName,dict.config=layerConfig,dict.inboundNodes=filteredInboundNodes,layerConfigs.push(dict)}config.layers=layerConfigs;let modelInputs=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i],nodeIndex=this.inputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.inputLayersTensorIndices[i];modelInputs.push([layer.name,newNodeIndex,tensorIndex])}config.inputLayers=modelInputs;let modelOutputs=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.outputLayersTensorIndices[i];modelOutputs.push([layer.name,newNodeIndex,tensorIndex])}return config.outputLayers=modelOutputs,config}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let createdLayers={},unprocessedNodes={};function addUnprocessedNode(layer,nodeData){layer.name in unprocessedNodes?unprocessedNodes[layer.name].push(nodeData):unprocessedNodes[layer.name]=[nodeData]}function processNode(layer,nodeData){let inputTensors2=[],kwargs;for(let inputData of nodeData){let inboundLayerName=inputData[0],inboundNodeIndex=inputData[1],inboundTensorIndex=inputData[2];if(kwargs=inputData[3]==null?{}:inputData[3],!(inboundLayerName in createdLayers)){addUnprocessedNode(layer,nodeData);return}let inboundLayer=createdLayers[inboundLayerName];if(inboundLayer.inboundNodes.length<=inboundNodeIndex){addUnprocessedNode(layer,nodeData);return}let inboundNode=inboundLayer.inboundNodes[inboundNodeIndex];inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex])}inputTensors2.length>0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config.customObjects!=null?config.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config.name,layersFromConfig=config.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex<flattenedXs.length;xIndex++)util_exports.assert(flattenedXs[xIndex].shape[0]===batchSize,()=>`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex<flattenedYs.length;yIndex++)util_exports.assert(flattenedYs[yIndex].shape[0]===batchSize,()=>`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data){if(data.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data[0],ys:data[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch<args.epochs;){let epochLogs={};await callbackList.onEpochBegin(epoch);let stepsDone=0,batchIndex=0;for(hasBatchesPerEpoch||(dataIterator=await dataset.iterator());hasBatchesPerEpoch?stepsDone<args.batchesPerEpoch:!0;){let iteratorOut=await dataIterator.next();if(hasBatchesPerEpoch&&iteratorOut.done){console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch*args.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(iteratorOut.value!=null){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),batchLogs={};batchLogs.batch=batchIndex,batchLogs.size=xs[0].shape[0],await callbackList.onBatchBegin(batchIndex,batchLogs);let sampleWeights=[];if(args.classWeight!=null){let standardClassWeights=standardizeClassWeights(args.classWeight,model2.outputNames);for(let i=0;i<standardClassWeights.length;++i)sampleWeights.push(await standardizeWeights(ys[i],null,standardClassWeights[i]))}let ins=xs.concat(ys).concat(sampleWeights),outs=trainFunction(ins);dispose(ins);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),batchIndex++,stepsDone++}if(hasBatchesPerEpoch?stepsDone>=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i<model2.metricsNames.length;++i)epochLogs[`val_${model2.metricsNames[i]}`]=valOuts[i]}break}if(model2.stopTraining_)break}if(await callbackList.onEpochEnd(epoch,epochLogs),epoch++,model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}finally{model2.isTraining=!1}}function getStepsPerEpoch(dataset,args){let stepsPerEpoch=null;return args.batchesPerEpoch!=null?stepsPerEpoch=args.batchesPerEpoch:Number.isFinite(dataset.size)&&(stepsPerEpoch=dataset.size),stepsPerEpoch}function isDatasetObject(dataset){return typeof dataset.iterator=="function"}function isLazyIteratorObject(iterator){return typeof iterator.next=="function"}async function evaluateDataset(model2,dataset,args){args=args||{};let hasBatches=args.batches!=null,f=model2.testFunction,outs=[];if(args.verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch<args.batches:!0;){let iteratorOut=await dataIterator.next();if(outs=tidy(()=>{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));let batchSize=xsAndYs[0].shape[0];for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i],oldScalar=outs[i];outs[i]=tidy(()=>add2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i<outs.length;++i){let oldScalar=outs[i];outs[i]=div(outs[i],numExamples),dispose(oldScalar)}return singletonOrArray(outs)}function checkBatchSize(batchSize){util_exports.assert(batchSize>0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart<size;)batchEnd=batchStart+batchSize,batchEnd>=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range2(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch<epochs;++epoch){await callbackList.onEpochBegin(epoch);let epochLogs={};if(stepsPerEpoch!=null)throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");{if(shuffle2==="batch")throw new NotImplementedError("batch shuffling is not implemneted yet");shuffle2&&util_exports.shuffle(indexArray);let epochIndexArray1D=tensor1d(indexArray),batches=makeBatches(numTrainSamples,batchSize);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchLogs={};if(await callbackList.onBatchBegin(batchIndex,batchLogs),tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}if(batchIndex===batches.length-1&&doValidation){let valOuts=model2.testLoop(valF,valIns,batchSize);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=valOuts[i];keep(out),epochLogs["val_"+label]=out}}}),await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),model2.stopTraining_)break}epochIndexArray1D.dispose()}if(await callbackList.onEpochEnd(epoch,epochLogs),model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}async function fitTensors(model2,x,y,args={}){if(model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;let inputs,targets,inputValX,inputValY,valX,valY,sampleWeights;try{let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!1,standardizedOuts=await model2.standardizeUserData(x,y,args.sampleWeight,args.classWeight,checkBatchAxis,batchSize);inputs=standardizedOuts[0],targets=standardizedOuts[1],sampleWeights=standardizedOuts[2];let doValidation=!1,valIns;if(args.validationData!=null&&args.validationData.length>0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;i<tensors.length;++i){let tensor2=tensors[i];if(tensor2.rank===1)outs.push(expandDims2(tensor2,1));else{if(tensor2.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");outs.push(tensor2)}}return outs}function disposeNewTensors(tensors,refTensors){if(tensors==null)return;let oldTensorIds=[];if(refTensors instanceof Tensor)oldTensorIds.push(refTensors.id);else if(Array.isArray(refTensors))refTensors.forEach(t=>oldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data!=null){let gotUnexpectedData=!1;if(isDataArray(data)&&data.length>0)gotUnexpectedData=!0;else if(isDataDict(data)){for(let key in data)if(data.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`)}return[]}if(data==null)return names.map(name=>null);let arrays;if(isDataDict(data)){data=data,arrays=[];for(let name of names){if(data[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data[name])}}else if(isDataArray(data)){if(data=data,data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);arrays=data}else{if(data=data,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);arrays=[data]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim>=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique2(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique2(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i<targets.length;++i){let y=targets[i],loss=lossFns[i],shape=outputShapes[i];if(loss!=null){if(loss===categoricalCrossentropy&&y.shape[y.shape.length-1]===1)throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(keyLosses.indexOf(loss)!==-1){let slicedYShape=y.shape.slice(1),slicedShape=shape.slice(1);for(let j=0;j<slicedYShape.length;++j){let targetDim=slicedYShape[j],outDim=slicedShape[j];if(outDim!=null&&targetDim!==outDim)throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function checkInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){let arrays;if(Array.isArray(data)){if(data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);arrays=data}else{if(names.length>1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);arrays=[data]}if(shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim!==dim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`)}}}function collectMetrics(metrics2,outputNames){if(metrics2==null||Array.isArray(metrics2)&&metrics2.length===0)return outputNames.map(name=>[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let shape=this.internalOutputShapes[i],name=this.outputNames[i];this.feedOutputNames.push(name),this.feedOutputShapes.push(shape),this.feedLossFns.push(this.lossFunctions[i])}let skipTargetIndices=[];this.metrics=args.metrics,this.metricsNames=["loss"],this.metricsTensors=[],nameScope("loss",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let weightedLoss=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let outputMetrics=nestedMetrics[i];(metrics2=>{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i])}else for(let input2 of this.inputs){let tensorValue=inputs[input2.name];if(tensorValue==null)throw new ValueError(`No value is provided for the model's input ${input2.name}`);feedDict.add(input2,tensorValue)}let executeOutputs=execute(outputSymbolicTensors,feedDict);return outputsIsArray?executeOutputs:executeOutputs[0]}retrieveSymbolicTensors(symbolicTensorNames){let outputSymbolicTensors=pyListRepeat(null,symbolicTensorNames.length),outputsRemaining=symbolicTensorNames.length;for(let layer of this.layers){let layerOutputs=Array.isArray(layer.output)?layer.output:[layer.output],layerOutputNames=layerOutputs.map(output=>output.name);for(let i=0;i<symbolicTensorNames.length;++i){let index=layerOutputNames.indexOf(symbolicTensorNames[i]);if(index!==-1&&(outputSymbolicTensors[i]=layerOutputs[index],outputsRemaining--),outputsRemaining===0)break}if(outputsRemaining===0)break}if(outputsRemaining>0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex<batches.length;++batchIndex)tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;i<insBatch.length;++i)feeds.push({key:this.inputs[i],value:insBatch[i]});else feeds.push({key:this.inputs[0],value:insBatch});let feedDict=new FeedDict(feeds);return execute(this.outputs,feedDict)}).forEach((batchOut,i)=>outsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i<this.feedOutputShapes.length;++i){let outputShape=this.feedOutputShapes[i];this.feedLossFns[i]===sparseCategoricalCrossentropy?outputShapes.push(outputShape.slice(0,outputShape.length-1).concat([1])):outputShapes.push(outputShape)}if(x=standardizeInputData(x,this.feedInputNames,this.feedInputShapes,!1,"input"),y=standardizeInputData(y,this.feedOutputNames,outputShapes,!1,"target"),checkArrayLengths(x,y,null),checkLossAndTargetCompatibility(y,this.feedLossFns,this.feedOutputShapes),this.stateful&&batchSize!=null&&batchSize>0&&x[0].shape[0]%batchSize!=0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i<classWeights.length;++i)standardSampleWeights.push(await standardizeWeights(standardYs[i],null,classWeights[i]))}return[standardXs,standardYs,standardSampleWeights]}testLoop(f,ins,batchSize,verbose=0,steps){return tidy(()=>{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range2(0,numSamples));for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(indexArray,batchStart,batchEnd-batchStart),insBatch=sliceArraysByIndices(ins,batchIds),batchOuts=f(insBatch);if(batchIndex===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i];outs[i]=add2(outs[i],mul(batchEnd-batchStart,batchOut))}}for(let i=0;i<outs.length;++i)outs[i]=div(outs[i],numSamples)}return outs})}getDedupedMetricsNames(){let outLabels=this.metricsNames,dedupedOutLabels=[];for(let i=0;i<outLabels.length;++i){let label=outLabels[i],newLabel=label;count(outLabels,label)>1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data=>{let lossValues=[],inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict,{training:!0}),totalLoss;for(let i=0;i<this.lossFunctions.length;++i){let loss=this.lossFunctions[i](targets[i],outputs[i]);sampleWeights[i]!=null&&(loss=computeWeightedLoss2(loss,sampleWeights[i]));let meanLoss=mean(loss);lossValues.push(meanLoss),i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss)}for(let i=0;i<this.metricsTensors.length;++i){let weightedMetric;if(this.outputs.length>1&&i<this.outputs.length)weightedMetric=lossValues[i];else{let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1];weightedMetric=mean(metric(targets[outputIndex],outputs[outputIndex]))}keep(weightedMetric),metricsValues.push(weightedMetric)}return totalLoss=mean(totalLoss),this.calculateLosses().forEach(regularizerLoss=>{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict);for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=mean(lossFunction(targets[i],outputs[i]));i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss),valOutputs.push(totalLoss)}for(let i=0;i<this.metricsTensors.length;++i){let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1],meanMetric=mean(metric(targets[outputIndex],outputs[outputIndex]));valOutputs.push(meanMetric)}return valOutputs})}async fit(x,y,args={}){return fitTensors(this,x,y,args)}async fitDataset(dataset,args){return fitDataset(this,dataset,args)}async trainOnBatch(x,y){let standardizeOut=await this.standardizeUserData(x,y),inputs=standardizeOut[0],targets=standardizeOut[1],losses4=this.makeTrainFunction()(inputs.concat(targets)),lossValues=[];for(let loss of losses4){let v=await loss.data();lossValues.push(v[0])}return dispose(losses4),singletonOrArray(lossValues)}getNamedWeights(config){let namedWeights=[],trainableOnly=config!=null&&config.trainableOnly,weights=trainableOnly?this.trainableWeights:this.weights,weightValues=this.getWeights(trainableOnly);for(let i=0;i<weights.length;++i)trainableOnly&&!weights[i].trainable||namedWeights.push({name:weights[i].originalName,tensor:weightValues[i]});return namedWeights}set stopTraining(stop){this.stopTraining_=stop}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(optimizer){this.optimizer_!==optimizer&&(this.optimizer_=optimizer,this.isOptimizerOwned=!1)}dispose(){let result=super.dispose();if(result.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let numTensorsBeforeOptmizerDisposal=memory().numTensors;this.optimizer_.dispose(),result.numDisposedVariables+=numTensorsBeforeOptmizerDisposal-memory().numTensors}return result}getLossIdentifiers(){let lossNames;if(typeof this.loss=="string")lossNames=toSnakeCase(this.loss);else if(Array.isArray(this.loss)){for(let loss of this.loss)if(typeof loss!="string")throw new Error("Serialization of non-string loss is not supported.");lossNames=this.loss.map(name=>toSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version2}`,convertedBy:null};if((config==null?!1:config.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config instanceof Array){if(config[0].className==null||config[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config}else util_exports.assert(config.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config.layers,delete config.layers,extraModelConfig=config;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config){return new Sequential(config)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config){return Input(config)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu2(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config={};return config.className="linear",config.config={},deserializeActivation(config)}if(typeof identifier=="string"){let config={};return config.className=identifier,config.config={},deserializeActivation(config)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square2(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config){return new cls({l1:config.l1,l2:config.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i<inputShape.length;++i)axes[i]=inputShape[i];this.inputSpec=[new InputSpec({ndim:inputShape.length,axes})],this.built=!0}call(inputs,kwargs){return inputs=getExactlyOneTensor(inputs),prelu(inputs,this.alpha.read())}getConfig(){let config={alphaInitializer:serializeInitializer(this.alphaInitializer),alphaRegularizer:serializeRegularizer(this.alphaRegularizer),alphaConstraint:serializeConstraint(this.alphaConstraint),sharedAxes:this.sharedAxes},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};PReLU.className="PReLU";serialization_exports.registerClass(PReLU);var ELU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA=1,args==null&&(args={}),args.alpha!=null&&args.alpha!==this.DEFAULT_ALPHA)throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return elu(x)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ELU.className="ELU";serialization_exports.registerClass(ELU);var ThresholdedReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_THETA=1,args==null&&(args={}),this.theta=args.theta==null?this.DEFAULT_THETA:args.theta}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return x.mul(cast2(x.greater(this.theta),"float32"))}computeOutputShape(inputShape){return inputShape}getConfig(){let config={theta:this.theta},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ThresholdedReLU.className="ThresholdedReLU";serialization_exports.registerClass(ThresholdedReLU);var Softmax3=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_AXIS=1,args==null&&(args={}),this.softmax=new Softmax2().apply,this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return this.softmax(x,this.axis)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Softmax3.className="Softmax";serialization_exports.registerClass(Softmax3);function normalizeArray(value,n,name){if(typeof value=="number")return pyListRepeat(value,n);if(value.length!==n)throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);for(let i=0;i<n;++i){let singleValue=value[i];if(!isInteger(singleValue))throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`)}return value}function convOutputLength(inputLength,filterSize,padding,stride,dilation=1){if(inputLength==null)return inputLength;let dilatedFilterSize=filterSize+(filterSize-1)*(dilation-1),outputLength;return padding==="same"?outputLength=inputLength:outputLength=inputLength-dilatedFilterSize+1,Math.floor((outputLength+stride-1)/stride)}function deconvLength(dimSize,strideSize,kernelSize,padding){if(dimSize==null)return null;if(padding==="valid")dimSize=dimSize*strideSize+max2([kernelSize-strideSize,0]);else if(padding==="same")dimSize=dimSize*strideSize;else throw new ValueError(`Unsupport padding mode: ${padding}.`);return dimSize}function preprocessConv2DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i<space.length;++i){let newDim=convOutputLength(space[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);newSpace.push(newDim)}let outputShape=[inputShape[0]];return this.dataFormat==="channelsLast"?(outputShape=outputShape.concat(newSpace),outputShape.push(this.filters)):(outputShape.push(this.filters),outputShape=outputShape.concat(newSpace)),outputShape}getConfig(){let config={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static verifyArgs(args){if(!("filters"in args)||typeof args.filters!="number"||args.filters<1)throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config=super.getConfig();return delete config.dilationRate,config}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config){super(rank,config);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config.kernelInitializer!=null||config.kernelRegularizer!=null||config.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config.padding!=null&&config.padding!=="same"&&config.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);this.depthMultiplier=config.depthMultiplier==null?1:config.depthMultiplier,this.depthwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<this.rank+2)throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(inputShape)}`);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=this.kernelSize.concat([inputDim,this.depthMultiplier]),pointwiseKernelShape=[];for(let i=0;i<this.rank;++i)pointwiseKernelShape.push(1);pointwiseKernelShape.push(inputDim*this.depthMultiplier,this.filters);let trainable=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,trainable,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",pointwiseKernelShape,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,trainable,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,trainable,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config=super.getConfig();return delete config.rank,delete config.kernelInitializer,delete config.kernelRegularizer,delete config.kernelConstraint,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config=super.getConfig();return delete config.rank,delete config.dataFormat,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d3(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d3(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config=super.getConfig();return config.depthMultiplier=this.depthMultiplier,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range2(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;t<timeSteps;++t){let currentInput=perStepInputs[t],stepOutputs=tidy(()=>stepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range2(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;i<numStates;++i)output.push(null);return output}else return this.states_}set states(s){this.states_=s}build(inputShape){let constantShape=null;if(this.numConstants!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let batchSize=this.stateful?inputShape[0]:null,inputDim=inputShape.slice(2);this.inputSpec[0]=new InputSpec({shape:[batchSize,null,...inputDim]});let stepInputShape=[inputShape[0]].concat(inputShape.slice(2));if(constantShape!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");this.cell.build(stepInputShape);let stateSize;if(Array.isArray(this.cell.stateSize)?stateSize=this.cell.stateSize:stateSize=[this.cell.stateSize],this.stateSpec!=null){if(!util_exports.arraysEqual(this.stateSpec.map(spec=>spec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],dim=Array.isArray(this.cell.stateSize)?this.cell.stateSize[index]:this.cell.stateSize,expectedShape=[batchSize,dim];if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims2(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile2(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile2(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cellConfig=config.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return new cls(config)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let matrixX=dot2(inputs,this.kernel.read());this.useBias&&(matrixX=biasAdd(matrixX,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0]));let recurrentKernelValue=this.recurrentKernel.read(),[rk1,rk2]=split(recurrentKernelValue,[2*this.units,this.units],recurrentKernelValue.rank-1),matrixInner=dot2(hTMinus1,rk1),[xZ,xR,xH]=split(matrixX,3,matrixX.rank-1),[recurrentZ,recurrentR]=split(matrixInner,2,matrixInner.rank-1);z=this.recurrentActivation.apply(add2(xZ,recurrentZ)),r=this.recurrentActivation.apply(add2(xR,recurrentR));let recurrentH=dot2(mul(r,hTMinus1),rk2);hh=this.activation.apply(add2(xH,recurrentH));let h=add2(mul(z,hTMinus1),mul(add2(1,neg(z)),hh));return[h,h]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},baseConfig,config)}};GRUCell.className="GRUCell";serialization_exports.registerClass(GRUCell);var GRU=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new GRUCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let z=dot2(inputs,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0])),z=add2(z,dot2(hTMinus1,this.recurrentKernel.read())),this.useBias&&(z=biasAdd(z,this.bias.read()));let[z0,z1,z2,z3]=split(z,4,z.rank-1);i=this.recurrentActivation.apply(z0),f=this.recurrentActivation.apply(z1),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(z2))),o=this.recurrentActivation.apply(z3);let h=mul(o,this.activation.apply(c));return[h,h,c]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},baseConfig,config)}};LSTMCell.className="LSTMCell";serialization_exports.registerClass(LSTMCell);var LSTM=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new LSTMCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i<this.cells.length;++i){let cell=this.cells[i];states=nestedStates[i],i===0?callInputs=[inputs[0]].concat(states):callInputs=[callInputs[0]].concat(states),callInputs=cell.call(callInputs,kwargs),newNestedStates.push(callInputs.slice(1))}states=[];for(let cellStates of newNestedStates.slice().reverse())states.push(...cellStates);return[callInputs[0]].concat(states)})}build(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let outputDim;this.cells.forEach((cell,i)=>{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cells=[];for(let cellConfig of config.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;i<cell.weights.length;++i)tuples.push([cell.weights[i],inputWeights[i]])}batchSetValue(tuples)}};StackedRNNCells.className="StackedRNNCells";serialization_exports.registerClass(StackedRNNCells);function generateDropoutMask(args){let{ones:ones4,rate,training=!1,count:count2=1}=args,droppedInputs=()=>dropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i<p2.length;i++)e.indexOf(p2[i])<0&&Object.prototype.propertyIsEnumerable.call(s,p2[i])&&(t[p2[i]]=s[p2[i]]);return t},ConvRNN2D=class extends RNN{constructor(args){if(args.unroll)throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(args.cell))throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(args);this.inputSpec=[new InputSpec({ndim:5})]}call(inputs,kwargs){return tidy(()=>{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],expectedShape=stateShape;if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config){return new cls(config)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i<this.noiseShape.length;++i)noiseShape.push(this.noiseShape[i]==null?inputShape[i]:this.noiseShape[i]);return noiseShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0<this.rate&&this.rate<1){let training=kwargs.training==null?!1:kwargs.training,noiseShape=this.getNoiseShape(input2);return inTrainPhase(()=>dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i<input2.rank;++i)permutation.push(i);permutation.push(1),input2=input2.transpose(permutation)}return batchFlatten(input2)})}getConfig(){let config={};this.dataFormat!=null&&(config.dataFormat=this.dataFormat);let baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Flatten.className="Flatten";serialization_exports.registerClass(Flatten);var Activation2=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.activation=getActivation(args.activation)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config={n:this.n},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i<this.targetShape.length;++i)this.isUnknown(this.targetShape[i])&&(this.targetShape[i]=null)}isUnknown(dim){return dim<0||dim==null}fixUnknownDimension(inputShape,outputShape){let errorMsg="Total size of new array must be unchanged.",finalShape=outputShape.slice(),known=1,unknown=null;for(let i=0;i<finalShape.length;++i){let dim=finalShape[i];if(this.isUnknown(dim))if(unknown===null)unknown=i;else throw new ValueError("Can only specifiy one unknown dimension.");else known*=dim}let originalSize=arrayProd(inputShape);if(unknown!==null){if(known===0||originalSize%known!=0)throw new ValueError(errorMsg);finalShape[unknown]=originalSize/known}else if(originalSize!==known)throw new ValueError(errorMsg);return finalShape}computeOutputShape(inputShape){let anyUnknownDims=!1;for(let i=0;i<inputShape.length;++i)if(this.isUnknown(inputShape[i])){anyUnknownDims=!0;break}return anyUnknownDims?inputShape.slice(0,1).concat(this.targetShape):inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape))}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range2(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={maskValue:this.maskValue};return Object.assign(config,baseConfig),config}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k<inLens.length;++k){let s1=inLens[k],s2=inputShape[k+1];if(s1!=null&&s2!=null&&s1!==s2)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);s1==null&&(inLens[i]=s2),i++}}return[inputShape[0],...inLens,this.outputDim]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast2(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length<shape2.length)return this.computeElementwiseOpOutputShape(shape2,shape1);if(shape2.length===0)return shape1;let outputShape=shape1.slice(0,shape1.length-shape2.length);for(let k=0;k<shape2.length;++k){let i=shape1[shape1.length-shape2.length+k],j=shape2[k];if(i==null||j==null||i<0||j<0)outputShape.push(null);else if(i===1)outputShape.push(j);else if(j===1)outputShape.push(i);else{if(i!==j)throw new ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(shape1)+" "+JSON.stringify(shape2));outputShape.push(i)}}return outputShape}build(inputShape){if(Array.isArray(inputShape)&&!Array.isArray(inputShape[0])&&(inputShape=[getExactlyOneShape(inputShape)]),inputShape=inputShape,inputShape.length<2)throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);if(batchSizes=unique2(batchSizes),batchSizes.length>1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let allRanks=inputShape.map(shape=>shape.length);inputShape.indexOf(null)===-1&&unique2(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max2(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k<maxNDim-xNDim;++k)x=expandDims2(x,1);reshapedInputs.push(x)}return this.mergeFunction(reshapedInputs)}else{let transposed=!1;for(let x of inputs){let xNDim=x.rank;if(xNDim==null){let xShape=x.shape,batchSize=xShape[0],newShape=xShape.slice(1).concat([batchSize]),xTransposed=x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));xTransposed=transpose(xTransposed,[1,0]),xTransposed=xTransposed.reshape(newShape),reshapedInputs.push(xTransposed),transposed=!0}else if(xNDim>1){let dims=range2(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range2(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);return batchSizes=unique2(batchSizes),batchSizes.length===1?outputShape=batchSizes.concat(outputShape):outputShape=[null].concat(outputShape),outputShape}computeMask(inputs,mask){return tidy(()=>{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i<mask.length-1;++i)output=logicalAnd(output,mask[i]);return output})}},Add2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return output})}};Add2.className="Add";serialization_exports.registerClass(Add2);var Multiply2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=mul(output,inputs[i]);return output})}};Multiply2.className="Multiply";serialization_exports.registerClass(Multiply2);var Average=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return mul(1/inputs.length,output)})}};Average.className="Average";serialization_exports.registerClass(Average);var Maximum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=maximum(output,inputs[i]);return output})}};Maximum2.className="Maximum";serialization_exports.registerClass(Maximum2);var Minimum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=minimum(output,inputs[i]);return output})}};Minimum2.className="Minimum";serialization_exports.registerClass(Minimum2);var Concatenate=class extends Merge{constructor(args){super(args);this.DEFAULT_AXIS=-1,args==null&&(args={}),this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0]))||inputShape.length===1)throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");inputShape=inputShape;let allNoneShape=!0;for(let shape of inputShape)if(shape!=null){allNoneShape=!1;break}if(allNoneShape)return;let shapeSet=[];for(let i=0;i<inputShape.length;++i){let shapeWithoutConcatAxis=inputShape[i].slice();shapeWithoutConcatAxis.splice(this.axis,1);let exists=!1;for(let shape of shapeSet)if(util_exports.arraysEqual(shape,shapeWithoutConcatAxis)){exists=!0;break}exists||shapeSet.push(shapeWithoutConcatAxis)}if(shapeSet.length>1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i<inputs.length;++i)mask[i]==null?outputMasks.push(onesLike(inputs[i]).asType("bool")):mask[i].rank<inputs[i].rank?outputMasks.push(expandDims(mask[i],-1)):outputMasks.push(mask[i]);let concatenatedMasks=concat(outputMasks,this.axis);return all(concatenatedMasks,-1,!1)})}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Concatenate.className="Concatenate";serialization_exports.registerClass(Concatenate);function interpretAxis(axis,dim){for(;axis<0;)axis+=dim;return axis}function batchDot(x,y,axes){if(x.shape.length>3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);y=y.reshape(y.shape.concat(diffShape))}else if(yNDim>xNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);x=x.reshape(x.shape.concat(diffShape))}else diff=0;let out;if(x.shape.length===2&&y.shape.length===2)axesArray[0]===axesArray[1]?out=x.mul(y).sum(axesArray[0]):out=x.transpose([1,0]).mul(y).sum(axesArray[1]);else{let adjX=axesArray[0]!==x.shape.length-1,adjY=axesArray[1]===y.shape.length-1;out=x.matMul(y,adjX,adjY)}if(diff>0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i<idx+diff;++i)squeezeAxes.push(i);out=out.squeeze(squeezeAxes)}return out.shape.length===1&&(out=out.expandDims(1)),out})}var Dot=class extends Merge{constructor(args){super(args);this.axes=args.axes,this.normalize=args.normalize==null?!1:args.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={stddev:this.stddev};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale22=1.0507009873554805,alphaP=-alpha*scale22,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast2(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon32=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon32);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon32);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon32);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon32),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range2(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon32),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range2(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range2(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range2(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=nDims);for(let axis of this.axis)if(axis<0||axis>=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique2(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale22=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i<nDims;++i)this.axis.indexOf(i)!==-1?(momentsTiling.push(inputShape[i]),scaleOffsetTiling.push(1)):(momentsTiling.push(1),scaleOffsetTiling.push(inputShape[i]));return mean4=mean4.tile(momentsTiling),variance=variance.tile(momentsTiling),scale22=scale22.tile(scaleOffsetTiling),offset=offset.tile(scaleOffsetTiling),batchNormalization(input2,mean4,variance,offset,scale22,this.epsilon)})}getConfig(){let config={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LayerNormalization.className="LayerNormalization";serialization_exports.registerClass(LayerNormalization);function spatial2dPadding(x,padding,dataFormat){return tidy(()=>{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims2(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config,customObjects={}){let layerConfig=config.layer,layer=deserialize(layerConfig,customObjects);delete config.layer;let newConfig={layer};return Object.assign(newConfig,config),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static fromConfig(cls,config){let rnnLayer=deserialize(config.layer);if(delete config.layer,config.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu3(args){return new ELU(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu2(args){return new PReLU(args)}function softmax2(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d3(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d4(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape2(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add3(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum2(args){return new Maximum2(args)}function minimum2(args){return new Minimum2(args)}function multiply(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config){return new L1L2(config)}function l12(config){return l1(config)}function l22(config){return l2(config)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less2(currVal,prevVal){return currVal<prevVal}function greater2(currVal,prevVal){return currVal>prevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less2:this.mode==="max"?this.monitorFunc=greater2:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater2:this.monitorFunc=less2,this.monitorFunc===less2&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less2?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node2,tensorMap,context,resourceManager){let inputParam=node2.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node2.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node2.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node2.inputNames.slice(start)[0],tensorMap,context,resourceManager),data=tensor2.dataSync();return inputParam.type==="number"?data[0]:util_exports.toNestedArray(tensor2.shape,data)}let attrParam=node2.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node2,tensorMap,context){let pad3=getParamValue("pad",node2,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node2,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var 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json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],reduction_exports={};__export2(reduction_exports,{json:()=>json14});var json14=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],slice_join_exports={};__export2(slice_join_exports,{json:()=>json15});var json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],spectral_exports={};__export2(spectral_exports,{json:()=>json16});var json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],transformation_exports={};__export2(transformation_exports,{json:()=>json17});var json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op.startsWith("Placeholder")?placeholders.push(map[node2.name]):node2.op==="Const"?weights.push(map[node2.name]):(node2.input==null||node2.input.length===0)&&initNodes.push(map[node2.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node2=nodes[key];node2.children.length===0&&outputs.push(node2)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2!=null&&(node2.signatureKey=outputNodeNameToKey[name],outputs.push(node2))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2&&(node2.signatureKey=inputNodeNameToKey[name],inputs.push(node2))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node2){let mapper=getRegisteredOp(node2.op)||this.opMappers[node2.op]||{};node2.attr==null&&(node2.attr={});let newNode={name:node2.name,op:node2.op,category:mapper.category,inputNames:(node2.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node2.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node2.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node2.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op==="Const"&&weights.push(map[node2.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node2={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node2.signatureKey=arg.name,inputs.push(node2),nodes[nodeName]=node2}),Object.keys(nodes).forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node2=nodes[nodeName];node2!=null&&(node2.defaultOutput=index,outputs.push(node2))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){switch(typeof value=="string"&&(value=DataType[value]),value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&&param.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&&param.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&&param.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&&param.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&&param.list&&param.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node2,tensorMap,context){this.node=node2,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node2.inputNames.map(name=>this.getInput(name)),node2.rawAttrs!=null&&(this.attrs=Object.keys(node2.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node2,tensorMap,context)=>{switch(node2.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node2,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp2=(node2,tensorMap,context)=>{switch(node2.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node2,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node2,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node2,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node2,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node2,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node2,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node2,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node2,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node2,tensorMap,context),getParamValue("imag",node2,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node2,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node2,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node2,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node2,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node2,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node2,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node2,tensorMap,context))];case"Log":return[log(getParamValue("x",node2,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node2,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node2,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node2,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node2,tensorMap,context))];case"Real":return[real(getParamValue("x",node2,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node2,tensorMap,context))];case"Round":return[round2(getParamValue("x",node2,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node2,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node2,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node2,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node2,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node2,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node2,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node2,tensorMap,context))];case"Square":return[square(getParamValue("x",node2,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node2,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node2,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node2,tensorMap,context),getParamValue("clipValueMin",node2,tensorMap,context),getParamValue("clipValueMax",node2,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node2,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node2.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node2,tensorMap,context),getParamValue("axes",node2,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==-1&&n2[i]!==-1&&n1[i]!==n2[i])return!1;return!0}var TensorArray=class{constructor(name,dtype,maxSize,elementShape,identicalElementShapes,dynamicSize,clearAfterRead){this.name=name,this.dtype=dtype,this.maxSize=maxSize,this.elementShape=elementShape,this.identicalElementShapes=identicalElementShapes,this.dynamicSize=dynamicSize,this.clearAfterRead=clearAfterRead,this.tensors=[],this.closed_=!1,this.idTensor=scalar(0),keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
           because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=tensor2.shape),assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${index}.`),t.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);if(t.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);t.tensor=tensor2,keep(tensor2),t.written=!0,this.tensors[index]=t}writeMany(indices,tensors){if(indices.length!==tensors.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);indices.forEach((i,index)=>this.write(i,tensors[index]))}gather(indices,dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);if(indices)indices=indices.slice(0,this.size());else{indices=[];for(let i=0;i<this.size();i++)indices.push(i)}if(indices.length===0)return tensor([],[0].concat(this.elementShape));let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,"TensorArray shape mismatch: "),stack(tensors,0)}concat(dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);if(this.size()===0)return tensor([],[0].concat(this.elementShape));let indices=[];for(let i=0;i<this.size();i++)indices.push(i);let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`),concat(tensors,0)}scatter(indices,tensor2){if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);if(indices.length!==tensor2.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);let maxIndex=Math.max(...indices);if(!this.dynamicSize&&maxIndex>=this.maxSize)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);this.writeMany(indices,unstack(tensor2,0))}split(length,tensor2){if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
         ${totalLength}, and tensor's shape is: ${tensor2.shape}`);if(!this.dynamicSize&&length.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=[];tidy(()=>{tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices2=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors[i]=reshape(slice(tensor2,indices2,sizes),this.elementShape)}return tensors});let indices=[];for(let i=0;i<length.length;i++)indices[i]=i;this.writeMany(indices,tensors)}},TensorList=class{constructor(tensors,elementShape,elementDtype,maxNumElements=-1){this.tensors=tensors,this.elementShape=elementShape,this.elementDtype=elementDtype,tensors!=null&&tensors.forEach(tensor2=>{if(elementDtype!==tensor2.dtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);assertShapesMatchAllowUndefinedSize(elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2)}),this.idTensor=scalar(0),this.maxNumElements=maxNumElements,keep(this.idTensor)}get id(){return this.idTensor.id}copy(){return new TensorList([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.id))&&tensor2.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(elementShape,elementDtype,numElements=-1){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(numElements!==-1&&this.tensors.length!==numElements)throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);return assertShapesMatchAllowUndefinedSize(elementShape,this.elementShape,"TensorList shape mismatch: "),tidy(()=>{let reshapedTensors=this.tensors.map(tensor2=>reshape(tensor2,elementShape));return stack(reshapedTensors,0)})}popBack(elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let tensor2=this.tensors.pop();return assertShapesMatchAllowUndefinedSize(tensor2.shape,elementShape,"TensorList shape mismatch: "),reshape(tensor2,elementShape)}pushBack(tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(assertShapesMatchAllowUndefinedSize(tensor2.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");keep(tensor2),this.tensors.push(tensor2)}resize(size){if(size<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);if(this.maxNumElements!==-1&&size>this.maxNumElements)throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=size}getItem(elementIndex,elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||elementIndex>this.tensors.length)throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);if(this.tensors[elementIndex]==null)throw new Error(`element at index ${elementIndex} is null.`);return assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape,elementShape,"TensorList shape mismatch: "),this.tensors[elementIndex]}setItem(elementIndex,tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||this.maxNumElements!==-1&&elementIndex>=this.maxNumElements)throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2),this.tensors[elementIndex]=tensor2}gather(indices,elementDtype,elementShape){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),indices=indices.slice(0,this.size()),indices.length===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=indices.map(i=>reshape(this.tensors[i],elementShape));return stack(tensors,0)})}concat(elementDtype,elementShape){if(!!elementDtype&&elementDtype!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),this.size()===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=this.tensors.map(t=>reshape(t,elementShape));return concat(tensors,0)})}};function fromTensor(tensor2,elementShape,elementDtype){let dtype=tensor2.dtype;if(tensor2.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);if(tensor2.dtype!==elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);let outputShape=tensor2.shape.slice(1);assertShapesMatchAllowUndefinedSize(outputShape,elementShape,"TensorList shape mismatch: ");let tensorList=unstack(tensor2);return new TensorList(tensorList,elementShape,dtype)}function reserve(elementShape,elementDtype,numElements){return new TensorList([],elementShape,elementDtype,numElements)}function scatter(tensor2,indices,elementShape,numElements){if(indices.length!==tensor2.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);let maxIndex=Math.max(...indices);if(numElements!=null&&numElements!==-1&&maxIndex>=numElements)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);let list=new TensorList([],elementShape,tensor2.dtype,numElements),tensors=unstack(tensor2,0);return indices.forEach((value,index)=>{list.setItem(value,tensors[index])}),list}function split2(tensor2,length,elementShape){let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
-        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors2[i]=reshape(slice(tensor2,indices,sizes),elementShape)}return tensor2.dispose(),tensors2}),list=new TensorList([],elementShape,tensor2.dtype,length.length);for(let i=0;i<tensors.length;i++)list.setItem(i,tensors[i]);return list}var executeOp3=async(node2,tensorMap,context)=>{switch(node2.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node2,tensorMap,context),elseFunc=getParamValue("elseBranch",node2,tensorMap,context),cond=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node2,tensorMap,context),condFunc=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node2,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context);return data.kept||(data=cloneTensor(data)),(await pred.data())[0]?[void 0,data]:[data,void 0]}case"Merge":{let inputName=node2.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data=getTensor(inputName,tensorMap,context);return[cloneTensor(data)]}return}case"Enter":{let frameId=getParamValue("frameName",node2,tensorMap,context),data=getParamValue("tensor",node2,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data)]}case"Exit":{let data=getParamValue("tensor",node2,tensorMap,context);return context.exitFrame(),[cloneTensor(data)]}case"NextIteration":{let data=getParamValue("tensor",node2,tensorMap,context);return context.nextIteration(),[cloneTensor(data)]}case"TensorArrayV3":{let size=getParamValue("size",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node2,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node2,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node2,tensorMap,context),name=getParamValue("name",node2,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),gatherDtype=getParamValue("dtype",node2,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node2,tensorMap,context),scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node2,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node2,tensorMap,context),splitTensor=getParamValue("tensor",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node2,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node2,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElementsParam;node2.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node2,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node2,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node2,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node2,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node2,tensorMap,context)=>{switch(node2.op){case"Conv1D":{let stride=getParamValue("stride",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node2,tensorMap,context);return[conv1d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node2,tensorMap,context),stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context);return[conv2dTranspose(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv3d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node2,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp5=(node2,tensorMap,context)=>{switch(node2.op){case"Fill":{let shape=getParamValue("shape",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),value=getParamValue("value",node2,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),num=getParamValue("num",node2,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node2,tensorMap,context),numSamples=getParamValue("numSamples",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node2,tensorMap,context),depth=getParamValue("depth",node2,tensorMap,context),onValue=getParamValue("onValue",node2,tensorMap,context),offValue=getParamValue("offValue",node2,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node2,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node2,tensorMap,context),getParamValue("minval",node2,tensorMap,context),getParamValue("maxval",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"Range":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),step4=getParamValue("step",node2,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node2,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node2,tensorMap,context),mean4=getParamValue("mean",node2,tensorMap,context),stdDev=getParamValue("stdDev",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node2,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function nmsParams(node2,tensorMap,context){let boxes=getParamValue("boxes",node2,tensorMap,context),scores=getParamValue("scores",node2,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node2,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node2,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node2,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node2,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node2,tensorMap,context)=>{switch(node2.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node2,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node2,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node2,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context));default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp7=(node2,tensorMap,context)=>{switch(node2.op){case"TopKV2":{let x=getParamValue("x",node2,tensorMap,context),k=getParamValue("k",node2,tensorMap,context),sorted=getParamValue("sorted",node2,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node2,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp8=(node2,tensorMap,context)=>{switch(node2.op){case"Const":return tensorMap[node2.name];case"PlaceholderWithDefault":let def=getParamValue("default",node2,tensorMap,context);return[getTensor(node2.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node2.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data2=getParamValue("x",node2,tensorMap,context);return[cloneTensor(data2)]}case"IdentityN":return getParamValue("x",node2,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node2,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node2,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node2,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node2,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node2,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context),message=getParamValue("message",node2,tensorMap,context),summarize=getParamValue("summarize",node2,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;i<data.length;i++)console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0,summarize));return[input2];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},HashTable=class{constructor(keyDType,valueDType){this.keyDType=keyDType,this.valueDType=valueDType,this.handle=scalar(0),this.tensorMap=new Map,keep(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i<keysLength;i++){let key=$keys[i],value=$values[i];keep(value),this.tensorMap.set(key,value)}return this.handle})}async find(keys,defaultValue){this.checkKeyAndValueTensor(keys,defaultValue);let $keys=await keys.data();return tidy(()=>{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node2,tensorMap,context,resourceManager)=>{switch(node2.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node2,tensorMap,context),valueDType=getParamValue("valueDType",node2,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node2.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp10=(node2,tensorMap,context)=>{switch(node2.op){case"ResizeBilinear":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node2,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node2,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node2,tensorMap,context),boxes=getParamValue("boxes",node2,tensorMap,context),boxInd=getParamValue("boxInd",node2,tensorMap,context),cropSize=getParamValue("cropSize",node2,tensorMap,context),method=getParamValue("method",node2,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node2,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp11=(node2,tensorMap,context)=>{switch(node2.op){case"Equal":return[equal(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Less":return[less(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node2,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node2,tensorMap,context),getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp12=(node2,tensorMap,context)=>{switch(node2.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context),getParamValue("transposeA",node2,tensorMap,context),getParamValue("transposeB",node2,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node2,tensorMap,context),getParamValue("perm",node2,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node2,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node2,tensorMap,context),b:getParamValue("b",node2,tensorMap,context),transposeA:getParamValue("transposeA",node2,tensorMap,context),transposeB:getParamValue("transposeB",node2,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp13=(node2,tensorMap,context)=>{switch(node2.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node2,tensorMap,context),getParamValue("radius",node2,tensorMap,context),getParamValue("bias",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context),getParamValue("beta",node2,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node2,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node2,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node2,tensorMap,context),getParamValue("outputShape",node2,tensorMap,context),getParamValue("sparseValues",node2,tensorMap,context),getParamValue("defaultValue",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp14=(node2,tensorMap,context)=>{switch(node2.op){case"Max":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[max(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[mean(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[min(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[sum2(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[all(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[any(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMax(getParamValue("x",node2,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMin(getParamValue("x",node2,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[prod(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node2,tensorMap,context),exclusive=getParamValue("exclusive",node2,tensorMap,context),reverse5=getParamValue("reverse",node2,tensorMap,context);return[cumsum(getParamValue("x",node2,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node2,tensorMap,context),weights=getParamValue("weights",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node2,tensorMap,context),weights2=getParamValue("weights",node2,tensorMap,context),size2=getParamValue("size",node2,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node2,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp15=(node2,tensorMap,context)=>{switch(node2.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),inputs=getParamValue("tensors",node2,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node2,tensorMap,context),batchDims=getParamValue("batchDims",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node2,tensorMap,context),axis=[];for(let i=0;i<dims.length;i++)dims[i]&&axis.push(i);let input2=getParamValue("x",node2,tensorMap,context);return[reverse(input2,axis)]}case"ReverseV2":{let axis=getParamValue("axis",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context);return[reverse(input2,axis)]}case"Slice":{let begin=getParamValue("begin",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context);return[slice(getParamValue("x",node2,tensorMap,context),begin,size)]}case"StridedSlice":{let begin=getParamValue("begin",node2,tensorMap,context),end=getParamValue("end",node2,tensorMap,context),strides=getParamValue("strides",node2,tensorMap,context),beginMask=getParamValue("beginMask",node2,tensorMap,context),endMask=getParamValue("endMask",node2,tensorMap,context),ellipsisMask=getParamValue("ellipsisMask",node2,tensorMap,context),newAxisMask=getParamValue("newAxisMask",node2,tensorMap,context),shrinkAxisMask=getParamValue("shrinkAxisMask",node2,tensorMap,context),tensor2=getParamValue("x",node2,tensorMap,context);return[stridedSlice(tensor2,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)]}case"Pack":return tidy(()=>{let axis=getParamValue("axis",node2,tensorMap,context),tensors=getParamValue("tensors",node2,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node2,tensorMap,context),tensor2=getParamValue("tensor",node2,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node2,tensorMap,context);return[tile(getParamValue("x",node2,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node2,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node2,tensorMap,context),tensor2=getParamValue("x",node2,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context),shape=getParamValue("shape",node2,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node2,tensorMap,context),shape=getParamValue("outputShape",node2,tensorMap,context),sparseValues=getParamValue("sparseValues",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp16=(node2,tensorMap,context)=>{switch(node2.op){case"FFT":return[fft(getParamValue("x",node2,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node2,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node2,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp17=(node2,tensorMap,context)=>{switch(node2.op){case"Cast":return[cast(getParamValue("x",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node2,tensorMap,context);return[expandDims(getParamValue("x",node2,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node2,tensorMap,context);return[squeeze(getParamValue("x",node2,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("mode",node2,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("constantValue",node2,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),paddings=getParamValue("paddings",node2,tensorMap,context);return[spaceToBatchND(getParamValue("x",node2,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),crops=getParamValue("crops",node2,tensorMap,context);return[batchToSpaceND(getParamValue("x",node2,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node2,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function executeOp18(node2,tensorMap,context,resourceManager){let value=((node22,tensorMap2,context2)=>{switch(node22.category){case"arithmetic":return tidy(()=>executeOp(node22,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node22,tensorMap2,context2));case"control":return executeOp3(node22,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node22,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node22,tensorMap2,context2));case"dynamic":return executeOp6(node22,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node22,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node22,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node22,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node22,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node22,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node22,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node22,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node22,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node22,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node22,tensorMap2,context2));case"hash_table":return executeOp9(node22,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node22.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node22,tensorMap2,context2));throw TypeError(`Custom op ${node22.op} is not registered.`);default:throw TypeError(`Unknown op '${node22.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node2,tensorMap,context);return util_exports.isPromise(value)?value.then(data=>[].concat(data)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;i<this.contexts.length-1;i++){let contexts2=this.contexts.slice(0,this.contexts.length-i);names.push(this.contextIdforContexts(contexts2))}names.push(""),this._currentContextIds=names}contextIdforContexts(contexts2){return contexts2?contexts2.map(context=>context.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node2=>parseNodeName(node2.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node2=frontier.pop();if(((isControlFlow(node2)||isDynamicShape(node2)||isHashTable(node2))&&dynamicNode==null&&(dynamicNode=node2,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node2.name),weightMap[node2.name]==null)&&inputNodeNames.indexOf(node2.name)===-1&&initNodeNames.indexOf(node2.name)===-1){if(node2.inputs.length===0){missingInputs.push(node2.name);continue}node2.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node2=>{usedNodes.has(node2.name)&&frontier.push(node2)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node2=frontier.pop();seen.add(node2.name),weightMap[node2.name]||orderedNodes.push(node2),node2.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node2){return CONTROL_FLOW_OPS.indexOf(node2.op)>=0}function isDynamicShape(node2){return DYNAMIC_SHAPE_OPS.indexOf(node2.op)>=0}function isHashTable(node2){return HASH_TABLE_OPS.indexOf(node2.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node2=>node2.signatureKey||node2.name)}get outputNodes(){return this._outputs.map(node2=>{let name=node2.signatureKey||node2.name;return node2.defaultOutput?`${name}:${node2.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node2=>node2.name).sort(),sortedOutputs=outputs.map(node2=>node2.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;i<orderedNodes.length;i++){let node2=orderedNodes[i];if(!tensorsMap[node2.name]){let tensors=executeOp18(node2,tensorsMap,context,this._resourceManager);if(util_exports.isPromise(tensors))throw new Error(`The execution of the op '${node2.op}' returned a promise. Please use model.executeAsync() instead.`);tensorsMap[node2.name]=tensors,this.checkTensorForDisposal(node2.name,node2,tensorsMap,context,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount)}}return this.parent==null&&context.dispose(tensorsToKeep),outputs.map(name=>getTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node2,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node2.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node2.children.length)}),node2.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node2=>({node:node2,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node2=>!isControlFlow(node2)&&!getTensor(node2.name,tensorsMap,context)).map(node2=>node2.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node2,stack2,context,tensorMap,added,usedNodes){node2.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node2=this.graph.nodes[nodeName];if(node2.attrParams.shape&&node2.attrParams.shape.value){let shape=node2.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node2.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node2.attrParams.dtype&&node2.attrParams.dtype.value&&util_exports.assert(input2.dtype===node2.attrParams.dtype.value,()=>`The dtype of dict['${node2.name}'] provided in model.execute(dict) must be ${node2.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version3="2.8.2",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version4,webcam:()=>webcam,zip:()=>zip});var seedrandom3=__toModule2(require_seedrandom2()),seedrandom2=__toModule2(require_seedrandom2());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;i<len;i++)newData[i]=this.get(this.wrap(this.begin+i));this.data=newData,this.capacity=newCapacity,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=len}};GrowingRingBuffer.INITIAL_CAPACITY=32;function iteratorFromItems(items){return new ArrayIterator(items)}function iteratorFromFunction(func2){return new FunctionCallIterator(func2)}function iteratorFromConcatenated(baseIterators,baseErrorHandler){return new ChainedIterator(baseIterators,baseErrorHandler)}function iteratorFromZipped(iterators,mismatchMode=ZipMismatchMode.FAIL){return new ZipIterator(iterators,mismatchMode)}var LazyIterator=class{async toArray(){let result=[],x=await this.next();for(;!x.done;)result.push(x.value),x=await this.next();return result}async toArrayForTest(){let stream=this.prefetch(100),result=[],x=await stream.next();for(;!x.done;)result.push(x.value),x=await stream.next();return result}async resolveFully(){let x=await this.next();for(;!x.done;)x=await this.next()}async resolveWhile(predicate){let x=await this.next(),shouldContinue=predicate(x.value);for(;!x.done&&shouldContinue;)x=await this.next(),shouldContinue=predicate(x.value)}handleErrors(handler){return new ErrorHandlingLazyIterator(this,handler)}filter(predicate){return new FilterIterator(this,predicate)}map(transform){return new MapIterator(this,transform)}mapAsync(transform){return new AsyncMapIterator(this,transform)}serialMapAsync(transform){return new AsyncMapIterator(this,transform).serial()}flatmap(transform){return new FlatmapIterator(this,transform)}async forEachAsync(f){return this.map(f).resolveFully()}async serialForEach(f){return this.serialMapAsync(f).resolveWhile(x=>x===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let skipped=await this.upstream.next();if(skipped.done)return skipped;dispose(skipped.value)}return this.upstream.next()}},TakeIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length<this.batchSize;){let item=await this.upstream.next();if(item.done)return this.enableSmallLastBatch&&batch.length>0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom2.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is
+        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors2[i]=reshape(slice(tensor2,indices,sizes),elementShape)}return tensor2.dispose(),tensors2}),list=new TensorList([],elementShape,tensor2.dtype,length.length);for(let i=0;i<tensors.length;i++)list.setItem(i,tensors[i]);return list}var executeOp3=async(node2,tensorMap,context)=>{switch(node2.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node2,tensorMap,context),elseFunc=getParamValue("elseBranch",node2,tensorMap,context),cond=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node2,tensorMap,context),condFunc=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node2,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context);return data.kept||(data=cloneTensor(data)),(await pred.data())[0]?[void 0,data]:[data,void 0]}case"Merge":{let inputName=node2.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data=getTensor(inputName,tensorMap,context);return[cloneTensor(data)]}return}case"Enter":{let frameId=getParamValue("frameName",node2,tensorMap,context),data=getParamValue("tensor",node2,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data)]}case"Exit":{let data=getParamValue("tensor",node2,tensorMap,context);return context.exitFrame(),[cloneTensor(data)]}case"NextIteration":{let data=getParamValue("tensor",node2,tensorMap,context);return context.nextIteration(),[cloneTensor(data)]}case"TensorArrayV3":{let size=getParamValue("size",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node2,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node2,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node2,tensorMap,context),name=getParamValue("name",node2,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),gatherDtype=getParamValue("dtype",node2,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node2,tensorMap,context),scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node2,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node2,tensorMap,context),splitTensor=getParamValue("tensor",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node2,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node2,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElementsParam;node2.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node2,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node2,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node2,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node2,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node2,tensorMap,context)=>{switch(node2.op){case"Conv1D":{let stride=getParamValue("stride",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node2,tensorMap,context);return[conv1d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node2,tensorMap,context),stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context);return[conv2dTranspose(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv3d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node2,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp5=(node2,tensorMap,context)=>{switch(node2.op){case"Fill":{let shape=getParamValue("shape",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),value=getParamValue("value",node2,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),num=getParamValue("num",node2,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node2,tensorMap,context),numSamples=getParamValue("numSamples",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node2,tensorMap,context),depth=getParamValue("depth",node2,tensorMap,context),onValue=getParamValue("onValue",node2,tensorMap,context),offValue=getParamValue("offValue",node2,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node2,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node2,tensorMap,context),getParamValue("minval",node2,tensorMap,context),getParamValue("maxval",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"Range":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),step4=getParamValue("step",node2,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node2,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node2,tensorMap,context),mean4=getParamValue("mean",node2,tensorMap,context),stdDev=getParamValue("stdDev",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node2,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function nmsParams(node2,tensorMap,context){let boxes=getParamValue("boxes",node2,tensorMap,context),scores=getParamValue("scores",node2,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node2,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node2,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node2,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node2,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node2,tensorMap,context)=>{switch(node2.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node2,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node2,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node2,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context));default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp7=(node2,tensorMap,context)=>{switch(node2.op){case"TopKV2":{let x=getParamValue("x",node2,tensorMap,context),k=getParamValue("k",node2,tensorMap,context),sorted=getParamValue("sorted",node2,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node2,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp8=(node2,tensorMap,context)=>{switch(node2.op){case"Const":return tensorMap[node2.name];case"PlaceholderWithDefault":let def=getParamValue("default",node2,tensorMap,context);return[getTensor(node2.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node2.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data2=getParamValue("x",node2,tensorMap,context);return[cloneTensor(data2)]}case"IdentityN":return getParamValue("x",node2,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node2,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node2,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node2,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node2,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node2,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context),message=getParamValue("message",node2,tensorMap,context),summarize=getParamValue("summarize",node2,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;i<data.length;i++)console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0,summarize));return[input2];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},HashTable=class{constructor(keyDType,valueDType){this.keyDType=keyDType,this.valueDType=valueDType,this.handle=scalar(0),this.tensorMap=new Map,keep(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i<keysLength;i++){let key=$keys[i],value=$values[i];keep(value),this.tensorMap.set(key,value)}return this.handle})}async find(keys,defaultValue){this.checkKeyAndValueTensor(keys,defaultValue);let $keys=await keys.data();return tidy(()=>{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node2,tensorMap,context,resourceManager)=>{switch(node2.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node2,tensorMap,context),valueDType=getParamValue("valueDType",node2,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node2.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp10=(node2,tensorMap,context)=>{switch(node2.op){case"ResizeBilinear":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node2,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node2,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node2,tensorMap,context),boxes=getParamValue("boxes",node2,tensorMap,context),boxInd=getParamValue("boxInd",node2,tensorMap,context),cropSize=getParamValue("cropSize",node2,tensorMap,context),method=getParamValue("method",node2,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node2,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp11=(node2,tensorMap,context)=>{switch(node2.op){case"Equal":return[equal(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Less":return[less(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node2,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node2,tensorMap,context),getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp12=(node2,tensorMap,context)=>{switch(node2.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context),getParamValue("transposeA",node2,tensorMap,context),getParamValue("transposeB",node2,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node2,tensorMap,context),getParamValue("perm",node2,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node2,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node2,tensorMap,context),b:getParamValue("b",node2,tensorMap,context),transposeA:getParamValue("transposeA",node2,tensorMap,context),transposeB:getParamValue("transposeB",node2,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp13=(node2,tensorMap,context)=>{switch(node2.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node2,tensorMap,context),getParamValue("radius",node2,tensorMap,context),getParamValue("bias",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context),getParamValue("beta",node2,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node2,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node2,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node2,tensorMap,context),getParamValue("outputShape",node2,tensorMap,context),getParamValue("sparseValues",node2,tensorMap,context),getParamValue("defaultValue",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp14=(node2,tensorMap,context)=>{switch(node2.op){case"Max":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[max(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[mean(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[min(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[sum2(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[all(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[any(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMax(getParamValue("x",node2,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMin(getParamValue("x",node2,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[prod(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node2,tensorMap,context),exclusive=getParamValue("exclusive",node2,tensorMap,context),reverse5=getParamValue("reverse",node2,tensorMap,context);return[cumsum(getParamValue("x",node2,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node2,tensorMap,context),weights=getParamValue("weights",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node2,tensorMap,context),weights2=getParamValue("weights",node2,tensorMap,context),size2=getParamValue("size",node2,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node2,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp15=(node2,tensorMap,context)=>{switch(node2.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),inputs=getParamValue("tensors",node2,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node2,tensorMap,context),batchDims=getParamValue("batchDims",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node2,tensorMap,context),axis=[];for(let i=0;i<dims.length;i++)dims[i]&&axis.push(i);let input2=getParamValue("x",node2,tensorMap,context);return[reverse(input2,axis)]}case"ReverseV2":{let axis=getParamValue("axis",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context);return[reverse(input2,axis)]}case"Slice":{let begin=getParamValue("begin",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context);return[slice(getParamValue("x",node2,tensorMap,context),begin,size)]}case"StridedSlice":{let begin=getParamValue("begin",node2,tensorMap,context),end=getParamValue("end",node2,tensorMap,context),strides=getParamValue("strides",node2,tensorMap,context),beginMask=getParamValue("beginMask",node2,tensorMap,context),endMask=getParamValue("endMask",node2,tensorMap,context),ellipsisMask=getParamValue("ellipsisMask",node2,tensorMap,context),newAxisMask=getParamValue("newAxisMask",node2,tensorMap,context),shrinkAxisMask=getParamValue("shrinkAxisMask",node2,tensorMap,context),tensor2=getParamValue("x",node2,tensorMap,context);return[stridedSlice(tensor2,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)]}case"Pack":return tidy(()=>{let axis=getParamValue("axis",node2,tensorMap,context),tensors=getParamValue("tensors",node2,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node2,tensorMap,context),tensor2=getParamValue("tensor",node2,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node2,tensorMap,context);return[tile(getParamValue("x",node2,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node2,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node2,tensorMap,context),tensor2=getParamValue("x",node2,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context),shape=getParamValue("shape",node2,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node2,tensorMap,context),shape=getParamValue("outputShape",node2,tensorMap,context),sparseValues=getParamValue("sparseValues",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp16=(node2,tensorMap,context)=>{switch(node2.op){case"FFT":return[fft(getParamValue("x",node2,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node2,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node2,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp17=(node2,tensorMap,context)=>{switch(node2.op){case"Cast":return[cast(getParamValue("x",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node2,tensorMap,context);return[expandDims(getParamValue("x",node2,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node2,tensorMap,context);return[squeeze(getParamValue("x",node2,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("mode",node2,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("constantValue",node2,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),paddings=getParamValue("paddings",node2,tensorMap,context);return[spaceToBatchND(getParamValue("x",node2,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),crops=getParamValue("crops",node2,tensorMap,context);return[batchToSpaceND(getParamValue("x",node2,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node2,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function executeOp18(node2,tensorMap,context,resourceManager){let value=((node22,tensorMap2,context2)=>{switch(node22.category){case"arithmetic":return tidy(()=>executeOp(node22,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node22,tensorMap2,context2));case"control":return executeOp3(node22,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node22,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node22,tensorMap2,context2));case"dynamic":return executeOp6(node22,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node22,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node22,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node22,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node22,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node22,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node22,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node22,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node22,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node22,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node22,tensorMap2,context2));case"hash_table":return executeOp9(node22,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node22.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node22,tensorMap2,context2));throw TypeError(`Custom op ${node22.op} is not registered.`);default:throw TypeError(`Unknown op '${node22.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node2,tensorMap,context);return util_exports.isPromise(value)?value.then(data=>[].concat(data)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;i<this.contexts.length-1;i++){let contexts2=this.contexts.slice(0,this.contexts.length-i);names.push(this.contextIdforContexts(contexts2))}names.push(""),this._currentContextIds=names}contextIdforContexts(contexts2){return contexts2?contexts2.map(context=>context.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node2=>parseNodeName(node2.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node2=frontier.pop();if((isControlFlow(node2)||isDynamicShape(node2)||isHashTable(node2))&&dynamicNode==null&&(dynamicNode=node2,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node2.name),weightMap[node2.name]==null&&inputNodeNames.indexOf(node2.name)===-1&&initNodeNames.indexOf(node2.name)===-1){if(node2.inputs.length===0){missingInputs.push(node2.name);continue}node2.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node2=>{usedNodes.has(node2.name)&&frontier.push(node2)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node2=frontier.pop();seen.add(node2.name),weightMap[node2.name]||orderedNodes.push(node2),node2.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node2){return CONTROL_FLOW_OPS.indexOf(node2.op)>=0}function isDynamicShape(node2){return DYNAMIC_SHAPE_OPS.indexOf(node2.op)>=0}function isHashTable(node2){return HASH_TABLE_OPS.indexOf(node2.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node2=>node2.signatureKey||node2.name)}get outputNodes(){return this._outputs.map(node2=>{let name=node2.signatureKey||node2.name;return node2.defaultOutput?`${name}:${node2.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node2=>node2.name).sort(),sortedOutputs=outputs.map(node2=>node2.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;i<orderedNodes.length;i++){let node2=orderedNodes[i];if(!tensorsMap[node2.name]){let tensors=executeOp18(node2,tensorsMap,context,this._resourceManager);if(util_exports.isPromise(tensors))throw new Error(`The execution of the op '${node2.op}' returned a promise. Please use model.executeAsync() instead.`);tensorsMap[node2.name]=tensors,this.checkTensorForDisposal(node2.name,node2,tensorsMap,context,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount)}}return this.parent==null&&context.dispose(tensorsToKeep),outputs.map(name=>getTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node2,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node2.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node2.children.length)}),node2.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node2=>({node:node2,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node2=>!isControlFlow(node2)&&!getTensor(node2.name,tensorsMap,context)).map(node2=>node2.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node2,stack2,context,tensorMap,added,usedNodes){node2.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node2=this.graph.nodes[nodeName];if(node2.attrParams.shape&&node2.attrParams.shape.value){let shape=node2.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node2.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node2.attrParams.dtype&&node2.attrParams.dtype.value&&util_exports.assert(input2.dtype===node2.attrParams.dtype.value,()=>`The dtype of dict['${node2.name}'] provided in model.execute(dict) must be ${node2.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version3="2.8.3",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version4,webcam:()=>webcam,zip:()=>zip});var seedrandom3=__toModule2(require_seedrandom2()),seedrandom2=__toModule2(require_seedrandom2());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;i<len;i++)newData[i]=this.get(this.wrap(this.begin+i));this.data=newData,this.capacity=newCapacity,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=len}};GrowingRingBuffer.INITIAL_CAPACITY=32;function iteratorFromItems(items){return new ArrayIterator(items)}function iteratorFromFunction(func2){return new FunctionCallIterator(func2)}function iteratorFromConcatenated(baseIterators,baseErrorHandler){return new ChainedIterator(baseIterators,baseErrorHandler)}function iteratorFromZipped(iterators,mismatchMode=ZipMismatchMode.FAIL){return new ZipIterator(iterators,mismatchMode)}var LazyIterator=class{async toArray(){let result=[],x=await this.next();for(;!x.done;)result.push(x.value),x=await this.next();return result}async toArrayForTest(){let stream=this.prefetch(100),result=[],x=await stream.next();for(;!x.done;)result.push(x.value),x=await stream.next();return result}async resolveFully(){let x=await this.next();for(;!x.done;)x=await this.next()}async resolveWhile(predicate){let x=await this.next(),shouldContinue=predicate(x.value);for(;!x.done&&shouldContinue;)x=await this.next(),shouldContinue=predicate(x.value)}handleErrors(handler){return new ErrorHandlingLazyIterator(this,handler)}filter(predicate){return new FilterIterator(this,predicate)}map(transform){return new MapIterator(this,transform)}mapAsync(transform){return new AsyncMapIterator(this,transform)}serialMapAsync(transform){return new AsyncMapIterator(this,transform).serial()}flatmap(transform){return new FlatmapIterator(this,transform)}async forEachAsync(f){return this.map(f).resolveFully()}async serialForEach(f){return this.serialMapAsync(f).resolveWhile(x=>x===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let skipped=await this.upstream.next();if(skipped.done)return skipped;dispose(skipped.value)}return this.upstream.next()}},TakeIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length<this.batchSize;){let item=await this.upstream.next();if(item.done)return this.enableSmallLastBatch&&batch.length>0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom2.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is
       ${batchSize}`);let size;return this.size===Infinity||this.size==null?size=this.size:smallLastBatch?size=Math.ceil(this.size/batchSize):size=Math.floor(this.size/batchSize),datasetFromIteratorFn(async()=>(await base2.iterator()).columnMajorBatch(batchSize,smallLastBatch,deepBatchConcat),size)}concatenate(dataset){let base2=this,size;return this.size===Infinity||dataset.size===Infinity?size=Infinity:this.size!=null&&dataset.size!=null?size=this.size+dataset.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).concatenate(await dataset.iterator()),size)}filter(predicate){let base2=this,size;return this.size===Infinity?size=Infinity:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).filter(x=>tidy(()=>predicate(x))),size)}async forEachAsync(f){return(await this.iterator()).forEachAsync(f)}map(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).map(x=>tidy(()=>transform(x))),this.size)}mapAsync(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).mapAsync(transform),this.size)}prefetch(bufferSize){if(bufferSize==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).prefetch(bufferSize),this.size)}repeat(count2){let base2=this,size;return this.size!=null&&count2>0?size=this.size*count2:count2===0?size=0:this.size!=null&&(count2===void 0||count2<0)?size=Infinity:size=null,datasetFromIteratorFn(async()=>{let iteratorIterator=iteratorFromFunction(async()=>({value:await base2.iterator(),done:!1}));return iteratorFromConcatenated(iteratorIterator.take(count2))},size)}skip(count2){let base2=this,size;return this.size!=null&&count2>=0&&this.size>=count2?size=this.size-count2:this.size!=null&&(this.size<count2||count2===void 0||count2<0)?size=0:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).skip(count2),size)}shuffle(bufferSize,seed,reshuffleEachIteration=!0){if(bufferSize==null||bufferSize<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let base2=this,random=seedrandom3.alea(seed||util_exports.now().toString());return datasetFromIteratorFn(async()=>{let seed2=random.int32();return reshuffleEachIteration&&(seed2+=random.int32()),(await base2.iterator()).shuffle(bufferSize,seed2.toString())},this.size)}take(count2){let base2=this,size;return this.size!=null&&this.size>count2?size=count2:this.size!=null&&this.size<=count2?size=this.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).take(count2),size)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Dataset.MAX_BUFFER_SIZE=1e4;function datasetFromIteratorFn(iteratorFn,size=null){return new class extends Dataset{constructor(){super(...arguments);this.size=size}async iterator(){return iteratorFn()}}}function array(items){return datasetFromIteratorFn(async()=>iteratorFromItems(items),items.length)}function zip(datasets){if(!isIterable2(datasets))throw new Error("The argument to zip() must be an object or array.");let size;if(Array.isArray(datasets))for(let i=0;i<datasets.length;i++)size=size==null?datasets[i].size:Math.min(size,datasets[i].size);else if(datasets instanceof Object)for(let ds in datasets)size=size==null?datasets[ds].size:Math.min(size,datasets[ds].size);return datasetFromIteratorFn(async()=>{let streams=await deepMapAndAwaitAll(datasets,d=>{if(d instanceof Dataset)return{value:d.iterator(),recurse:!1};if(isIterable2(d))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return iteratorFromZipped(streams,ZipMismatchMode.SHORTEST)},size)}function deepBatchConcat(rows){if(rows===null)return null;let exampleRow=rows[0];return canTensorify(exampleRow)?{value:batchConcat(rows),recurse:!1}:{value:null,recurse:!0}}function batchConcat(arrays){if(arrays.length===0)throw new Error("Can't make a batch of zero elements.");return arrays[0]instanceof Tensor?stack(arrays):tensor(arrays)}var TextLineDataset=class extends Dataset{constructor(input2){super();this.input=input2}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i<this.fullColumnNames.length;i++){let key=this.fullColumnNames[i],config=this.columnConfigs?this.columnConfigs[key]:null;if(!(this.configuredColumnsOnly&&!config)){let value=values[i],parsedValue=null;if(value==="")if(config&&config.default!==void 0)parsedValue=config.default;else{if(config&&(config.required||config.isLabel))throw new Error(`Required column ${key} is empty in this line: ${line}`);parsedValue=void 0}else{let valueAsNum=Number(value);if(isNaN(valueAsNum))config&&config.dtype==="bool"?parsedValue=this.getBoolean(value):parsedValue=value;else if(!config||!config.dtype)parsedValue=valueAsNum;else switch(config.dtype){case"float32":parsedValue=valueAsNum;break;case"int32":parsedValue=Math.floor(valueAsNum);break;case"bool":parsedValue=this.getBoolean(value);break;default:parsedValue=valueAsNum}}config&&config.isLabel?labels[key]=parsedValue:features[key]=parsedValue}}return Object.keys(labels).length===0?features:{xs:features,ys:labels}}getBoolean(value){return value==="1"||value.toLowerCase()==="true"?1:0}parseRow(line,validateElementCount=!0){let result=[],readOffset=0,readLength=line.length,currentState=STATE_OUT;for(let i=0;i<readLength;i++)switch(currentState){case STATE_OUT:switch(line.charAt(i)){case CODE_QUOTE:readOffset=i+1,currentState=STATE_QUOTE;break;case this.delimiter:if(readOffset=i+1,this.delimiter===" "&&this.delimWhitespace)break;result.push(""),currentState=STATE_OUT;break;default:currentState=STATE_FIELD,readOffset=i;break}break;case STATE_FIELD:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i)),currentState=STATE_OUT,readOffset=i+1;break;default:}break;case STATE_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE_AFTER_QUOTE;break;default:}break;case STATE_QUOTE_AFTER_QUOTE:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i-1)),currentState=STATE_OUT,readOffset=i+1;break;case CODE_QUOTE:currentState=STATE_QUOTE;break;default:currentState=STATE_WITHIN_QUOTE_IN_QUOTE;break}break;case STATE_WITHIN_QUOTE_IN_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE;break;default:}break;default:}if(currentState===STATE_QUOTE_AFTER_QUOTE?result.push(line.substring(readOffset,readLength-1)):result.push(line.substring(readOffset)),validateElementCount&&result.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);return result}},MicrophoneIterator=class extends LazyIterator{constructor(microphoneConfig){super();this.microphoneConfig=microphoneConfig,this.isClosed=!1,this.fftSize=microphoneConfig.fftSize||1024;let fftSizeLog2=Math.log2(this.fftSize);if(this.fftSize<0||fftSizeLog2<4||fftSizeLog2>14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data,i)=>freqData.set(data,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data=fileReader.result;if(data instanceof ArrayBuffer&&(data=new Uint8Array(data)),!(data instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version4="2.8.2";function assertNotComplex(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(`
+`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i<this.fullColumnNames.length;i++){let key=this.fullColumnNames[i],config=this.columnConfigs?this.columnConfigs[key]:null;if(!(this.configuredColumnsOnly&&!config)){let value=values[i],parsedValue=null;if(value==="")if(config&&config.default!==void 0)parsedValue=config.default;else{if(config&&(config.required||config.isLabel))throw new Error(`Required column ${key} is empty in this line: ${line}`);parsedValue=void 0}else{let valueAsNum=Number(value);if(isNaN(valueAsNum))config&&config.dtype==="bool"?parsedValue=this.getBoolean(value):parsedValue=value;else if(!config||!config.dtype)parsedValue=valueAsNum;else switch(config.dtype){case"float32":parsedValue=valueAsNum;break;case"int32":parsedValue=Math.floor(valueAsNum);break;case"bool":parsedValue=this.getBoolean(value);break;default:parsedValue=valueAsNum}}config&&config.isLabel?labels[key]=parsedValue:features[key]=parsedValue}}return Object.keys(labels).length===0?features:{xs:features,ys:labels}}getBoolean(value){return value==="1"||value.toLowerCase()==="true"?1:0}parseRow(line,validateElementCount=!0){let result=[],readOffset=0,readLength=line.length,currentState=STATE_OUT;for(let i=0;i<readLength;i++)switch(currentState){case STATE_OUT:switch(line.charAt(i)){case CODE_QUOTE:readOffset=i+1,currentState=STATE_QUOTE;break;case this.delimiter:if(readOffset=i+1,this.delimiter===" "&&this.delimWhitespace)break;result.push(""),currentState=STATE_OUT;break;default:currentState=STATE_FIELD,readOffset=i;break}break;case STATE_FIELD:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i)),currentState=STATE_OUT,readOffset=i+1;break;default:}break;case STATE_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE_AFTER_QUOTE;break;default:}break;case STATE_QUOTE_AFTER_QUOTE:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i-1)),currentState=STATE_OUT,readOffset=i+1;break;case CODE_QUOTE:currentState=STATE_QUOTE;break;default:currentState=STATE_WITHIN_QUOTE_IN_QUOTE;break}break;case STATE_WITHIN_QUOTE_IN_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE;break;default:}break;default:}if(currentState===STATE_QUOTE_AFTER_QUOTE?result.push(line.substring(readOffset,readLength-1)):result.push(line.substring(readOffset)),validateElementCount&&result.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);return result}},MicrophoneIterator=class extends LazyIterator{constructor(microphoneConfig){super();this.microphoneConfig=microphoneConfig,this.isClosed=!1,this.fftSize=microphoneConfig.fftSize||1024;let fftSizeLog2=Math.log2(this.fftSize);if(this.fftSize<0||fftSizeLog2<4||fftSizeLog2>14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data,i)=>freqData.set(data,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data=fileReader.result;if(data instanceof ArrayBuffer&&(data=new Uint8Array(data)),!(data instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version4="2.8.3";function assertNotComplex(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(`
 ============================
 Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
-============================`));let dataId={};return this.data.set(dataId,{values,dtype,refCount:1}),dataId}makeTensorInfo(shape,dtype,values){let outId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));outId=this.write(encodedValues,shape,dtype)}else outId=this.write(values,shape,dtype);return{dataId:outId,shape,dtype}}incRef(dataId){let tensorData=this.data.get(dataId);tensorData.refCount++}decRef(dataId){if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--}}move(dataId,values,shape,dtype){this.data.set(dataId,{values,dtype,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{dtype,complexTensorInfos}=this.data.get(dataId);if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);return backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}return this.data.get(dataId).values}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}makeOutput(values,shape,dtype){let dataId=this.write(values,shape,dtype);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}disposeData(dataId){if(this.data.has(dataId)){let{complexTensorInfos}=this.data.get(dataId);complexTensorInfos!=null&&(this.disposeData(complexTensorInfos.real.dataId),this.disposeData(complexTensorInfos.imag.dataId)),this.data.delete(dataId)}}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--,tensorData.refCount<1&&this.disposeData(dataId)}}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(condition){assertNotComplex([condition],"where");let condVals=this.readSync(condition.dataId);return whereImpl2(condition.shape,condVals)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},shared_exports={};__export2(shared_exports,{addImpl:()=>addImpl,bincountImpl:()=>bincountImpl,bincountReduceImpl:()=>bincountReduceImpl,ceilImpl:()=>ceilImpl,concatImpl:()=>concatImpl,expImpl:()=>expImpl,expm1Impl:()=>expm1Impl,floorImpl:()=>floorImpl,gatherV2Impl:()=>gatherV2Impl,greaterImpl:()=>greaterImpl,lessImpl:()=>lessImpl,linSpaceImpl:()=>linSpaceImpl,logImpl:()=>logImpl,maxImpl:()=>maxImpl,maximumImpl:()=>maximumImpl,minimumImpl:()=>minimumImpl,multiplyImpl:()=>multiplyImpl,negImpl:()=>negImpl,notEqualImpl:()=>notEqualImpl,prodImpl:()=>prodImpl,rangeImpl:()=>rangeImpl,rsqrtImpl:()=>rsqrtImpl,simpleAbsImpl:()=>simpleAbsImpl,sliceImpl:()=>sliceImpl,squaredDifferenceImpl:()=>squaredDifferenceImpl,stridedSliceImpl:()=>stridedSliceImpl,subImpl:()=>subImpl,tileImpl:()=>tileImpl,topKImpl:()=>topKImpl,transposeImpl:()=>transposeImpl,uniqueImpl:()=>uniqueImpl});function simpleAbsImpl(vals){let resultValues=new Float32Array(vals.length);for(let i=0;i<vals.length;++i)resultValues[i]=Math.abs(vals[i]);return resultValues}var abs2=args=>{let{x}=args.inputs,cpuBackend=args.backend;assertNotComplex(x,"abs");let resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),values=cpuBackend.data.get(x.dataId).values;return resultValues=simpleAbsImpl(values),cpuBackend.makeOutput(resultValues,x.shape,"float32")},absConfig={kernelName:Abs,backendName:"cpu",kernelFunc:abs2};function createSimpleBinaryKernelImpl(op2){return(aShape,bShape,aVals,bVals,dtype)=>{let newShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultRank=newShape.length,resultStrides=util_exports.computeStrides(newShape),resultSize=util_exports.sizeFromShape(newShape),result=util_exports.getTypedArrayFromDType(dtype,resultSize),aRank=aShape.length,bRank=bShape.length,aStrides=util_exports.computeStrides(aShape),bStrides=util_exports.computeStrides(bShape),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,newShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<result.length;++i)result[i]=op2(aVals[i%aVals.length],bVals[i%bVals.length]);else for(let i=0;i<result.length;++i){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides);result[i]=op2(aVals[aIndex],bVals[bIndex])}return[result,newShape]}}function complex2(args){let{inputs,backend:backend2}=args,{real:real4,imag:imag4}=inputs,realVals=backend2.data.get(real4.dataId).values,imagVals=backend2.data.get(imag4.dataId).values,complexInfo=backend2.makeTensorInfo(real4.shape,"complex64"),complex4=backend2.data.get(complexInfo.dataId);return complex4.complexTensorInfos={real:backend2.makeTensorInfo(real4.shape,"float32",realVals),imag:backend2.makeTensorInfo(imag4.shape,"float32",imagVals)},complexInfo}var complexConfig={kernelName:Complex,backendName:"cpu",kernelFunc:complex2};function zeros3(backend2,shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros3(backend2,shape,"float32"),imag4=zeros3(backend2,shape,"float32");return complex2({inputs:{real:real4,imag:imag4},backend:backend2})}let values=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape),dtype);return backend2.makeTensorInfo(shape,dtype,values)}function identity2(args){let{inputs,backend:backend2}=args,{x}=inputs;return backend2.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig={kernelName:Identity,backendName:"cpu",kernelFunc:identity2};function real2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,real4=backend2.data.get(input2.dataId).complexTensorInfos.real,realVal=backend2.data.get(real4.dataId).values;return backend2.makeTensorInfo(real4.shape,real4.dtype,realVal)}var realConfig={kernelName:Real,backendName:"cpu",kernelFunc:real2};function cast3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity2({inputs:{x},backend:backend2});let zerosTensorInfo=zeros3(backend2,x.shape,x.dtype),floatX=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex2({inputs:{real:floatX,imag:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),result=cast3({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity2({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32"){let values=backend2.data.get(x.dataId).values,resultValues=Int32Array.from(values);return backend2.makeTensorInfo(x.shape,"int32",resultValues)}if(dtype==="bool"){let xVals=backend2.data.get(x.dataId).values,zero=util_exports.toTypedArray([0],x.dtype),[resultData,resultShape]=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0)(x.shape,[],xVals,zero,"bool");return backend2.makeTensorInfo(resultShape,"bool",resultData)}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig={kernelName:Cast,backendName:"cpu",kernelFunc:cast3};function binaryKernelFunc(name,simpleImpl,complexImpl,dtype){return complexImpl==null?({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;assertNotComplex([a,b],name);let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}:({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;if(a.dtype==="complex64"||b.dtype==="complex64"){let $aComplex=cast3({inputs:{x:a},backend:cpuBackend,attrs:{dtype:"complex64"}}),$aComplexVals=cpuBackend.data.get($aComplex.dataId),aReal=$aComplexVals.complexTensorInfos.real,aImag=$aComplexVals.complexTensorInfos.imag,aRealVals=cpuBackend.data.get(aReal.dataId).values,aImagVals=cpuBackend.data.get(aImag.dataId).values,$bComplex=cast3({inputs:{x:b},backend:cpuBackend,attrs:{dtype:"complex64"}}),$bComplexVals=cpuBackend.data.get($bComplex.dataId),bReal=$bComplexVals.complexTensorInfos.real,bImag=$bComplexVals.complexTensorInfos.imag,bRealVals=cpuBackend.data.get(bReal.dataId).values,bImagVals=cpuBackend.data.get(bImag.dataId).values,[resultRealData,resultImagData,resultShape]=complexImpl(a.shape,b.shape,aRealVals,aImagVals,bRealVals,bImagVals),resultReal=cpuBackend.makeTensorInfo(resultShape,"float32",resultRealData),resultImag=cpuBackend.makeTensorInfo(resultShape,"float32",resultImagData),result=complex2({inputs:{real:resultReal,imag:resultImag},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($aComplex),cpuBackend.disposeIntermediateTensorInfo($bComplex),cpuBackend.disposeIntermediateTensorInfo(resultReal),cpuBackend.disposeIntermediateTensorInfo(resultImag),result}else{let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}}}function createComplexBinaryKernelImpl(op2){return(aShape,bShape,aRealVals,aImagVals,bRealVals,bImagVals)=>{let resultShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultSize=util_exports.sizeFromShape(resultShape),resultRank=resultShape.length,resultStrides=util_exports.computeStrides(resultShape),resultRealVals=util_exports.getTypedArrayFromDType("float32",resultSize),resultImagVals=util_exports.getTypedArrayFromDType("float32",resultSize),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,resultShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,resultShape),aVals=backend_util_exports.mergeRealAndImagArrays(aRealVals,aImagVals),bVals=backend_util_exports.mergeRealAndImagArrays(bRealVals,bImagVals),aRank=aShape.length,aStrides=util_exports.computeStrides(aShape),bRank=bShape.length,bStrides=util_exports.computeStrides(bShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resultRealVals.length;i++){let aIdx=i%aVals.length,bIdx=i%bVals.length,result=op2(aVals[aIdx*2],aVals[aIdx*2+1],bVals[bIdx*2],bVals[bIdx*2+1]);resultRealVals[i]=result.real,resultImagVals[i]=result.imag}else for(let i=0;i<resultRealVals.length;i++){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides),opResult=op2(aVals[aIndex*2],aVals[aIndex*2+1],bVals[bIndex*2],bVals[bIndex*2+1]);resultRealVals[i]=opResult.real,resultImagVals[i]=opResult.imag}return[resultRealVals,resultImagVals,resultShape]}}var addImpl=createSimpleBinaryKernelImpl((a,b)=>a+b),addComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal+bReal,imag:aImag+bImag})),add4=binaryKernelFunc(Add,addImpl,addComplexImpl),addConfig={kernelName:Add,backendName:"cpu",kernelFunc:add4};function bincountImpl(xVals,weightsVals,weightsDtype,weightsShape,size){let weightsSize=util_exports.sizeFromShape(weightsShape),outVals=util_exports.makeZerosTypedArray(size,weightsDtype);for(let i=0;i<xVals.length;i++){let value=xVals[i];if(value<0)throw new Error("Input x must be non-negative!");value>=size||(weightsSize>0?outVals[value]+=weightsVals[i]:outVals[value]+=1)}return outVals}function bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput=!1){let numRows=xBuf.shape[0],numCols=xBuf.shape[1],outBuf=buffer([numRows,size],weightsBuf.dtype);for(let i=0;i<numRows;i++)for(let j=0;j<numCols;j++){let value=xBuf.get(i,j);if(value<0)throw new Error("Input x must be non-negative!");value>=size||(binaryOutput?outBuf.set(1,i,value):weightsBuf.size>0?outBuf.set(outBuf.get(i,value)+weightsBuf.get(i,j),i,value):outBuf.set(outBuf.get(i,value)+1,i,value))}return outBuf}function createSimpleUnaryImpl(op2){return(values,dtype,attrs)=>{let newValues=util_exports.getTypedArrayFromDType(dtype,values.length);for(let i=0;i<values.length;++i)newValues[i]=op2(values[i],attrs);return newValues}}function unaryKernelFunc(name,op2,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),$dtype=dtype||x.dtype,newValues=util_exports.getArrayFromDType($dtype,xSize);for(let i=0;i<xSize;++i)newValues[i]=op2(values[i],attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}function unaryKernelFuncFromImpl(name,unaryImpl,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,$dtype=dtype||x.dtype,newValues=unaryImpl(values,$dtype,attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}var ceilImpl=createSimpleUnaryImpl(xi=>Math.ceil(xi)),ceil2=unaryKernelFuncFromImpl(Ceil,ceilImpl),ceilConfig={kernelName:Ceil,backendName:"cpu",kernelFunc:ceil2};function concatImpl(inputs,outShape,dtype,simplyConcat){let outVals=util_exports.getArrayFromDType(dtype,util_exports.sizeFromShape(outShape));if(simplyConcat&&dtype!=="string"){let offset=0;inputs.forEach(input2=>{let size=util_exports.sizeFromShape(input2.shape);outVals.set(input2.vals,offset),offset+=size})}else{let colOffset=0;inputs.forEach(input2=>{let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(input2.vals):input2.vals,tIdx=0;for(let row=0;row<input2.shape[0];++row){let resIdx=row*outShape[1]+colOffset;for(let col=0;col<input2.shape[1];++col)outVals[resIdx+col]=decodedData[tIdx++]}colOffset+=input2.shape[1]})}return outVals}var expImpl=createSimpleUnaryImpl(xi=>Math.exp(xi)),exp2=unaryKernelFuncFromImpl(Exp,expImpl),expConfig={kernelName:Exp,backendName:"cpu",kernelFunc:exp2},expm1Impl=createSimpleUnaryImpl(xi=>Math.expm1(xi)),expm12=unaryKernelFuncFromImpl(Expm1,expm1Impl),expm1Config={kernelName:Expm1,backendName:"cpu",kernelFunc:expm12},floorImpl=createSimpleUnaryImpl(xi=>Math.floor(xi)),floor2=unaryKernelFuncFromImpl(Floor,floorImpl),floorConfig={kernelName:Floor,backendName:"cpu",kernelFunc:floor2};function gatherV2Impl(xBuf,indicesBuf,flattenOutputShape){let outBuf=buffer(flattenOutputShape,xBuf.dtype);for(let i=0;i<outBuf.size;++i){let originalLoc=outBuf.indexToLoc(i).slice(),batchIdx=originalLoc[0],indicesIdx=originalLoc[2],indicesIndex=indicesBuf.locToIndex([batchIdx,indicesIdx]);originalLoc[2]=indicesBuf.values[indicesIndex];let originalIndex=xBuf.locToIndex(originalLoc);outBuf.values[i]=xBuf.values[originalIndex]}return outBuf}var greaterImpl=createSimpleBinaryKernelImpl((a,b)=>a>b?1:0),greater3=binaryKernelFunc(Greater,greaterImpl,null,"bool"),greaterConfig={kernelName:Greater,backendName:"cpu",kernelFunc:greater3},lessImpl=createSimpleBinaryKernelImpl((a,b)=>a<b?1:0),less3=binaryKernelFunc(Less,lessImpl,null,"bool"),lessConfig={kernelName:Less,backendName:"cpu",kernelFunc:less3};function linSpaceImpl(start,stop,num){let step4=(stop-start)/(num-1),values=util_exports.makeZerosTypedArray(num,"float32");values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var logImpl=createSimpleUnaryImpl(xi=>Math.log(xi)),log3=unaryKernelFuncFromImpl(Log,logImpl),logConfig={kernelName:Log,backendName:"cpu",kernelFunc:log3};function maxImpl(aVals,reduceSize,outShape,dtype){let vals=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(outShape));for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value)}vals[i]=max6}return vals}var maximumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.max(aValue,bValue)),maximum3=binaryKernelFunc(Maximum,maximumImpl),maximumConfig={kernelName:Maximum,backendName:"cpu",kernelFunc:maximum3},minimumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.min(aValue,bValue)),minimum3=binaryKernelFunc(Minimum,minimumImpl),minimumConfig={kernelName:Minimum,backendName:"cpu",kernelFunc:minimum3},multiplyImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue*bValue),multiplyComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal*bReal-aImag*bImag,imag:aReal*bImag+aImag*bReal})),multiply2=binaryKernelFunc(Multiply,multiplyImpl,multiplyComplexImpl),multiplyConfig={kernelName:Multiply,backendName:"cpu",kernelFunc:multiply2};function negImpl(xVals,xShape,xDtype){let minusOne=util_exports.createScalarValue(-1,xDtype);return multiplyImpl([],xShape,minusOne,xVals,xDtype)}function neg2(args){let{inputs,backend:backend2}=args,{x}=inputs;assertNotComplex(x,"neg");let xVals=backend2.data.get(x.dataId).values,[res,newShape]=negImpl(xVals,x.shape,x.dtype);return backend2.makeTensorInfo(newShape,x.dtype,res)}var negConfig={kernelName:Neg,backendName:"cpu",kernelFunc:neg2},notEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0),notEqual2=binaryKernelFunc(NotEqual,notEqualImpl,null,"bool"),notEqualConfig={kernelName:NotEqual,backendName:"cpu",kernelFunc:notEqual2};function transposeImpl(xVals,xShape,dtype,perm,newShape){let xRank=xShape.length,xSize=util_exports.sizeFromShape(xShape),xStrides=util_exports.computeStrides(xShape),newStrides=util_exports.computeStrides(newShape),result=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(newShape));for(let i=0;i<xSize;++i){let loc=util_exports.indexToLoc(i,xRank,xStrides),newLoc=new Array(loc.length);for(let i2=0;i2<newLoc.length;i2++)newLoc[i2]=loc[perm[i2]];let newIndex=util_exports.locToIndex(newLoc,xRank,newStrides);result[newIndex]=xVals[i]}return result}function transpose2(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{perm}=attrs;assertNotComplex(x,"transpose");let xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let values=backend2.data.get(x.dataId).values,result=transposeImpl(values,x.shape,x.dtype,perm,newShape);return{dataId:backend2.write(result,newShape,x.dtype),shape:newShape,dtype:x.dtype}}var transposeConfig={kernelName:Transpose,backendName:"cpu",kernelFunc:transpose2};function prodImpl(xShape,xDtype,xVals,reductionAxes){let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,reductionAxes),outDtype=upcastType(xDtype,"int32"),outVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),outDtype),reduceSize=util_exports.sizeFromShape(reduceShape);for(let i=0;i<outVals.length;++i){let offset=i*reduceSize,prod5=1;for(let j=0;j<reduceSize;++j)prod5*=xVals[offset+j];outVals[i]=prod5}return{outVals,outShape,outDtype}}function prod2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"prod");let xRank=x.shape.length,axes=util_exports.parseAxisParam(axis,x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=x,intermediateTensorInfos=[];permutation!=null&&(permutedX=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}),intermediateTensorInfos.push(permutedX),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank));let xVals=backend2.data.get(permutedX.dataId).values,{outVals,outShape,outDtype}=prodImpl(permutedX.shape,permutedX.dtype,xVals,reductionAxes),resultShape=outShape;return keepDims&&(resultShape=backend_util_exports.expandShapeToKeepDim(outShape,axes)),intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(resultShape,outDtype,outVals)}var prodConfig={kernelName:Prod,backendName:"cpu",kernelFunc:prod2};function rangeImpl(start,stop,step4,dtype){let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step4<0,decreasingRangePositiveStep=stop<start&&step4>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return util_exports.makeZerosTypedArray(0,dtype);let numElements=Math.abs(Math.ceil((stop-start)/step4)),values=util_exports.makeZerosTypedArray(numElements,dtype);stop<start&&step4===1&&(step4=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var rsqrtImpl=createSimpleUnaryImpl(xi=>1/Math.sqrt(xi)),rsqrt2=unaryKernelFuncFromImpl(Rsqrt,rsqrtImpl),rsqrtConfig={kernelName:Rsqrt,backendName:"cpu",kernelFunc:rsqrt2};function sliceImpl(vals,begin,size,shape,dtype){let isContinous=slice_util_exports.isSliceContinous(shape,begin,size),length=util_exports.sizeFromShape(size),xStrides=util_exports.computeStrides(shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin,xStrides);return dtype==="string"?vals.slice(flatOffset,flatOffset+length):vals.subarray(flatOffset,flatOffset+length)}let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(vals):vals,inBuf=buffer(shape,dtype,decodedData),outBuf=buffer(size,dtype);for(let i=0;i<outBuf.size;++i){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.map((idx,j)=>idx+begin[j]);outBuf.set(inBuf.get(...inLoc),...outLoc)}return dtype==="string"?backend_util_exports.fromStringArrayToUint8(outBuf.values):outBuf.values}function slice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs;assertNotComplex(x,"slice");let[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);slice_util_exports.assertParamsValid(x,$begin,$size);let vals=backend2.data.get(x.dataId).values,outVals=sliceImpl(vals,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outVals)}var sliceConfig={kernelName:Slice,backendName:"cpu",kernelFunc:slice2},squaredDifferenceImpl=createSimpleBinaryKernelImpl((a,b)=>{let diff=a-b;return diff*diff}),squaredDifference2=binaryKernelFunc(SquaredDifference,squaredDifferenceImpl),squaredDifferenceConfig={kernelName:SquaredDifference,backendName:"cpu",kernelFunc:squaredDifference2};function stridedSliceImpl(outShape,xBuf,strides,begin){let outBuf=buffer(outShape,xBuf.dtype);for(let i=0;i<outBuf.size;i++){let loc=outBuf.indexToLoc(i),newLoc=new Array(loc.length);for(let j=0;j<newLoc.length;j++)newLoc[j]=loc[j]*strides[j]+begin[j];outBuf.set(xBuf.get(...newLoc),...loc)}return outBuf}var subImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue-bValue),subComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal-bReal,imag:aImag-bImag})),sub2=binaryKernelFunc(Sub,subImpl,subComplexImpl),subConfig={kernelName:Sub,backendName:"cpu",kernelFunc:sub2};function tileImpl(xBuf,reps){let newShape=new Array(xBuf.rank);for(let i=0;i<newShape.length;i++)newShape[i]=xBuf.shape[i]*reps[i];let result=buffer(newShape,xBuf.dtype);for(let i=0;i<result.values.length;++i){let newLoc=result.indexToLoc(i),originalLoc=new Array(xBuf.rank);for(let j=0;j<originalLoc.length;j++)originalLoc[j]=newLoc[j]%xBuf.shape[j];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result}function topKImpl(x,xShape,xDtype,k,sorted){let lastDim=xShape[xShape.length-1],[batch,size]=[x.length/lastDim,lastDim],allTopKVals=util_exports.getTypedArrayFromDType(xDtype,batch*k),allTopKIndices=util_exports.getTypedArrayFromDType("int32",batch*k);for(let b=0;b<batch;b++){let offset=b*size,vals=x.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value);let outOffset=b*k,topKVals=allTopKVals.subarray(outOffset,outOffset+k),topKIndices=allTopKIndices.subarray(outOffset,outOffset+k);for(let i=0;i<k;i++)topKVals[i]=valAndInd[i].value,topKIndices[i]=valAndInd[i].index}let outputShape=xShape.slice();return outputShape[outputShape.length-1]=k,[buffer(outputShape,xDtype,allTopKVals),buffer(outputShape,"int32",allTopKIndices)]}function uniqueImpl(values,axis,shape,dtype){let $axis=util_exports.parseAxisParam(axis,shape)[0],newShape=[1,shape[0],1];for(let i=0;i<$axis;i++)newShape[0]*=shape[i];newShape[1]=shape[$axis];for(let i=$axis+1;i<shape.length;i++)newShape[2]*=shape[i];let uniqueElements={},indices=new Int32Array(shape[$axis]),inputBuffer=new TensorBuffer(newShape,dtype,values),uniqueIndices=[],is1DTensor=newShape[0]===1&&newShape[2]===1;for(let i=0;i<shape[$axis];i++){let element;if(is1DTensor)element=values[i].toString();else{let axisValues=[];for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)axisValues.push(inputBuffer.get(m,i,n));element=axisValues.join(",")}if(uniqueElements[element]!==void 0)indices[i]=uniqueElements[element];else{let uniqueIndex=Object.keys(uniqueElements).length;uniqueElements[element]=uniqueIndex,indices[i]=uniqueIndex,uniqueIndices.push(i)}}let outputTmpShape=newShape.slice();outputTmpShape[1]=Object.keys(uniqueElements).length;let outputBuffer=new TensorBuffer(outputTmpShape,dtype);uniqueIndices.forEach((uniqueElementIndex,i)=>{for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)outputBuffer.set(inputBuffer.get(m,uniqueElementIndex,n),m,i,n)});let outputShape=shape.slice();return outputShape[$axis]=outputTmpShape[1],{outputValues:outputBuffer.values,outputShape,indices}}var version5="2.8.2";registerBackend("cpu",()=>new MathBackendCPU,1);var elu4=unaryKernelFunc(Elu,xi=>xi>=0?xi:Math.exp(xi)-1),eluConfig={kernelName:Elu,backendName:"cpu",kernelFunc:elu4};function leakyRelu2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{alpha}=attrs;assertNotComplex([x],"leakyRelu");let xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outVals=util_exports.getTypedArrayFromDType("float32",xSize);for(let i=0;i<xVals.length;i++)outVals[i]=xVals[i]<0?alpha*xVals[i]:xVals[i];return backend2.makeTensorInfo(x.shape,"float32",outVals)}var leakyReluConfig={kernelName:LeakyRelu,backendName:"cpu",kernelFunc:leakyRelu2},preluImpl=createSimpleBinaryKernelImpl((xValue,aValue)=>xValue<0?aValue*xValue:xValue);function prelu3(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs;assertNotComplex([x,alpha],"prelu");let aVals=backend2.data.get(x.dataId).values,bVals=backend2.data.get(alpha.dataId).values,[resultData,resultShape]=preluImpl(x.shape,alpha.shape,aVals,bVals,x.dtype);return backend2.makeTensorInfo(resultShape,x.dtype,resultData)}var preluConfig={kernelName:Prelu,backendName:"cpu",kernelFunc:prelu3},relu2=unaryKernelFunc(Relu,xi=>Math.max(0,xi)),reluConfig={kernelName:Relu,backendName:"cpu",kernelFunc:relu2},relu62=unaryKernelFunc(Relu6,xi=>Math.min(Math.max(0,xi),6)),relu6Config={kernelName:Relu6,backendName:"cpu",kernelFunc:relu62};function applyActivation2(backend2,x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return identity2({inputs:{x},backend:backend2});if(activation2==="relu")return relu2({inputs:{x},backend:backend2});if(activation2==="elu")return elu4({inputs:{x},backend:backend2});if(activation2==="relu6")return relu62({inputs:{x},backend:backend2});if(activation2==="prelu")return prelu3({inputs:{x,alpha:preluActivationWeights},backend:backend2});if(activation2==="leakyrelu")return leakyRelu2({inputs:{x},backend:backend2,attrs:{alpha:leakyreluAlpha}});throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`)}function reshape3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`),backend2.incRef(x.dataId);let xData=backend2.data.get(x.dataId);if(xData.complexTensorInfos!=null){let real4=xData.complexTensorInfos.real,imag4=xData.complexTensorInfos.imag;real4.shape=$shape,imag4.shape=$shape}return{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig={kernelName:Reshape,backendName:"cpu",kernelFunc:reshape3};function batchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;assertNotComplex([a,b],"matMul");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape3({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape3({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),sharedDim=transposeA?a3d.shape[1]:a3d.shape[2],leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),a3dValues=backend2.data.get(a3d.dataId).values,b3dValues=backend2.data.get(b3d.dataId).values,a3dStrides=util_exports.computeStrides(a3d.shape),b3dStrides=util_exports.computeStrides(b3d.shape),[aBatch,aOuterStep,aInnerStep]=transposeA?[a3dStrides[0],1,a3dStrides[1]]:[a3dStrides[0],a3dStrides[1],1],[bInnerStep,bOuterStep,bBatch]=transposeB?[1,b3dStrides[1],b3dStrides[0]]:[b3dStrides[1],1,b3dStrides[0]],size=leftDim*rightDim,result=buffer([batchDim,leftDim,rightDim],a3d.dtype),resVals=result.values,blockSize=backend2.blockSize;for(let bi=0;bi<batchDim;bi++)for(let i0=0;i0<leftDim;i0+=blockSize)for(let j0=0;j0<rightDim;j0+=blockSize)for(let k0=0;k0<sharedDim;k0+=blockSize){let iBlock=Math.min(i0+blockSize,leftDim),jBlock=Math.min(j0+blockSize,rightDim),kBlock=Math.min(k0+blockSize,sharedDim);for(let i=i0;i<iBlock;i++)for(let j=j0;j<jBlock;j++){let sum6=0;for(let k=k0;k<kBlock;k++){let batchOffsetA=Math.min(bi,batchDimA-1)*aBatch,batchOffsetB=Math.min(bi,batchDimB-1)*bBatch,aVal=a3dValues[batchOffsetA+i*aOuterStep+k*aInnerStep],bVal=b3dValues[k*bInnerStep+j*bOuterStep+batchOffsetB];sum6+=aVal*bVal}resVals[bi*size+(i*rightDim+j)]+=sum6}}return backend2.disposeIntermediateTensorInfo(a3d),backend2.disposeIntermediateTensorInfo(b3d),backend2.makeTensorInfo(outShape,result.dtype,result.values)}var batchMatMulConfig={kernelName:BatchMatMul,backendName:"cpu",kernelFunc:batchMatMul};function _fusedMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs,{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,current,addRes,activationRes,intermediates=[];current=batchMatMul({inputs:{a,b},attrs:{transposeA,transposeB},backend:backend2}),bias&&(addRes=add4({inputs:{a:current,b:bias},backend:backend2}),intermediates.push(current),current=addRes),activation2&&(activationRes=applyActivation2(backend2,current,activation2,preluActivationWeights,leakyreluAlpha),intermediates.push(current),current=activationRes);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return current}var _fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"cpu",kernelFunc:_fusedMatMul},acos2=unaryKernelFunc(Acos,xi=>Math.acos(xi)),acosConfig={kernelName:Acos,backendName:"cpu",kernelFunc:acos2},acosh2=unaryKernelFunc(Acosh,xi=>Math.acosh(xi)),acoshConfig={kernelName:Acosh,backendName:"cpu",kernelFunc:acosh2};function addN2(args){let{inputs,backend:backend2}=args,tensors=inputs;assertNotComplex(inputs,"addN");let vals=tensors.map(t=>backend2.data.get(t.dataId).values),outBuf=buffer(tensors[0].shape,tensors[0].dtype),outVals=outBuf.values;for(let i=0;i<tensors.length;i++){let currVals=vals[i];for(let j=0;j<outVals.length;j++)outVals[j]+=currVals[j]}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var addNConfig={kernelName:AddN,backendName:"cpu",kernelFunc:addN2};function all2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"all");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("all",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,all4=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];all4=all4&&value}vals[i]=all4}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var allConfig={kernelName:All,backendName:"cpu",kernelFunc:all2};function any2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"any");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("any",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,anyVal=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];anyVal=anyVal||value}vals[i]=anyVal}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var anyConfig={kernelName:Any,backendName:"cpu",kernelFunc:any2};function argMax2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMax");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMax",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset],maxIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value,maxIndex=j)}vals[i]=maxIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMaxConfig={kernelName:ArgMax,backendName:"cpu",kernelFunc:argMax2};function argMin2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMin");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMin",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset],minIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value,minIndex=j)}vals[i]=minIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMinConfig={kernelName:ArgMin,backendName:"cpu",kernelFunc:argMin2},asin2=unaryKernelFunc(Asin,xi=>Math.asin(xi)),asinConfig={kernelName:Asin,backendName:"cpu",kernelFunc:asin2},asinh2=unaryKernelFunc(Asinh,xi=>Math.asinh(xi)),asinhConfig={kernelName:Asinh,backendName:"cpu",kernelFunc:asinh2},atan3=unaryKernelFunc(Atan,xi=>Math.atan(xi)),atanConfig={kernelName:Atan,backendName:"cpu",kernelFunc:atan3},atan2Impl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.atan2(aValue,bValue)),atan22=binaryKernelFunc(Atan2,atan2Impl),atan2Config={kernelName:Atan2,backendName:"cpu",kernelFunc:atan22},atanh2=unaryKernelFunc(Atanh,xi=>Math.atanh(xi)),atanhConfig={kernelName:Atanh,backendName:"cpu",kernelFunc:atanh2};function pool2(xValues,xShape,dtype,strides,convInfo,poolType){let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3],outputRowStrides=convInfo.outShape[2]*convInfo.outShape[3],outputColStrides=convInfo.outShape[3];for(let b=0;b<convInfo.batchSize;++b){let outputBatchOffset=b*outputBatchStrides,inputBatchOffset=b*strides[0];for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=Math.max(0,xRCorner),xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner),outputRowOffset=outputBatchOffset+yR*outputRowStrides;for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=Math.max(0,xCCorner),xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),minMaxValue=initialValue,avgValue=0,count2=0;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let xROffset=inputBatchOffset+xR*strides[1];for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let xCOffset=xROffset+xC*strides[2],pixel=xValues[xCOffset+d];poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++)}if(isNaN(minMaxValue))break}let outputOffset=outputRowOffset+yC*outputColStrides+d;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}return output}function maxPoolPositions(xValues,xShape,dtype,convInfo,flattenPositions=!1,includeBatchInIndex=!1){let maxPositions=buffer(convInfo.outShape,"int32"),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=buffer(xShape,dtype,xValues);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=xRCorner;for(;xRMin<0;)xRMin+=dilationHeight;let xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner);for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=xCCorner;for(;xCMin<0;)xCMin+=dilationWidth;let xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let wR=xR-xRCorner;for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let wC=xC-xCCorner,pixel=xBuf.get(b,xR,xC,d);pixel>maxValue&&(maxValue=pixel,flattenPositions?maxPosition=includeBatchInIndex?((b*convInfo.inHeight+xR)*convInfo.inWidth+xC)*convInfo.inChannels+d:(xR*convInfo.inWidth+xC)*convInfo.inChannels+d:maxPosition=wR*effectiveFilterWidth+wC)}}maxPositions.set(maxPosition,b,yR,yC,d)}}return maxPositions}function pool3d2(xValues,xShape,dtype,strides,convInfo,poolType){let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputDepthStrides=convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputRowStrides=convInfo.outShape[3]*convInfo.outShape[4],outputColStrides=convInfo.outShape[4];for(let batch=0;batch<convInfo.batchSize;++batch){let outputBatchOffset=batch*outputBatchStrides,inputBatchOffset=batch*strides[0];for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner),outputDepthOffset=outputBatchOffset+yDepth*outputDepthStrides;for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner),outputRowOffset=outputDepthOffset+yRow*outputRowStrides;for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),outputColOffset=outputRowOffset+yCol*outputColStrides,minMaxValue=initialValue,avgValue=0,count2=0;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let xDepthOffset=inputBatchOffset+xDepth*strides[1];for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let xRowOffset=xDepthOffset+xRow*strides[2];for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let xColOffset=xRowOffset+xCol*strides[3],pixel=xValues[xColOffset+channel];if(poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++),isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}let outputOffset=outputColOffset+channel;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}}return output}function maxPool3dPositions(xBuf,convInfo){let maxPositions=buffer(convInfo.outShape,"int32"),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner);for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner);for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let wDepth=xDepth-xDepthCorner;for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let wRow=xRow-xRowCorner;for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let wCol=xCol-xColCorner,pixel=xBuf.get(batch,xDepth,xRow,xCol,channel);pixel>=maxValue&&(maxValue=pixel,maxPosition=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterHeight+wCol)}}}maxPositions.set(maxPosition,batch,yDepth,yRow,yCol,channel)}}}return maxPositions}function avgPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"avgPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"avg");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var avgPoolConfig={kernelName:AvgPool,backendName:"cpu",kernelFunc:avgPool2};function avgPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"avgPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"avg");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var avgPool3DConfig={kernelName:AvgPool3D,backendName:"cpu",kernelFunc:avgPool3D};function avgPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"avgPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),avgMultiplier=1/(filterDepth*filterHeight*filterWidth),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol||(dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel))}}}dx.set(dotProd*avgMultiplier,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPool3DGradConfig2={kernelName:AvgPool3DGrad,backendName:"cpu",kernelFunc:avgPool3DGrad};function avgPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex([dy,input2],"avgPoolGrad");let{filterSize,strides,pad:pad3}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterHeight*filterWidth),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC||(dotProd+=dyBuf.get(b,dyR,dyC,d))}}dx.set(dotProd*avgMultiplier,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPoolGradConfig2={kernelName:AvgPoolGrad,backendName:"cpu",kernelFunc:avgPoolGrad2};function batchNorm2(args){let{inputs,backend:backend2,attrs}=args,{x,scale:scale22,offset,mean:mean4,variance}=inputs;util_exports.assert(mean4.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean4.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale22==null||mean4.shape.length===scale22.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),assertNotComplex([x,mean4,variance,scale22,offset],"batchNorm");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let xVals=backend2.data.get(x.dataId).values,mVals=backend2.data.get(mean4.dataId).values,varVals=backend2.data.get(variance.dataId).values,sVals=scale22?backend2.data.get(scale22.dataId).values:new Float32Array([1]),offVals=offset?backend2.data.get(offset.dataId).values:new Float32Array([0]),outVals=new Float32Array(xVals.length),offValsLength=offVals.length,sValsLength=sVals.length,varValsLength=varVals.length,mValsLength=mVals.length,offi=0,mi=0,si=0,vi=0;for(let i=0;i<xVals.length;++i)outVals[i]=offVals[offi++]+(xVals[i]-mVals[mi++])*sVals[si++]/Math.sqrt(varVals[vi++]+varianceEpsilon),offi>=offValsLength&&(offi=0),mi>=mValsLength&&(mi=0),si>=sValsLength&&(si=0),vi>=varValsLength&&(vi=0);return backend2.makeTensorInfo(x.shape,x.dtype,outVals)}var batchNormConfig={kernelName:FusedBatchNorm,backendName:"cpu",kernelFunc:batchNorm2};function batchToSpaceND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;assertNotComplex([x],"batchToSpaceND");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),xReshaped=reshape3({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),xTransposed=transpose2({inputs:{x:xReshaped},backend:backend2,attrs:{perm:permuted}}),xTransposedReshaped=reshape3({inputs:{x:xTransposed},backend:backend2,attrs:{shape:reshapedPermuted}}),result=slice2({inputs:{x:xTransposedReshaped},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return backend2.disposeIntermediateTensorInfo(xReshaped),backend2.disposeIntermediateTensorInfo(xTransposed),backend2.disposeIntermediateTensorInfo(xTransposedReshaped),result}var batchToSpaceNDConfig={kernelName:BatchToSpaceND,backendName:"cpu",kernelFunc:batchToSpaceND2};function bincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig={kernelName:Bincount,backendName:"cpu",kernelFunc:bincount2},clip=unaryKernelFunc(ClipByValue,(xi,attrs)=>{let clipAttrs=attrs;return xi>clipAttrs.clipValueMax?clipAttrs.clipValueMax:xi<clipAttrs.clipValueMin?clipAttrs.clipValueMin:xi}),clipConfig={kernelName:ClipByValue,backendName:"cpu",kernelFunc:clip},complexAbs=args=>{let{x}=args.inputs,cpuBackend=args.backend,resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),complexVals=cpuBackend.data.get(x.dataId),real4=complexVals.complexTensorInfos.real,imag4=complexVals.complexTensorInfos.imag,realVals=cpuBackend.data.get(real4.dataId).values,imagVals=cpuBackend.data.get(imag4.dataId).values;for(let i=0;i<realVals.length;i++){let real5=realVals[i],imag5=imagVals[i];resultValues[i]=Math.hypot(real5,imag5)}return cpuBackend.makeOutput(resultValues,x.shape,"float32")},complexAbsConfig={kernelName:ComplexAbs,backendName:"cpu",kernelFunc:complexAbs};function imag2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,imag4=backend2.data.get(input2.dataId).complexTensorInfos.imag,imagVal=backend2.data.get(imag4.dataId).values;return backend2.makeTensorInfo(imag4.shape,imag4.dtype,imagVal)}var imagConfig={kernelName:Imag,backendName:"cpu",kernelFunc:imag2};function concat2(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity2({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,$axis),$inputs[0].dtype==="complex64"){let reals=$inputs.map(t=>real2({inputs:{input:t},backend:backend2})),imags=$inputs.map(t=>imag2({inputs:{input:t},backend:backend2})),realConcated=concat2({inputs:reals,backend:backend2,attrs:{axis:$axis}}),imagConcated=concat2({inputs:imags,backend:backend2,attrs:{axis:$axis}}),result=complex2({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result}let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice($axis));return reshape3({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.data.get(t.dataId).values,shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),$axis),outInfo=backend2.makeTensorInfo(finalOutShape,inputs[0].dtype,outVals);return inputs2D.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}var concatConfig={kernelName:Concat,backendName:"cpu",kernelFunc:concat2};function conv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"conv2d");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,padLeft=convInfo.padInfo.left,padTop=convInfo.padInfo.top,isChannelsLast=convInfo.dataFormat==="channelsLast",y=new TensorBuffer(convInfo.outShape,x.dtype),xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),xBatchStride=xStrides[0],xRowStride=isChannelsLast?xStrides[1]:xStrides[2],xColStride=isChannelsLast?xStrides[2]:1,xChannelStride=isChannelsLast?1:xStrides[1],yBatchStride=y.strides[0],yRowStride=isChannelsLast?y.strides[1]:y.strides[2],yColStride=isChannelsLast?y.strides[2]:1,yChannelStride=isChannelsLast?1:y.strides[1],xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xBatchStride,yOffset1=b*yBatchStride;for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*yRowStride,xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(!(xR<0||xR>=convInfo.inHeight)){let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xRowStride;for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*yColStride,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(!(xC<0||xC>=convInfo.inWidth)){let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*xColStride,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1*xChannelStride];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset3+d2*yChannelStride]+=xVal*wVals[wOffset3+d2];wOffset3+=convInfo.outChannels}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,yVals)}var conv2DConfig={kernelName:Conv2D,backendName:"cpu",kernelFunc:conv2D};function conv2DBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"conv2dBackpropFilter");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,isChannelsLast=convInfo.dataFormat==="channelsLast",dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,xVals=backend2.data.get(x.dataId).values,dyVals=backend2.data.get(dy.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d1=0;d1<convInfo.inChannels;++d1)for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;isChannelsLast?dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2):dotProd+=xBuf.get(b,d1,xR,xC)*dyBuf.get(b,d2,yR,yC)}}dW.set(dotProd,wR,wC,d1,d2)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var conv2DBackpropFilterConfig={kernelName:Conv2DBackpropFilter,backendName:"cpu",kernelFunc:conv2DBackpropFilter2};function conv2DBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{inputShape,strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;assertNotComplex([dy,filter],"conv2dBackpropInput");let filterStrides=util_exports.computeStrides(filter.shape),dyStrides=util_exports.computeStrides(dy.shape),$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,dyValues=backend2.data.get(dy.dataId).values,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo;$dataFormat=convInfo.dataFormat;let topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=$dataFormat==="channelsLast",xBatchStride=dx.strides[0],xRowStride=isChannelsLast?dx.strides[1]:dx.strides[2],xColStride=isChannelsLast?dx.strides[2]:1,xChannelStride=isChannelsLast?1:dx.strides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1];for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=yBatchStride*b+yRowStride*yR+yColStride*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+yChannelStride*d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}let dxOffset=xBatchStride*b+xRowStride*xR+xColStride*xC+xChannelStride*d1;dxValues[dxOffset]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv2DBackpropInputConfig={kernelName:Conv2DBackpropInput,backendName:"cpu",kernelFunc:conv2DBackpropInput2};function conv3D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs;assertNotComplex([x,filter],"conv3d");let convInfo=backend_util_exports.computeConv3DInfo(x.shape,filter.shape,strides,dilations,pad3),{filterDepth,filterHeight,filterWidth,dilationDepth,dilationHeight,dilationWidth,padInfo}=convInfo,padFront=padInfo.front,padLeft=padInfo.left,padTop=padInfo.top,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values,xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yF=0;yF<convInfo.outDepth;++yF){let yOffset2=yOffset1+yF*y.strides[1],xFCorner=yF*convInfo.strideDepth-padFront;for(let wF=0;wF<filterDepth;++wF){let xF=xFCorner+wF*dilationDepth;if(!(xF<0||xF>=convInfo.inDepth)){let wOffset1=wF*filterStrides[0],xOffset2=xOffset1+xF*xStrides[1];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset3=yOffset2+yR*y.strides[2],xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(!(xR<0||xR>=convInfo.inHeight)){let wOffset2=wOffset1+wR*filterStrides[1],xOffset3=xOffset2+xR*xStrides[2];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset4=yOffset3+yC*convInfo.outChannels,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(!(xC<0||xC>=convInfo.inWidth)){let wOffset3=wOffset2+wC*filterStrides[2],xOffset4=xOffset3+xC*convInfo.inChannels,wOffset4=wOffset3;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset4+d1];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset4+d2]+=xVal*wVals[wOffset4+d2];wOffset4+=convInfo.outChannels}}}}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var conv3DConfig={kernelName:Conv3D,backendName:"cpu",kernelFunc:conv3D};function conv3DBackpropFilterV2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,filterShape}=attrs;assertNotComplex([x,dy],"conv3dBackpropFilterV2");let xStrides=util_exports.computeStrides(x.shape),dyStrides=util_exports.computeStrides(dy.shape),convInfo=backend_util_exports.computeConv3DInfo(x.shape,filterShape,strides,1,pad3),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dw=new TensorBuffer(convInfo.filterShape,"float32"),dwValues=dw.values,[dwS0,dwS1,dwS2,dwS3]=dw.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,xValues=backend2.data.get(x.dataId).values,[xS0,xS1,xS2,xS3]=xStrides,frontPad=convInfo.padInfo.front,leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top;for(let wF=0;wF<filterDepth;++wF){let yFMin=Math.max(0,Math.ceil((frontPad-wF)/strideDepth)),yFMax=Math.min(convInfo.outDepth,(convInfo.inDepth+frontPad-wF)/strideDepth),wOffset1=wF*dwS0;for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight),wOffset2=wR*dwS1+wOffset1;for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth),wOffset3=wC*dwS2+wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let wOffset4=d1*dwS3+wOffset3;for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xS0,yOffset1=b*dyS0;for(let yF=yFMin;yF<yFMax;++yF){let xOffset2=(wF+yF*strideDepth-frontPad)*xS1+xOffset1,yOffset2=yF*dyS1+yOffset1;for(let yR=yRMin;yR<yRMax;++yR){let xOffset3=(wR+yR*strideHeight-topPad)*xS2+xOffset2,yOffset3=yR*dyS2+yOffset2;for(let yC=yCMin;yC<yCMax;++yC){let xOffset4=(wC+yC*strideWidth-leftPad)*xS3+xOffset3,yOffset4=yC*dyS3+yOffset3;dotProd+=xValues[xOffset4+d1]*dyValues[yOffset4+d2]}}}}dwValues[wOffset4+d2]=dotProd}}}}}return backend2.makeTensorInfo(dw.shape,dw.dtype,dw.values)}var conv3DBackpropFilterV2Config={kernelName:Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:conv3DBackpropFilterV2};function conv3DBackpropInputV2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{pad:pad3,strides,inputShape}=attrs;assertNotComplex([dy],"conv3dBackpropInputV2");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv3DInfo(inputShape,filter.shape,strides,1,pad3),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2,dxS3]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2,fltS3]=filterStrides,{batchSize,filterDepth,filterHeight,filterWidth,inChannels,inDepth,inHeight,inWidth,outChannels,outDepth,outHeight,outWidth,strideDepth,strideHeight,strideWidth}=convInfo,frontPad=filterDepth-1-convInfo.padInfo.front,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xF=0;xF<inDepth;++xF){let xFCorner=xF-frontPad,xFMin=Math.max(0,Math.ceil(xFCorner/strideDepth)),yFMax=Math.min(outDepth,(filterDepth+xFCorner)/strideDepth);for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yF=xFMin;yF<yFMax;++yF){let wF=yF*strideDepth-xFCorner;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yF+dyS2*yR+dyS3*yC,fltOffset=fltS0*(filterDepth-1-wF)+fltS1*(filterHeight-1-wR)+fltS2*(filterWidth-1-wC)+fltS3*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}}dxValues[dxS0*b+dxS1*xF+dxS2*xR+dxS3*xC+d1]=dotProd}}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv3DBackpropInputV2Config={kernelName:Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:conv3DBackpropInputV2},cos2=unaryKernelFunc(Cos,xi=>Math.cos(xi)),cosConfig={kernelName:Cos,backendName:"cpu",kernelFunc:cos2},cosh2=unaryKernelFunc(Cosh,xi=>Math.cosh(xi)),coshConfig={kernelName:Cosh,backendName:"cpu",kernelFunc:cosh2};function cropAndResize2(args){let{inputs,backend:backend2,attrs}=args,{image:image3,boxes,boxInd}=inputs,{cropSize,method,extrapolationValue}=attrs,[batch,imageHeight,imageWidth,numChannels]=image3.shape,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,output=buffer([numBoxes,cropHeight,cropWidth,numChannels],"float32"),boxVals=backend2.data.get(boxes.dataId).values,boxIndVals=backend2.data.get(boxInd.dataId).values,imageVals=backend2.data.get(image3.dataId).values,inStride=util_exports.computeStrides(image3.shape),outStride=util_exports.computeStrides(output.shape);for(let b=0;b<numBoxes;b++){let startInd=b*4,y1=boxVals[startInd],x1=boxVals[startInd+1],y2=boxVals[startInd+2],x2=boxVals[startInd+3],bInd=boxIndVals[b];if(!(bInd>=batch)){let heightScale=cropHeight>1?(y2-y1)*(imageHeight-1)/(cropHeight-1):0,widthScale=cropWidth>1?(x2-x1)*(imageWidth-1)/(cropWidth-1):0;for(let y=0;y<cropHeight;y++){let yInd=cropHeight>1?y1*(imageHeight-1)+y*heightScale:.5*(y1+y2)*(imageHeight-1);if(yInd<0||yInd>imageHeight-1){for(let x=0;x<cropWidth;x++)for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}if(method==="bilinear"){let topInd=Math.floor(yInd),bottomInd=Math.ceil(yInd),yLerp=yInd-topInd;for(let x=0;x<cropWidth;x++){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let leftInd=Math.floor(xInd),rightInd=Math.ceil(xInd),xLerp=xInd-leftInd;for(let c=0;c<numChannels;c++){let ind=c+leftInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0],topLeft=imageVals[ind];ind=c+rightInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];let topRight=imageVals[ind];ind=c+leftInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomLeft=imageVals[ind];ind=c+rightInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomRight=imageVals[ind],top=topLeft+(topRight-topLeft)*xLerp,bottom=bottomLeft+(bottomRight-bottomLeft)*xLerp;ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0],output.values[ind]=top+(bottom-top)*yLerp}}}else for(let x=0;x<cropWidth;++x){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let closestX=Math.round(xInd),closestY=Math.round(yInd);for(let c=0;c<numChannels;c++){let inInd=c+closestX*inStride[2]+closestY*inStride[1]+bInd*inStride[0],outInd=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[outInd]=imageVals[inInd]}}}}}return backend2.makeTensorInfo(output.shape,output.dtype,output.values)}var cropAndResizeConfig={kernelName:CropAndResize,backendName:"cpu",kernelFunc:cropAndResize2};function cumsum2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs;assertNotComplex(x,"cumsum");let permutation=backend_util_exports.getAxesPermutation([axis],x.shape.length),$x=x;permutation!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,x.shape.length)[0];if(permutedAxis!==$x.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length-1} but got axis=${permutedAxis}`);let resultDtype=upcastType($x.dtype,"int32"),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape),resultDtype),aVals=backend2.data.get($x.dataId).values,finalDim=$x.shape[$x.shape.length-1],indexAdjuster=reverse5?(i,j)=>i+finalDim-j-1:(i,j)=>i+j;for(let i=0;i<aVals.length;i+=finalDim)for(let j=0;j<finalDim;j++){let idx=indexAdjuster(i,j);if(j===0)vals[idx]=exclusive?0:aVals[idx];else{let prevIdx=indexAdjuster(i,j-1);vals[idx]=exclusive?aVals[prevIdx]+vals[prevIdx]:aVals[idx]+vals[prevIdx]}}let result=backend2.makeTensorInfo($x.shape,resultDtype,vals);if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose2({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo($x),reverseTransposedResult}return result}var cumsumConfig={kernelName:Cumsum,backendName:"cpu",kernelFunc:cumsum2};function denseBincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig={kernelName:DenseBincount,backendName:"cpu",kernelFunc:denseBincount2};function depthToSpace2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(dataFormat==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`),util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=x.shape[1],inputWidth=x.shape[2],inputDepth=x.shape[3],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),xValues=backend2.data.get(x.dataId).values,result=new Float32Array(batchSize*outputHeight*outputWidth*outputDepth),outputIdx=0;for(let b=0;b<batchSize;++b)for(let h=0;h<outputHeight;++h){let inH=Math.floor(h/blockSize),offsetH=h%blockSize;for(let w=0;w<outputWidth;++w){let inW=Math.floor(w/blockSize),offsetW=w%blockSize,offsetD=(offsetH*blockSize+offsetW)*outputDepth;for(let d=0;d<outputDepth;++d){let inputIdx=d+offsetD+inputDepth*(inW+inputWidth*(inH+inputHeight*b));result[outputIdx++]=xValues[inputIdx]}}}return backend2.makeTensorInfo([batchSize,outputHeight,outputWidth,outputDepth],x.dtype,result)}var depthToSpaceConfig={kernelName:DepthToSpace,backendName:"cpu",kernelFunc:depthToSpace2};function depthwiseConv2dNative(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"depthwiseConv2DNative");let xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),{filterHeight,filterWidth,dilationHeight,dilationWidth,padInfo}=convInfo,padLeft=padInfo.left,padTop=padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*y.strides[1],xRCorner=yR*convInfo.strideHeight-padLeft;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(!(xR<0||xR>=convInfo.inHeight)){let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xStrides[1];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*y.strides[2],xCCorner=yC*convInfo.strideWidth-padTop;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(!(xC<0||xC>=convInfo.inWidth)){let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*convInfo.inChannels,yOffset4=yOffset3,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1];for(let q=0;q<chMul;++q)yVals[yOffset4+q]+=xVal*wVals[wOffset3+q];yOffset4+=chMul,wOffset3+=chMul}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var depthwiseConv2dNativeConfig={kernelName:DepthwiseConv2dNative,backendName:"cpu",kernelFunc:depthwiseConv2dNative};function depthwiseConv2dNativeBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"depthwiseConv2dNativeBackpropFilter");let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,dilations,pad3,dimRoundingMode,!0),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,xVals=backend2.data.get(x.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyVals=backend2.data.get(dy.dataId).values,dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d2=0;d2<convInfo.outChannels;++d2){let d1=Math.trunc(d2/chMul),dm=d2%chMul,dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2)}}dW.set(dotProd,wR,wC,d1,dm)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var depthwiseConv2dNativeBackpropFilterConfig={kernelName:DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropFilter2};function depthwiseConv2dNativeBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,inputShape}=attrs;assertNotComplex([dy,filter],"depthwiseConv2DNativeBackpropInput");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,chMul=outChannels/inChannels;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yR+dyS2*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let dm=0;dm<chMul;++dm){let d2=d1*chMul+dm,pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+dm];dotProd+=pixel*weight}}}dxValues[dxS0*b+dxS1*xR+dxS2*xC+d1]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var depthwiseConv2dNativeBackpropInputConfig={kernelName:DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropInput2};function diag2(args){let{inputs,backend:backend2}=args,{x}=inputs,xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outBuf=buffer([xSize,xSize],x.dtype),vals=outBuf.values;for(let i=0;i<xVals.length;i++)vals[i*xSize+i]=xVals[i];let outShape=[...x.shape,...x.shape];return backend2.makeTensorInfo(outShape,outBuf.dtype,outBuf.values)}var diagConfig={kernelName:Diag,backendName:"cpu",kernelFunc:diag2},dilation2dConfig={kernelName:Dilation2D,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,xVals=cpuBackend.data.get(x.dataId).values,xRank=x.shape.length,filterVals=cpuBackend.data.get(filter.dataId).values,filterRank=filter.shape.length,{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations),outSize=util_exports.sizeFromShape(outShape),outRank=outShape.length,outputVals=util_exports.getArrayFromDType(x.dtype,outSize);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let xIndex=util_exports.locToIndex([b,hIn,wIn,d],xRank,util_exports.computeStrides(x.shape)),filterIndex=util_exports.locToIndex([h,w,d],filterRank,util_exports.computeStrides(filter.shape)),val=xVals[xIndex]+filterVals[filterIndex];val>curVal&&(curVal=val)}}}let outputIndex=util_exports.locToIndex([b,hOut,wOut,d],outRank,util_exports.computeStrides(outShape));outputVals[outputIndex]=curVal}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(outputVals,x.dtype),outShape,x.dtype),shape:outShape,dtype:x.dtype}}},dilation2dBackpropFilterConfig={kernelName:Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(filter.shape,filter.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hMax=0,wMax=0;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hMax=h,wMax=w)}}}gradients[hMax][wMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),filter.shape,filter.dtype),shape:filter.shape,dtype:filter.dtype}}},dilation2dBackpropInputConfig={kernelName:Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(x.shape,x.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hInMax=hBeg<0?0:hBeg,wInMax=wBeg<0?0:wBeg;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hInMax=hIn,wInMax=wIn)}}}gradients[b][hInMax][wInMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}};function eluGrad(args){let{inputs,backend:backend2}=args,{dy,y}=inputs;assertNotComplex([dy,y],"eluGrad");let resultValues=new Float32Array(util_exports.sizeFromShape(y.shape)),values=backend2.data.get(y.dataId).values,dyValues=backend2.data.get(dy.dataId).values;for(let i=0;i<values.length;++i){let v=values[i];v>=1?resultValues[i]=dyValues[i]:resultValues[i]=dyValues[i]*(v+1)}return backend2.makeTensorInfo(y.shape,"float32",resultValues)}var eluGradConfig2={kernelName:EluGrad,backendName:"cpu",kernelFunc:eluGrad},equalImpl=createSimpleBinaryKernelImpl((a,b)=>a===b?1:0),equal2=binaryKernelFunc(Equal,equalImpl,null,"bool"),equalConfig={kernelName:Equal,backendName:"cpu",kernelFunc:equal2},p=backend_util_exports.ERF_P,a1=backend_util_exports.ERF_A1,a2=backend_util_exports.ERF_A2,a3=backend_util_exports.ERF_A3,a4=backend_util_exports.ERF_A4,a5=backend_util_exports.ERF_A5,erf2=unaryKernelFunc(Erf,xi=>{let sign4=Math.sign(xi),v=Math.abs(xi),t=1/(1+p*v);return sign4*(1-((((a5*t+a4)*t+a3)*t+a2)*t+a1)*t*Math.exp(-v*v))}),erfConfig={kernelName:Erf,backendName:"cpu",kernelFunc:erf2};function expandDims3(args){let{inputs,backend:backend2,attrs}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig={kernelName:ExpandDims,backendName:"cpu",kernelFunc:expandDims3},realDivImpl=createSimpleBinaryKernelImpl((a,b)=>a/b),div2=binaryKernelFunc(RealDiv,realDivImpl),realDivConfig={kernelName:RealDiv,backendName:"cpu",kernelFunc:div2};function fftBatch(input2,inverse,cpuBackend){let inputShape=input2.shape,batch=inputShape[0],innerDim=inputShape[1],inputVals=cpuBackend.data.get(input2.dataId),real2D=inputVals.complexTensorInfos.real,imag2D=inputVals.complexTensorInfos.imag,resultShape=[batch,innerDim],resultSize=util_exports.sizeFromShape(resultShape),resultReal=util_exports.getTypedArrayFromDType("float32",resultSize),resultImag=util_exports.getTypedArrayFromDType("float32",resultSize);for(let b=0;b<batch;b++){let r=slice2({inputs:{x:real2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),i=slice2({inputs:{x:imag2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),input3=complex2({inputs:{real:r,imag:i},backend:cpuBackend}),{real:real4,imag:imag4}=fftImpl(input3,inverse,cpuBackend),res=backend_util_exports.mergeRealAndImagArrays(real4,imag4);for(let d=0;d<innerDim;d++){let c=backend_util_exports.getComplexWithIndex(res,d);resultReal[b*innerDim+d]=c.real,resultImag[b*innerDim+d]=c.imag}cpuBackend.disposeIntermediateTensorInfo(r),cpuBackend.disposeIntermediateTensorInfo(i),cpuBackend.disposeIntermediateTensorInfo(input3)}let $realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultReal),$imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultImag),result=complex2({inputs:{real:$realInfo,imag:$imagInfo},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($realInfo),cpuBackend.disposeIntermediateTensorInfo($imagInfo),result}function fftImpl(input2,inverse,cpuBackend){let inputSize=util_exports.sizeFromShape(input2.shape),inputVals=cpuBackend.data.get(input2.dataId),realVals=cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values,imagVals=cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;if(isExponentOf2(inputSize)){let result=fftRadix2(realVals,imagVals,inputSize,inverse,cpuBackend),resultShape=[input2.shape[0],input2.shape[1]];if(inverse){let realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.real),imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.imag),sizeInfo=cpuBackend.makeTensorInfo([],"float32",util_exports.createScalarValue(inputSize,"float32")),sizeInfoCopy=identity2({inputs:{x:sizeInfo},backend:cpuBackend}),divRealInfo=realDivConfig.kernelFunc({inputs:{a:realInfo,b:sizeInfo},backend:cpuBackend}),divImagInfo=realDivConfig.kernelFunc({inputs:{a:imagInfo,b:sizeInfoCopy},backend:cpuBackend}),divRealVals=cpuBackend.data.get(divRealInfo.dataId).values,divImagVals=cpuBackend.data.get(divImagInfo.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(realInfo),cpuBackend.disposeIntermediateTensorInfo(imagInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy),cpuBackend.disposeIntermediateTensorInfo(divRealInfo),cpuBackend.disposeIntermediateTensorInfo(divImagInfo),{real:divRealVals,imag:divImagVals}}return result}else{let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),rawOutput=fourierTransformByMatmul(data,inputSize,inverse);return backend_util_exports.splitRealAndImagArrays(rawOutput)}}function isExponentOf2(size){return(size&size-1)===0}function fftRadix2(realVals,imagVals,size,inverse,cpuBackend){if(size===1)return{real:realVals,imag:imagVals};let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),half=size/2,evenComplex=backend_util_exports.complexWithEvenIndex(data),evenRealVals=evenComplex.real,evenImagVals=evenComplex.imag,evenShape=[evenRealVals.length],evenRealInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenRealVals),evenImagInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenImagVals),evenTensorInfo=complex2({inputs:{real:evenRealInfo,imag:evenImagInfo},backend:cpuBackend}),oddComplex=backend_util_exports.complexWithOddIndex(data),oddRealVals=oddComplex.real,oddImagVals=oddComplex.imag,oddShape=[oddRealVals.length],oddRealInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddRealVals),oddImagInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddImagVals),oddTensorInfo=complex2({inputs:{real:oddRealInfo,imag:oddImagInfo},backend:cpuBackend}),$evenComplex=fftRadix2(evenRealVals,evenImagVals,half,inverse,cpuBackend),$evenRealVals=$evenComplex.real,$evenImagVals=$evenComplex.imag,$evenShape=[$evenRealVals.length],$evenRealInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenRealVals),$evenImagInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenImagVals),$evenTensorInfo=complex2({inputs:{real:$evenRealInfo,imag:$evenImagInfo},backend:cpuBackend}),$oddComplex=fftRadix2(oddRealVals,oddImagVals,half,inverse,cpuBackend),$oddRealVals=$oddComplex.real,$oddImagVals=$oddComplex.imag,$oddShape=[$oddRealVals.length],$oddRealInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddRealVals),$oddImagInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddImagVals),$oddTensorInfo=complex2({inputs:{real:$oddRealInfo,imag:$oddImagInfo},backend:cpuBackend}),e=backend_util_exports.exponents(size,inverse),eShape=[e.real.length],eRealInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.real),eImagInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.imag),complexInfo=complex2({inputs:{real:eRealInfo,imag:eImagInfo},backend:cpuBackend}),exponentInfo=multiply2({inputs:{a:complexInfo,b:$oddTensorInfo},backend:cpuBackend}),addPart=add4({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),subPart=sub2({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),addPartReal=real2({inputs:{input:addPart},backend:cpuBackend}),subPartReal=real2({inputs:{input:subPart},backend:cpuBackend}),addPartImag=imag2({inputs:{input:addPart},backend:cpuBackend}),subPartImag=imag2({inputs:{input:subPart},backend:cpuBackend}),$real=concat2({inputs:[addPartReal,subPartReal],backend:cpuBackend,attrs:{axis:0}}),$imag=concat2({inputs:[addPartImag,subPartImag],backend:cpuBackend,attrs:{axis:0}}),$realVals=cpuBackend.data.get($real.dataId).values,$imagVals=cpuBackend.data.get($imag.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(evenRealInfo),cpuBackend.disposeIntermediateTensorInfo(evenImagInfo),cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo(oddRealInfo),cpuBackend.disposeIntermediateTensorInfo(oddImagInfo),cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo($evenRealInfo),cpuBackend.disposeIntermediateTensorInfo($evenImagInfo),cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo($oddRealInfo),cpuBackend.disposeIntermediateTensorInfo($oddImagInfo),cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo(eRealInfo),cpuBackend.disposeIntermediateTensorInfo(eImagInfo),cpuBackend.disposeIntermediateTensorInfo(complexInfo),cpuBackend.disposeIntermediateTensorInfo(exponentInfo),cpuBackend.disposeIntermediateTensorInfo(addPart),cpuBackend.disposeIntermediateTensorInfo(subPart),cpuBackend.disposeIntermediateTensorInfo(addPartReal),cpuBackend.disposeIntermediateTensorInfo(addPartImag),cpuBackend.disposeIntermediateTensorInfo(subPartReal),cpuBackend.disposeIntermediateTensorInfo(subPartImag),cpuBackend.disposeIntermediateTensorInfo($real),cpuBackend.disposeIntermediateTensorInfo($imag),{real:$realVals,imag:$imagVals}}function fourierTransformByMatmul(data,size,inverse){let ret=new Float32Array(size*2);for(let r=0;r<size;r++){let real4=0,imag4=0;for(let c=0;c<size;c++){let e=backend_util_exports.exponent(r*c,size,inverse),term=backend_util_exports.getComplexWithIndex(data,c);real4+=term.real*e.real-term.imag*e.imag,imag4+=term.real*e.imag+term.imag*e.real}inverse&&(real4/=size,imag4/=size),backend_util_exports.assignToTypedArray(ret,real4,imag4,r)}return ret}function fft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!1,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var fftConfig={kernelName:FFT,backendName:"cpu",kernelFunc:fft2};function fill2(args){let{backend:backend2,attrs}=args,{shape,value,dtype}=attrs,$dtype=dtype||util_exports.inferDtype(value),values=util_exports.getArrayFromDType($dtype,util_exports.sizeFromShape(shape));return fillValues(values,value,$dtype),backend2.makeTensorInfo(shape,$dtype,values)}var fillConfig={kernelName:Fill,backendName:"cpu",kernelFunc:fill2};function fillValues(values,value,dtype){values.fill(value)}var flipLeftRightConfig={kernelName:FlipLeftRight,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let x=[batch,row,col,channel][2],coordX=Math.round(imageWidth-x),outIdx=batchOffset+rowOffset+colOffset+channel,outputValue=imageVals[outIdx];if(coordX>=0&&coordX<imageWidth){let rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}},floorDivImpl=createSimpleBinaryKernelImpl((a,b)=>Math.floor(a/b)),floorDiv2=binaryKernelFunc(FloorDiv,floorDivImpl,null,"int32"),floorDivConfig={kernelName:FloorDiv,backendName:"cpu",kernelFunc:floorDiv2};function fusedConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=conv2D({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let resultOld=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(resultOld)}if(activation2){let resultOld=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(resultOld)}return result}var fusedConv2DConfig={kernelName:FusedConv2D,backendName:"cpu",kernelFunc:fusedConv2D};function fusedDepthwiseConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=depthwiseConv2dNative({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let oldResult=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(oldResult)}if(activation2){let oldResult=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(oldResult)}return result}var fusedDepthwiseConv2DConfig={kernelName:FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:fusedDepthwiseConv2D};function gatherNd(args){let{inputs,backend:backend2}=args,{params,indices}=inputs,paramsSize=util_exports.sizeFromShape(params.shape),indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(params,indices);if(numSlices===0)return backend2.makeTensorInfo(resultShape,params.dtype,[]);let outBuf=buffer([numSlices,sliceSize],params.dtype),indicesData=backend2.data.get(indices.dataId).values,paramsData=backend2.data.get(params.dataId).values;for(let i=0;i<numSlices;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];flattenIndex+=dim*strides[j],index.push(dim)}if(flattenIndex<0||flattenIndex>=paramsSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${params.shape}`);for(let k=0;k<sliceSize;k++)outBuf.values[i*sliceSize+k]=paramsData[flattenIndex*sliceSize+k]}return backend2.makeTensorInfo(resultShape,outBuf.dtype,outBuf.values)}var gatherNdConfig={kernelName:GatherNd,backendName:"cpu",kernelFunc:gatherNd};function gatherV2(args){let{inputs,backend:backend2,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs;assertNotComplex([x,indices],"gatherV2");let $batchDims=batchDims;batchDims==null&&($batchDims=0);let indicesSize=util_exports.sizeFromShape(indices.shape),parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,$batchDims),flattenX=reshape3({inputs:{x},backend:backend2,attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]}}),flattenIndex=reshape3({inputs:{x:indices},backend:backend2,attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]}}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],indicesBuf=backend2.bufferSync(flattenIndex),xBuf=backend2.bufferSync(flattenX),outBuf=gatherV2Impl(xBuf,indicesBuf,flattenOutputShape);return backend2.disposeIntermediateTensorInfo(flattenX),backend2.disposeIntermediateTensorInfo(flattenIndex),backend2.makeTensorInfo(shapeInfo.outputShape,outBuf.dtype,outBuf.values)}var gatherV2Config={kernelName:GatherV2,backendName:"cpu",kernelFunc:gatherV2},greaterEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a>=b?1:0),greaterEqual2=binaryKernelFunc(GreaterEqual,greaterEqualImpl,null,"bool"),greaterEqualConfig={kernelName:GreaterEqual,backendName:"cpu",kernelFunc:greaterEqual2};function ifft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!0,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var ifftConfig={kernelName:IFFT,backendName:"cpu",kernelFunc:ifft2},isFinite3=unaryKernelFunc(IsFinite,xi=>Number.isFinite(xi)?1:0,"bool"),isFiniteConfig={kernelName:IsFinite,backendName:"cpu",kernelFunc:isFinite3},isInf2=unaryKernelFunc(IsInf,xi=>Math.abs(xi)===Infinity?1:0,"bool"),isInfConfig={kernelName:IsInf,backendName:"cpu",kernelFunc:isInf2},isNaN3=unaryKernelFunc(IsNan,xi=>Number.isNaN(xi)?1:0,"bool"),isNaNConfig={kernelName:IsNan,backendName:"cpu",kernelFunc:isNaN3},lessEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a<=b?1:0),lessEqual2=binaryKernelFunc(LessEqual,lessEqualImpl,null,"bool"),lessEqualConfig={kernelName:LessEqual,backendName:"cpu",kernelFunc:lessEqual2};function linSpace(args){let{backend:backend2,attrs}=args,{start,stop,num}=attrs,outVals=linSpaceImpl(start,stop,num);return backend2.makeTensorInfo([outVals.length],"float32",outVals)}var linSpaceConfig={kernelName:LinSpace,backendName:"cpu",kernelFunc:linSpace},log1p2=unaryKernelFunc(Log1p,xi=>Math.log1p(xi)),log1pConfig={kernelName:Log1p,backendName:"cpu",kernelFunc:log1p2},logicalAndImpl=createSimpleBinaryKernelImpl((a,b)=>a&&b),logicalAnd2=binaryKernelFunc(LogicalAnd,logicalAndImpl,null,"bool"),logicalAndConfig={kernelName:LogicalAnd,backendName:"cpu",kernelFunc:logicalAnd2},logicalNot2=unaryKernelFunc(LogicalNot,xi=>xi?0:1,"bool"),logicalNotConfig={kernelName:LogicalNot,backendName:"cpu",kernelFunc:logicalNot2},logicalOrImpl=createSimpleBinaryKernelImpl((a,b)=>a||b),logicalOr2=binaryKernelFunc(LogicalOr,logicalOrImpl,null,"bool"),logicalOrConfig={kernelName:LogicalOr,backendName:"cpu",kernelFunc:logicalOr2};function lRN(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(x,"LRN");let channels=x.shape[3],maxD=channels-1,xValues=backend2.data.get(x.dataId).values,size=util_exports.sizeFromShape(x.shape),result=new Float32Array(size);function sumAcrossChannels(offset){let currentChannel=offset%channels,beginSumOffset=offset-currentChannel+Math.max(0,currentChannel-depthRadius),endSumOffset=offset-currentChannel+Math.min(currentChannel+depthRadius,maxD),sum6=0;for(;beginSumOffset<=endSumOffset;beginSumOffset++){let z=xValues[beginSumOffset];sum6+=z*z}return sum6}for(let offset=0;offset<size;offset++){let sum6=sumAcrossChannels(offset),val=xValues[offset]*Math.pow(bias+alpha*sum6,-beta);result[offset]=val}return backend2.makeTensorInfo(x.shape,x.dtype,result)}var lRNConfig={kernelName:LRN,backendName:"cpu",kernelFunc:lRN};function lRNGrad(args){let{inputs,backend:backend2,attrs}=args,{x,y,dy}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(dy,"LRNGrad");let dySize=util_exports.sizeFromShape(dy.shape),channels=dy.shape[3],dyValues=backend2.data.get(dy.dataId).values,xValues=backend2.data.get(x.dataId).values,yValues=backend2.data.get(y.dataId).values,result=new Float32Array(dySize),size=dySize;for(let offset=0;offset<size;offset++){let currentChannel=offset%channels,depthBegin=offset-currentChannel+Math.max(0,currentChannel-depthRadius),depthEnd=offset-currentChannel+Math.min(channels,currentChannel+depthRadius+1),norm2=0;for(let k=depthBegin;k<depthEnd;k++)norm2+=Math.pow(xValues[k],2);norm2=alpha*norm2+bias;for(let k=depthBegin;k<depthEnd;k++){let dyi=-2*alpha*beta*xValues[k]*yValues[offset]/norm2;offset===k&&(dyi+=Math.pow(norm2,-beta)),dyi*=dyValues[offset],result[k]+=dyi}}return backend2.makeTensorInfo(dy.shape,x.dtype,result)}var lRNGradConfig={kernelName:LRNGrad,backendName:"cpu",kernelFunc:lRNGrad};function max3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reductionIndices,keepDims}=attrs,cpuBackend=backend2,xShape=x.shape,xRank=xShape.length,origAxes=util_exports.parseAxisParam(reductionIndices,xShape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xVals=cpuBackend.data.get(x.dataId).values;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];xVals=transposeImpl(xVals,xShape,x.dtype,permutedAxes,newShape),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xShape=newShape}assertNotComplex(x,"max"),backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),result=maxImpl(xVals,reduceSize,maxOutShape,x.dtype),dataId=cpuBackend.write(result,maxOutShape,x.dtype),outShape=maxOutShape;return keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes)),{dataId,shape:outShape,dtype:x.dtype}}var maxConfig={kernelName:Max,backendName:"cpu",kernelFunc:max3};function maxPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"maxPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"max");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var maxPoolConfig={kernelName:MaxPool,backendName:"cpu",kernelFunc:maxPool2};function maxPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"maxPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"max");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var maxPool3DConfig={kernelName:MaxPool3D,backendName:"cpu",kernelFunc:maxPool3D};function maxPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"maxPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),inputBuf=backend2.bufferSync(input2),maxPosBuf=maxPool3dPositions(inputBuf,convInfo),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(!(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)){let maxPos=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(batch,dyDepth,dyRow,dyCol,channel),curPos=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterWidth+wCol,mask=maxPos===curPos?1:0;mask!==0&&(dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel)*mask)}}}}dx.set(dotProd,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPool3DGradConfig2={kernelName:MaxPool3DGrad,backendName:"cpu",kernelFunc:maxPool3DGrad};function maxPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex([input2,output],"maxPoolGrad");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),xValues=backend2.data.get(x.dataId).values,maxPosBuf=buffer(convInfo.outShape,x.dtype,maxPoolPositions(xValues,x.shape,x.dtype,convInfo).values),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(!(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)){let maxPos=effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(b,dyR,dyC,d),curPos=wR*effectiveFilterWidth+wC,mask=maxPos===curPos?1:0;mask!==0&&(dotProd+=dyBuf.get(b,dyR,dyC,d)*mask)}}}dx.set(dotProd,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPoolGradConfig2={kernelName:MaxPoolGrad,backendName:"cpu",kernelFunc:maxPoolGrad2};function maxPoolWithArgmaxImpl(xValues,xShape,dtype,includeBatchInIndex,convInfo){let strides=util_exports.computeStrides(xShape),maxPools=pool2(xValues,xShape,dtype,strides,convInfo,"max"),maxPositions=maxPoolPositions(xValues,xShape,dtype,convInfo,!0,includeBatchInIndex);return[maxPools.values,maxPositions.values]}var maxPoolWithArgmaxConfig={kernelName:MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{x}=inputs,{filterSize,strides,pad:pad3,includeBatchInIndex}=attrs,cpuBackend=backend2;assertNotComplex(x,"MaxPoolWithArgmax");let values=cpuBackend.data.get(x.dataId).values,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,[1,1],pad3),[pooled,indexes]=maxPoolWithArgmaxImpl(values,x.shape,x.dtype,includeBatchInIndex,convInfo),pooledDataId=cpuBackend.write(pooled,convInfo.outShape,x.dtype),indexesDataId=cpuBackend.write(indexes,convInfo.outShape,x.dtype);return[{dataId:pooledDataId,shape:convInfo.outShape,dtype:x.dtype},{dataId:indexesDataId,shape:convInfo.outShape,dtype:"int32"}]}};function sum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"sum");let $x;x.dtype==="bool"?$x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"int32"}}):$x=identity2({inputs:{x},backend:backend2});let xRank=$x.shape.length,axes=util_exports.parseAxisParam(axis,$x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose2({inputs:{x:$x},backend:backend2,attrs:{perm:permutation}}),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank)),backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,permutedX.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(permutedX.shape,reductionAxes),resultDtype=backend_util_exports.upcastType(permutedX.dtype,"int32"),result=zeros3(backend2,outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=backend2.data.get(result.dataId).values,aVals=backend2.data.get(permutedX.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,sum6=0;for(let j=0;j<reduceSize;++j)sum6+=aVals[offset+j];vals[i]=sum6}if(keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(result.shape,axes),oldResult=result;result=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:newShape}}),backend2.disposeIntermediateTensorInfo(oldResult)}return backend2.disposeIntermediateTensorInfo($x),permutation!=null&&backend2.disposeIntermediateTensorInfo(permutedX),result}var sumConfig={kernelName:Sum,backendName:"cpu",kernelFunc:sum3};function mean2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs,axes=util_exports.parseAxisParam(axis,x.shape),reduceShape=backend_util_exports.computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=util_exports.sizeFromShape(reduceShape),toDispose=[],reduceSizeScalar=backend2.makeTensorInfo([],"float32",new Float32Array([reduceSize]));toDispose.push(reduceSizeScalar);let $x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}});toDispose.push($x);let res=div2({inputs:{a:$x,b:reduceSizeScalar},backend:backend2});toDispose.push(res);let result=sum3({inputs:{x:res},backend:backend2,attrs:{axis,keepDims}});return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var meanConfig={kernelName:Mean,backendName:"cpu",kernelFunc:mean2};function min3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"min");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("min",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value)}vals[i]=min6}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var minConfig={kernelName:Min,backendName:"cpu",kernelFunc:min3};function mirrorPad2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,mode}=attrs;assertNotComplex(x,"mirrorPad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),end=paddings.map((p2,i)=>p2[0]+x.shape[i]),offset=mode==="reflect"?0:1,xVals=backend2.data.get(x.dataId).values,xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);for(let i=0;i<resultSize;i++){let coords2=util_exports.indexToLoc(i,resultRank,resultStrides);for(let i2=0;i2<resultRank;i2++)coords2[i2]<start[i2]?coords2[i2]=start[i2]*2-coords2[i2]-offset:coords2[i2]>=end[i2]&&(coords2[i2]=(end[i2]-1)*2-coords2[i2]+offset);coords2=coords2.map((c,i2)=>c-start[i2]);let inIndex=util_exports.locToIndex(coords2,xRank,xStrides);resVals[i]=xVals[inIndex]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var mirrorPadConfig={kernelName:MirrorPad,backendName:"cpu",kernelFunc:mirrorPad2},modImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>{let rem=aValue%bValue;return aValue<0&&bValue<0||aValue>=0&&bValue>=0?rem:(rem+bValue)%bValue}),mod2=binaryKernelFunc(Mod,modImpl),modConfig={kernelName:Mod,backendName:"cpu",kernelFunc:mod2},seedrandom4=__toModule2(require_seedrandom2());function softmax3(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{dim}=attrs,logitsRank=logits.shape.length,$dim=dim;if($dim===-1&&($dim=logitsRank-1),$dim!==logitsRank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);let axes=util_exports.parseAxisParam([$dim],logits.shape),maxLogit=max3({inputs:{x:logits},backend:backend2,attrs:{reductionIndices:axes,keepDims:!1}}),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),maxLogitReshaped=reshape3({inputs:{x:maxLogit},backend:backend2,attrs:{shape:expandedShape}}),a=sub2({inputs:{a:logits,b:maxLogitReshaped},backend:backend2}),b=exp2({inputs:{x:a},backend:backend2}),sumExp=sum3({inputs:{x:b},backend:backend2,attrs:{axis:axes,keepDims:!1}}),sumReshaped=reshape3({inputs:{x:sumExp},backend:backend2,attrs:{shape:expandedShape}}),result=div2({inputs:{a:b,b:sumReshaped},backend:backend2});return backend2.disposeIntermediateTensorInfo(maxLogit),backend2.disposeIntermediateTensorInfo(maxLogitReshaped),backend2.disposeIntermediateTensorInfo(a),backend2.disposeIntermediateTensorInfo(b),backend2.disposeIntermediateTensorInfo(sumExp),backend2.disposeIntermediateTensorInfo(sumReshaped),result}var softmaxConfig={kernelName:Softmax,backendName:"cpu",kernelFunc:softmax3};function multinomial2(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{numSamples,seed,normalized}=attrs;assertNotComplex(logits,"multinomial");let probabilities=normalized?logits:softmax3({inputs:{logits},backend:backend2,attrs:{dim:-1}}),batchSize=probabilities.shape[0],numEvents=probabilities.shape[1],probVals=backend2.data.get(probabilities.dataId).values,resShape=[batchSize,numSamples],resVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape),"int32");for(let b=0;b<batchSize;++b){let offset=b*numEvents,cdf=new Float32Array(numEvents-1);cdf[0]=probVals[offset];for(let event=1;event<cdf.length;++event)cdf[event]=cdf[event-1]+probVals[offset+event];let random=seedrandom4.alea(seed.toString()),outOffset=b*numSamples;for(let sampleId=0;sampleId<numSamples;++sampleId){let r=random();resVals[outOffset+sampleId]=cdf.length;for(let event=0;event<cdf.length;event++)if(r<cdf[event]){resVals[outOffset+sampleId]=event;break}}}return normalized||backend2.disposeIntermediateTensorInfo(probabilities),backend2.makeTensorInfo(resShape,"int32",resVals)}var multinomialConfig={kernelName:Multinomial,backendName:"cpu",kernelFunc:multinomial2},nonMaxSuppressionV3Impl2=kernel_impls_exports.nonMaxSuppressionV3Impl;function nonMaxSuppressionV3(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold}=attrs;assertNotComplex(boxes,"NonMaxSuppression");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices}=nonMaxSuppressionV3Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices))}var nonMaxSuppressionV3Config={kernelName:NonMaxSuppressionV3,backendName:"cpu",kernelFunc:nonMaxSuppressionV3},nonMaxSuppressionV4Impl2=kernel_impls_exports.nonMaxSuppressionV4Impl;function nonMaxSuppressionV4(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs;assertNotComplex(boxes,"NonMaxSuppressionPadded");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([],"int32",new Int32Array([validOutputs]))]}var nonMaxSuppressionV4Config={kernelName:NonMaxSuppressionV4,backendName:"cpu",kernelFunc:nonMaxSuppressionV4},nonMaxSuppressionV5Impl2=kernel_impls_exports.nonMaxSuppressionV5Impl;function nonMaxSuppressionV5(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs;assertNotComplex(boxes,"NonMaxSuppressionWithScore");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl2(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([selectedScores.length],"float32",new Float32Array(selectedScores))]}var nonMaxSuppressionV5Config={kernelName:NonMaxSuppressionV5,backendName:"cpu",kernelFunc:nonMaxSuppressionV5};function oneHot2(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs;assertNotComplex(indices,"oneHot");let indicesSize=util_exports.sizeFromShape(indices.shape),res=new Float32Array(indicesSize*depth);res.fill(offValue);let indicesVal=backend2.data.get(indices.dataId).values;for(let event=0;event<indicesSize;++event)indicesVal[event]>=0&&indicesVal[event]<depth&&(res[event*depth+indicesVal[event]]=onValue);return backend2.makeTensorInfo([...indices.shape,depth],"int32",res)}var oneHotConfig={kernelName:OneHot,backendName:"cpu",kernelFunc:oneHot2};function zerosLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=zerosLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:0,dtype:x.dtype}})}var zerosLikeConfig={kernelName:ZerosLike,backendName:"cpu",kernelFunc:zerosLike2};function onesLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=onesLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:1,dtype:x.dtype}})}var onesLikeConfig={kernelName:OnesLike,backendName:"cpu",kernelFunc:onesLike2};function pack(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims3({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let intermediateTensorInfos=[],expandedTensors=inputs.map(t=>{let expandedT=expandDims3({inputs:{input:t},backend:backend2,attrs:{dim:axis}});return intermediateTensorInfos.push(expandedT),expandedT}),result=concat2({inputs:expandedTensors,backend:backend2,attrs:{axis}});return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var packConfig={kernelName:Pack,backendName:"cpu",kernelFunc:pack};function padV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,constantValue}=attrs;assertNotComplex(x,"pad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),xVals=backend2.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);constantValue!==0&&resVals.fill(constantValue);for(let i=0;i<xSize;i++){let outCoords=util_exports.indexToLoc(i,xRank,xStrides).map((c,i2)=>c+start[i2]),outIndex=util_exports.locToIndex(outCoords,resultRank,resultStrides);resVals[outIndex]=xVals[i]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var padV2Config={kernelName:PadV2,backendName:"cpu",kernelFunc:padV2},powImpl=createSimpleBinaryKernelImpl((a,b)=>Math.pow(a,b)),pow2=binaryKernelFunc(Pow,powImpl),powConfig={kernelName:Pow,backendName:"cpu",kernelFunc:pow2};function range3(args){let{backend:backend2,attrs}=args,{start,stop,dtype,step:step4}=attrs,values=rangeImpl(start,stop,step4,dtype);return backend2.makeTensorInfo([values.length],dtype,values)}var rangeConfig={kernelName:Range,backendName:"cpu",kernelFunc:range3},reciprocal2=unaryKernelFunc(Reciprocal,xi=>1/xi),reciprocalConfig={kernelName:Reciprocal,backendName:"cpu",kernelFunc:reciprocal2};function resizeBilinear2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeBilinear");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,result=new Float32Array(util_exports.sizeFromShape([batch,newHeight,newWidth,numChannels])),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],outputIdx=0,effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];for(let b=0;b<batch;b++)for(let r=0;r<newHeight;r++){let sourceFracRow;halfPixelCenters?sourceFracRow=effectiveRowSizeRatio*(r+.5)-.5:sourceFracRow=effectiveRowSizeRatio*r;let sourceRowFloor=Math.max(0,Math.floor(sourceFracRow)),rowFrac=sourceFracRow-sourceRowFloor,sourceRowCeil=Math.min(oldHeight-1,Math.ceil(sourceFracRow)),topRowOffset=b*imagesStrides[0]+sourceRowFloor*imagesStrides[1],botRowOffset=b*imagesStrides[0]+sourceRowCeil*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol;halfPixelCenters?sourceFracCol=effectiveColSizeRatio*(c+.5)-.5:sourceFracCol=effectiveColSizeRatio*c;let sourceColFloor=Math.max(0,Math.floor(sourceFracCol)),colFrac=sourceFracCol-sourceColFloor,sourceColCeil=Math.min(oldWidth-1,Math.ceil(sourceFracCol)),topLeftOffest=topRowOffset+sourceColFloor*imagesStrides[2],botLeftOffset=botRowOffset+sourceColFloor*imagesStrides[2],topRightOffset=topRowOffset+sourceColCeil*imagesStrides[2],botRightOffest=botRowOffset+sourceColCeil*imagesStrides[2];for(let d=0;d<numChannels;d++){let topLeft=xValues[topLeftOffest+d],bottomLeft=xValues[botLeftOffset+d],topRight=xValues[topRightOffset+d],bottomRight=xValues[botRightOffest+d],top=topLeft+(topRight-topLeft)*colFrac,bottom=bottomLeft+(bottomRight-bottomLeft)*colFrac,newValue=top+(bottom-top)*rowFrac;result[outputIdx++]=newValue}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],"float32",result)}var resizeBilinearConfig={kernelName:ResizeBilinear,backendName:"cpu",kernelFunc:resizeBilinear2};function resizeBilinearGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeBilinearGrad");let imagesStrides=util_exports.computeStrides(images.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],dyValues=backend2.data.get(dy.dataId).values,offset=0;for(let b=0;b<batch;b++){let bOffset=b*imagesStrides[0];for(let r=0;r<yHeight;r++){let dxR=r*heightScale,topDxRIndex=Math.floor(dxR),bottomDxRIndex=Math.min(Math.ceil(dxR),xHeight-1),topDxROffset=bOffset+topDxRIndex*imagesStrides[1],bottomDxROffset=bOffset+bottomDxRIndex*imagesStrides[1],dxRLerp=dxR-topDxRIndex,inverseDxRLerp=1-dxRLerp;for(let c=0;c<yWidth;c++){let dxC=c*widthScale,leftDxCIndex=Math.floor(dxC),rightDxCIndex=Math.min(Math.ceil(dxC),xWidth-1),dxCLerp=dxC-leftDxCIndex,inverseDxCLerp=1-dxCLerp,topLeftRCOffset=topDxROffset+leftDxCIndex*imagesStrides[2],topRightRCOffset=topDxROffset+rightDxCIndex*imagesStrides[2],bottomLeftRCOffset=bottomDxROffset+leftDxCIndex*imagesStrides[2],bottomRightRCOffset=bottomDxROffset+rightDxCIndex*imagesStrides[2],inverseDxRLerpTimesInverseDxCLerp=inverseDxRLerp*inverseDxCLerp,inverseDxRLerpTimesDxCLerp=inverseDxRLerp*dxCLerp,dxRLerpTimesInverseDxCLerp=dxRLerp*inverseDxCLerp,dxRLerpTimesDxCLerp=dxRLerp*dxCLerp;for(let d=0;d<depth;d++){let dyVal=dyValues[offset++];output[topLeftRCOffset+d]+=dyVal*inverseDxRLerpTimesInverseDxCLerp,output[topRightRCOffset+d]+=dyVal*inverseDxRLerpTimesDxCLerp,output[bottomLeftRCOffset+d]+=dyVal*dxRLerpTimesInverseDxCLerp,output[bottomRightRCOffset+d]+=dyVal*dxRLerpTimesDxCLerp}}}}return backend2.makeTensorInfo([batch,xWidth,xHeight,depth],"float32",output)}var resizeBilinearGradConfig2={kernelName:ResizeBilinearGrad,backendName:"cpu",kernelFunc:resizeBilinearGrad};function resizeNearestNeighbor2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeNearestNeighbor");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,output=new Float32Array(batch*newHeight*newWidth*numChannels),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1],outputOffset=0;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<newHeight;r++){let sourceFracRow=halfPixelCenters?effectiveRowSizeRatio*(r+.5):effectiveRowSizeRatio*r,sourceNearestRow=Math.min(oldHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));halfPixelCenters&&(sourceNearestRow=Math.max(0,sourceNearestRow));let rowOffset=batchOffset+sourceNearestRow*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=halfPixelCenters?effectiveColSizeRatio*(c+.5):effectiveColSizeRatio*c,sourceNearestCol=Math.min(oldWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));halfPixelCenters&&(sourceNearestCol=Math.max(0,sourceNearestCol));let colOffset=rowOffset+sourceNearestCol*imagesStrides[2];for(let d=0;d<numChannels;d++){let newVal=xValues[colOffset+d];output[outputOffset++]=newVal}}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],images.dtype,output)}var resizeNearestNeighborConfig={kernelName:ResizeNearestNeighbor,backendName:"cpu",kernelFunc:resizeNearestNeighbor2};function resizeNearestNeighborGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeNearestNeighborGrad");let imagesStrides=util_exports.computeStrides(images.shape),dyStrides=util_exports.computeStrides(dy.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),dyValues=backend2.data.get(dy.dataId).values,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<xHeight;r++){let rowOffset=batchOffset+r*imagesStrides[1],startRLerp=Math.floor(r*invHeightScale),startDyR=Math.floor(startRLerp-winHeight/2);for(let c=0;c<xWidth;c++){let colOffset=rowOffset+c*imagesStrides[2],startCLerp=Math.floor(c*invWidthScale),startDyC=Math.floor(startCLerp-winWidth/2);for(let d=0;d<depth;d++){let accum=0;for(let dyRIndex=0;dyRIndex<winHeight;dyRIndex++){let dyR=dyRIndex+startDyR;if(!(dyR<0||dyR>=yHeight)){let dyROffset=batchOffset+dyR*dyStrides[1],sourceFracRow=dyR*heightScale,sourceNearestRow=Math.min(xHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));if(r===sourceNearestRow)for(let dyCIndex=0;dyCIndex<winWidth;dyCIndex++){let dyC=dyCIndex+startDyC;if(!(dyC<0||dyC>=yWidth)){let dyCOffset=dyROffset+dyC*dyStrides[2],sourceFracCol=dyC*widthScale,sourceNearestCol=Math.min(xWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));c===sourceNearestCol&&(accum+=dyValues[dyCOffset+d])}}}}output[colOffset+d]=accum}}}}return backend2.makeTensorInfo(images.shape,images.dtype,output)}var resizeNearestNeighborGradConfig2={kernelName:ResizeNearestNeighborGrad,backendName:"cpu",kernelFunc:resizeNearestNeighborGrad};function reverse2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs;assertNotComplex(x,"reverse");let xRank=x.shape.length,$dims=util_exports.parseAxisParam(dims,x.shape);if(xRank===0)return identity2({inputs:{x},backend:backend2});let outBuf=new TensorBuffer(x.shape,x.dtype),xBuf=backend2.bufferSync(x);for(let i=0;i<outBuf.size;i++){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.slice();$dims.forEach(d=>inLoc[d]=x.shape[d]-1-inLoc[d]),outBuf.set(xBuf.get(...inLoc),...outLoc)}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var reverseConfig={kernelName:Reverse,backendName:"cpu",kernelFunc:reverse2},rotateWithOffsetConfig={kernelName:RotateWithOffset,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fullOpacityValue=255,sinFactor=Math.sin(radians),cosFactor=Math.cos(radians),imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],y=coords2[1],coordX=(x-centerX)*cosFactor-(y-centerY)*sinFactor,coordY=(x-centerX)*sinFactor+(y-centerY)*cosFactor;coordX=Math.round(coordX+centerX),coordY=Math.round(coordY+centerY);let outputValue=fillValue;if(typeof fillValue!="number"&&(channel===3?outputValue=fullOpacityValue:outputValue=fillValue[channel]),coordX>=0&&coordX<imageWidth&&coordY>=0&&coordY<imageHeight){let rotatedRowOffset=coordY*(imageWidth*numChannels),rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rotatedRowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}let outIdx=batchOffset+rowOffset+colOffset+channel;output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}},round3=unaryKernelFunc(Round,xi=>{let base2=Math.floor(xi);return xi-base2<.5?Math.floor(xi):xi-base2>.5?Math.ceil(xi):base2%2===0?base2:base2+1}),roundConfig={kernelName:Round,backendName:"cpu",kernelFunc:round3};function scatterImpl(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices){let flattenShape=[outputSize/sliceSize,sliceSize],indicesData=indices.values,updatesData=updates.values;if(outputSize===0)return buffer(shape,updates.dtype);let outBuf=buffer(flattenShape,updates.dtype);outBuf.values.fill(defaultValue);for(let i=0;i<numUpdates;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];index.push(dim),flattenIndex+=dim*strides[j]}if(flattenIndex<0||flattenIndex>=outputSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${shape}`);for(let k=0;k<sliceSize;k++)sumDupeIndices?outBuf.values[flattenIndex*sliceSize+k]+=updatesData[i*sliceSize+k]:outBuf.values[flattenIndex*sliceSize+k]=updates.rank===0?updatesData[0]:updatesData[i*sliceSize+k]}return outBuf}function scatterNd(args){let{inputs,backend:backend2,attrs}=args,{indices,updates}=inputs,{shape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),sumDupeIndices=!0,indicesBuf=backend2.bufferSync(indices),updatesBuf=backend2.bufferSync(updates),outBuf=scatterImpl(indicesBuf,updatesBuf,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,0,sumDupeIndices);return backend2.makeTensorInfo(shape,outBuf.dtype,outBuf.values)}var scatterNdConfig={kernelName:ScatterNd,backendName:"cpu",kernelFunc:scatterNd};function select(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs;assertNotComplex([condition,t,e],"select");let conditionRank=condition.shape.length,values=backend2.data.get(condition.dataId).values,tValues=backend2.data.get(t.dataId).values,eValues=backend2.data.get(e.dataId).values,resultDtype=upcastType(t.dtype,e.dtype),newValues=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape),resultDtype),index=0,offset=conditionRank===0||conditionRank>1||t.shape.length===1?1:util_exports.sizeFromShape(t.shape.slice(1));for(let i=0;i<values.length;i++)for(let j=0;j<offset;j++)values[i]===1?newValues[index++]=tValues[i]:newValues[index++]=eValues[i];return backend2.makeTensorInfo(t.shape,resultDtype,newValues)}var selectConfig={kernelName:Select,backendName:"cpu",kernelFunc:select},scaleAlpha=backend_util_exports.SELU_SCALEALPHA,scale=backend_util_exports.SELU_SCALE,selu2=unaryKernelFunc(Selu,xi=>xi>=0?scale*xi:scaleAlpha*(Math.exp(xi)-1)),seluConfig={kernelName:Selu,backendName:"cpu",kernelFunc:selu2},sigmoid2=unaryKernelFunc(Sigmoid,xi=>1/(1+Math.exp(-xi))),sigmoidConfig={kernelName:Sigmoid,backendName:"cpu",kernelFunc:sigmoid2},sign2=unaryKernelFunc(Sign,xi=>xi<0?-1:xi>0?1:0),signConfig={kernelName:Sign,backendName:"cpu",kernelFunc:sign2},sin2=unaryKernelFunc(Sin,xi=>Math.sin(xi)),sinConfig={kernelName:Sin,backendName:"cpu",kernelFunc:sin2},sinh2=unaryKernelFunc(Sinh,xi=>Math.sinh(xi)),sinhConfig={kernelName:Sinh,backendName:"cpu",kernelFunc:sinh2},epsilon2=11920928955078125e-23,threshold=Math.log(epsilon2)+2,softplus2=unaryKernelFunc(Softplus,xi=>{let tooLarge=xi>-threshold,tooSmall=xi<threshold,expX=Math.exp(xi),result;return tooSmall?result=expX:tooLarge?result=xi:result=Math.log(1+expX),result}),softplusConfig={kernelName:Softplus,backendName:"cpu",kernelFunc:softplus2};function spaceToBatchND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;assertNotComplex([x],"spaceToBatchND");let prod5=util_exports.sizeFromShape(blockShape),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=padV2Config.kernelFunc({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),paddedXReshaped=reshape3({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose2({inputs:{x:paddedXReshaped},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape3({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return backend2.disposeIntermediateTensorInfo(paddedX),backend2.disposeIntermediateTensorInfo(paddedXReshaped),backend2.disposeIntermediateTensorInfo(paddedXT),result}var spaceToBatchNDConfig={kernelName:SpaceToBatchND,backendName:"cpu",kernelFunc:spaceToBatchND2};function sparseToDense2(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,indicesBuf=backend2.bufferSync(sparseIndices),updatesBuf=backend2.bufferSync(sparseValues),$defaultValue=backend2.data.get(defaultValue.dataId).values[0],outBuf=scatterImpl(indicesBuf,updatesBuf,outputShape,outputSize,sliceSize,numUpdates,sliceRank,strides,$defaultValue,sumDupeIndices);return backend2.makeTensorInfo(outputShape,outBuf.dtype,outBuf.values)}var sparseToDenseConfig={kernelName:SparseToDense,backendName:"cpu",kernelFunc:sparseToDense2};function splitV(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[$axis]=s;let sliceT=slice2({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[$axis]+=s,sliceT})}var splitVConfig={kernelName:SplitV,backendName:"cpu",kernelFunc:splitV},sqrt2=unaryKernelFunc(Sqrt,xi=>Math.sqrt(xi)),sqrtConfig={kernelName:Sqrt,backendName:"cpu",kernelFunc:sqrt2},squareConfig={kernelName:Square,backendName:"cpu",kernelFunc:({inputs,backend:backend2})=>{let{x}=inputs,cpuBackend=backend2;assertNotComplex(x,"square");let values=cpuBackend.data.get(x.dataId).values,newValues=new Float32Array(values.length);for(let i=0;i<values.length;++i){let value=values[i];newValues[i]=value*value}return{dataId:cpuBackend.write(newValues,x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}},step2=unaryKernelFunc(Step,(xi,attrs)=>{let stepAttrs=attrs;return isNaN(xi)?NaN:xi>0?1:stepAttrs.alpha}),stepConfig={kernelName:Step,backendName:"cpu",kernelFunc:step2};function stridedSlice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs;assertNotComplex(x,"stridedSlice");let{nonStrided,$begin,$strides,size,newShape,outShape}=slice_util_exports.sliceInfo(x.shape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask),$x=reshape3({inputs:{x},backend:backend2,attrs:{shape:newShape}}),result;if(nonStrided){let sliced=slice2({inputs:{x:$x},backend:backend2,attrs:{begin:$begin,size}});result=reshape3({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(sliced)}else if(outShape.some(axis=>axis===0))result=backend2.makeTensorInfo(outShape,x.dtype,[]);else{let xBuf=backend2.bufferSync($x),outBuf=stridedSliceImpl(outShape,xBuf,$strides,$begin);result=backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:outShape}});return backend2.disposeIntermediateTensorInfo($x),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var stridedSliceConfig={kernelName:StridedSlice,backendName:"cpu",kernelFunc:stridedSlice2},tan2=unaryKernelFunc(Tan,xi=>Math.tan(xi)),tanConfig={kernelName:Tan,backendName:"cpu",kernelFunc:tan2},tanh3=unaryKernelFunc(Tanh,xi=>Math.tanh(xi)),tanhConfig={kernelName:Tanh,backendName:"cpu",kernelFunc:tanh3};function tile3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reps}=attrs;assertNotComplex(x,"tile");let outBuf=tileImpl(backend2.bufferSync(x),reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var tileConfig={kernelName:Tile,backendName:"cpu",kernelFunc:tile3};function topK(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs;assertNotComplex(x,"topk");let xVals=backend2.data.get(x.dataId).values,[allTopKVals,allTopKIndices]=topKImpl(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig={kernelName:TopK,backendName:"cpu",kernelFunc:topK};function unique3(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex(x,"unique");let values=backend2.data.get(x.dataId).values,{outputValues,outputShape,indices}=uniqueImpl(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig={kernelName:Unique,backendName:"cpu",kernelFunc:unique3};function unpack(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let valueRank=value.shape.length,num=value.shape[axis],outShape=new Array(valueRank-1),outIndex=0;for(let i=0;i<valueRank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let begin=new Array(valueRank).fill(0),size=value.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let tempRes=slice2({inputs:{x:value},backend:backend2,attrs:{begin,size}});res[i]=reshape3({inputs:{x:tempRes},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(tempRes)}return res}var unpackConfig={kernelName:Unpack,backendName:"cpu",kernelFunc:unpack};function unsortedSegmentSum2(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs;assertNotComplex(x,"unsortedSegmentSum");let xRank=x.shape.length,segmentIdsRank=segmentIds.shape.length,res=[],intermediates=[],numIters=xRank-segmentIdsRank,$segmentIds=segmentIds;for(let i=0;i<numIters;++i){let expanded=expandDims3({inputs:{input:$segmentIds},backend:backend2,attrs:{dim:i+1}});$segmentIds=expanded,intermediates.push(expanded)}for(let i=0;i<numSegments;++i){let scalarValue=util_exports.createScalarValue(i,"int32"),segmentId=backend2.makeTensorInfo([],"int32",scalarValue),mask=equal2({inputs:{a:segmentId,b:$segmentIds},backend:backend2}),maskCasted=cast3({inputs:{x:mask},backend:backend2,attrs:{dtype:"float32"}}),mul2=multiply2({inputs:{a:maskCasted,b:x},backend:backend2}),sumTensorInfo=sum3({inputs:{x:mul2},backend:backend2,attrs:{axis:0,keepDims:!1}});res.push(sumTensorInfo),intermediates.push(segmentId),intermediates.push(mask),intermediates.push(maskCasted),intermediates.push(mul2),intermediates.push(sumTensorInfo)}let result=pack({inputs:res,backend:backend2,attrs:{axis:0}});return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig={kernelName:UnsortedSegmentSum,backendName:"cpu",kernelFunc:unsortedSegmentSum2},kernelConfigs=[_fusedMatMulConfig,absConfig,acosConfig,acoshConfig,addConfig,addNConfig,allConfig,anyConfig,argMaxConfig,argMinConfig,asinConfig,asinhConfig,atanConfig,atan2Config,atanhConfig,avgPoolConfig,avgPool3DConfig,avgPool3DGradConfig2,avgPoolGradConfig2,batchMatMulConfig,batchNormConfig,batchToSpaceNDConfig,bincountConfig,castConfig,ceilConfig,clipConfig,complexConfig,complexAbsConfig,concatConfig,conv2DBackpropFilterConfig,conv2DBackpropInputConfig,conv2DConfig,conv3DBackpropFilterV2Config,conv3DBackpropInputV2Config,conv3DConfig,cosConfig,coshConfig,cropAndResizeConfig,cumsumConfig,denseBincountConfig,depthToSpaceConfig,depthwiseConv2dNativeConfig,depthwiseConv2dNativeBackpropFilterConfig,depthwiseConv2dNativeBackpropInputConfig,diagConfig,dilation2dConfig,dilation2dBackpropInputConfig,dilation2dBackpropFilterConfig,realDivConfig,eluConfig,eluGradConfig2,equalConfig,erfConfig,expConfig,expandDimsConfig,expm1Config,fftConfig,fillConfig,flipLeftRightConfig,floorConfig,floorDivConfig,fusedConv2DConfig,fusedDepthwiseConv2DConfig,gatherNdConfig,gatherV2Config,greaterConfig,greaterEqualConfig,identityConfig,ifftConfig,imagConfig,isFiniteConfig,isInfConfig,isNaNConfig,leakyReluConfig,lessConfig,lessEqualConfig,linSpaceConfig,logConfig,log1pConfig,logicalAndConfig,logicalNotConfig,logicalOrConfig,lRNConfig,lRNGradConfig,maximumConfig,maxPoolConfig,maxPool3DConfig,maxPool3DGradConfig2,maxPoolGradConfig2,maxPoolWithArgmaxConfig,maxConfig,meanConfig,minConfig,minimumConfig,mirrorPadConfig,modConfig,multinomialConfig,multiplyConfig,negConfig,nonMaxSuppressionV3Config,nonMaxSuppressionV4Config,nonMaxSuppressionV5Config,notEqualConfig,oneHotConfig,onesLikeConfig,packConfig,padV2Config,powConfig,preluConfig,prodConfig,rangeConfig,realConfig,reciprocalConfig,reluConfig,relu6Config,reshapeConfig,resizeBilinearConfig,resizeBilinearGradConfig2,resizeNearestNeighborConfig,resizeNearestNeighborGradConfig2,reverseConfig,rotateWithOffsetConfig,roundConfig,rsqrtConfig,scatterNdConfig,selectConfig,seluConfig,sigmoidConfig,signConfig,sinConfig,sinhConfig,sliceConfig,softmaxConfig,softplusConfig,spaceToBatchNDConfig,sparseToDenseConfig,splitVConfig,sqrtConfig,squareConfig,squaredDifferenceConfig,stepConfig,stridedSliceConfig,subConfig,sumConfig,tanConfig,tanhConfig,tileConfig,topKConfig,transposeConfig,uniqueConfig,unpackConfig,unsortedSegmentSumConfig,zerosLikeConfig];for(let kernelConfig of kernelConfigs)registerKernel(kernelConfig);var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(webGLVersion,gl){contexts[webGLVersion]=gl}function getWebGLContext(webGLVersion){if(!(webGLVersion in contexts)){let newCtx=getWebGLRenderingContext(webGLVersion);if(newCtx!==null)contexts[webGLVersion]=newCtx;else return console.log("Could not get context for WebGL version",webGLVersion),null}let gl=contexts[webGLVersion];return gl.isContextLost()?(delete contexts[webGLVersion],getWebGLContext(webGLVersion)):(gl.disable(gl.DEPTH_TEST),gl.disable(gl.STENCIL_TEST),gl.disable(gl.BLEND),gl.disable(gl.DITHER),gl.disable(gl.POLYGON_OFFSET_FILL),gl.disable(gl.SAMPLE_COVERAGE),gl.enable(gl.SCISSOR_TEST),gl.enable(gl.CULL_FACE),gl.cullFace(gl.BACK),contexts[webGLVersion])}function createCanvas(webGLVersion){if(typeof OffscreenCanvas!="undefined"&&webGLVersion===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function getWebGLRenderingContext(webGLVersion){if(webGLVersion!==1&&webGLVersion!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let canvas=createCanvas(webGLVersion);return canvas.addEventListener("webglcontextlost",ev=>{ev.preventDefault(),delete contexts[webGLVersion]},!1),webGLVersion===1?canvas.getContext("webgl",WEBGL_ATTRIBUTES)||canvas.getContext("experimental-webgl",WEBGL_ATTRIBUTES):canvas.getContext("webgl2",WEBGL_ATTRIBUTES)}var PackingScheme;(function(PackingScheme2){PackingScheme2[PackingScheme2.DENSE=0]="DENSE",PackingScheme2[PackingScheme2.SHARED_BATCH=1]="SHARED_BATCH"})(PackingScheme||(PackingScheme={}));var TextureUsage;(function(TextureUsage2){TextureUsage2[TextureUsage2.RENDER=0]="RENDER",TextureUsage2[TextureUsage2.UPLOAD=1]="UPLOAD",TextureUsage2[TextureUsage2.PIXELS=2]="PIXELS",TextureUsage2[TextureUsage2.DOWNLOAD=3]="DOWNLOAD"})(TextureUsage||(TextureUsage={}));var PhysicalTextureType;(function(PhysicalTextureType2){PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(PhysicalTextureType||(PhysicalTextureType={}));function getUnpackedMatrixTextureShapeWidthHeight(rows,columns){return[columns,rows]}function getUnpackedArraySizeFromMatrixSize(matrixSize,channelsPerTexture){return matrixSize*channelsPerTexture}function getDenseTexShape(shape){let size=util_exports.sizeFromShape(shape),texelsNeeded=Math.ceil(size/4);return util_exports.sizeToSquarishShape(texelsNeeded)}function getPackedMatrixTextureShapeWidthHeight(rows,columns){return[Math.max(1,Math.ceil(columns/2)),Math.max(1,Math.ceil(rows/2))]}function getPackedRGBAArraySizeFromMatrixShape(rows,columns){let[w,h]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return w*h*4}function getTextureConfig(gl,textureHalfFloatExtension){let glany=gl,internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat;return env().getNumber("WEBGL_VERSION")===2?(internalFormatFloat=glany.R32F,internalFormatHalfFloat=glany.R16F,internalFormatPackedHalfFloat=glany.RGBA16F,internalFormatPackedFloat=glany.RGBA32F,textureFormatFloat=glany.RED,downloadUnpackNumChannels=4,defaultNumChannels=1,textureTypeHalfFloat=glany.HALF_FLOAT,textureTypeFloat=glany.FLOAT):(internalFormatFloat=gl.RGBA,internalFormatHalfFloat=gl.RGBA,internalFormatPackedHalfFloat=gl.RGBA,internalFormatPackedFloat=glany.RGBA,textureFormatFloat=gl.RGBA,downloadUnpackNumChannels=4,defaultNumChannels=4,textureTypeHalfFloat=textureHalfFloatExtension!=null?textureHalfFloatExtension.HALF_FLOAT_OES:null,textureTypeFloat=gl.FLOAT),downloadTextureFormat=gl.RGBA,{internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat}}function callAndCheck(gl,func2){let returnValue=func2();return env().getBool("DEBUG")&&checkWebGLError(gl),returnValue}function checkWebGLError(gl){let error=gl.getError();if(error!==gl.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(gl,error))}var MIN_FLOAT16=596e-10,MAX_FLOAT16=65504;function canBeRepresented(num){return!!(env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||num===0||MIN_FLOAT16<Math.abs(num)&&Math.abs(num)<MAX_FLOAT16)}function getWebGLErrorMessage(gl,status){switch(status){case gl.NO_ERROR:return"NO_ERROR";case gl.INVALID_ENUM:return"INVALID_ENUM";case gl.INVALID_VALUE:return"INVALID_VALUE";case gl.INVALID_OPERATION:return"INVALID_OPERATION";case gl.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case gl.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case gl.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${status}`}}function getExtensionOrThrow(gl,extensionName){return throwIfNull(gl,()=>gl.getExtension(extensionName),'Extension "'+extensionName+'" not supported on this browser.')}function createVertexShader(gl,vertexShaderSource){let vertexShader=throwIfNull(gl,()=>gl.createShader(gl.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(vertexShader,vertexShaderSource)),callAndCheck(gl,()=>gl.compileShader(vertexShader)),gl.getShaderParameter(vertexShader,gl.COMPILE_STATUS)===!1)throw console.log(gl.getShaderInfoLog(vertexShader)),new Error("Failed to compile vertex shader.");return vertexShader}function createFragmentShader(gl,fragmentShaderSource){let fragmentShader=throwIfNull(gl,()=>gl.createShader(gl.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(fragmentShader,fragmentShaderSource)),callAndCheck(gl,()=>gl.compileShader(fragmentShader)),gl.getShaderParameter(fragmentShader,gl.COMPILE_STATUS)===!1)throw logShaderSourceAndInfoLog(fragmentShaderSource,gl.getShaderInfoLog(fragmentShader)),new Error("Failed to compile fragment shader.");return fragmentShader}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(shaderSource,shaderInfoLog){let lineNumberRegexResult=lineNumberRegex.exec(shaderInfoLog);if(lineNumberRegexResult==null){console.log(`Couldn't parse line number in error: ${shaderInfoLog}`),console.log(shaderSource);return}let lineNumber=+lineNumberRegexResult[1],shaderLines=shaderSource.split(`
+============================`));let dataId={};return this.data.set(dataId,{values,dtype,refCount:1}),dataId}makeTensorInfo(shape,dtype,values){let outId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));outId=this.write(encodedValues,shape,dtype)}else outId=this.write(values,shape,dtype);return{dataId:outId,shape,dtype}}incRef(dataId){let tensorData=this.data.get(dataId);tensorData.refCount++}decRef(dataId){if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--}}move(dataId,values,shape,dtype){this.data.set(dataId,{values,dtype,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{dtype,complexTensorInfos}=this.data.get(dataId);if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);return backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}return this.data.get(dataId).values}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}makeOutput(values,shape,dtype){let dataId=this.write(values,shape,dtype);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}disposeData(dataId){if(this.data.has(dataId)){let{complexTensorInfos}=this.data.get(dataId);complexTensorInfos!=null&&(this.disposeData(complexTensorInfos.real.dataId),this.disposeData(complexTensorInfos.imag.dataId)),this.data.delete(dataId)}}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--,tensorData.refCount<1&&this.disposeData(dataId)}}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(condition){assertNotComplex([condition],"where");let condVals=this.readSync(condition.dataId);return whereImpl2(condition.shape,condVals)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},shared_exports={};__export2(shared_exports,{addImpl:()=>addImpl,bincountImpl:()=>bincountImpl,bincountReduceImpl:()=>bincountReduceImpl,ceilImpl:()=>ceilImpl,concatImpl:()=>concatImpl,expImpl:()=>expImpl,expm1Impl:()=>expm1Impl,floorImpl:()=>floorImpl,gatherV2Impl:()=>gatherV2Impl,greaterImpl:()=>greaterImpl,lessImpl:()=>lessImpl,linSpaceImpl:()=>linSpaceImpl,logImpl:()=>logImpl,maxImpl:()=>maxImpl,maximumImpl:()=>maximumImpl,minimumImpl:()=>minimumImpl,multiplyImpl:()=>multiplyImpl,negImpl:()=>negImpl,notEqualImpl:()=>notEqualImpl,prodImpl:()=>prodImpl,rangeImpl:()=>rangeImpl,rsqrtImpl:()=>rsqrtImpl,simpleAbsImpl:()=>simpleAbsImpl,sliceImpl:()=>sliceImpl,squaredDifferenceImpl:()=>squaredDifferenceImpl,stridedSliceImpl:()=>stridedSliceImpl,subImpl:()=>subImpl,tileImpl:()=>tileImpl,topKImpl:()=>topKImpl,transposeImpl:()=>transposeImpl,uniqueImpl:()=>uniqueImpl});function simpleAbsImpl(vals){let resultValues=new Float32Array(vals.length);for(let i=0;i<vals.length;++i)resultValues[i]=Math.abs(vals[i]);return resultValues}var abs2=args=>{let{x}=args.inputs,cpuBackend=args.backend;assertNotComplex(x,"abs");let resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),values=cpuBackend.data.get(x.dataId).values;return resultValues=simpleAbsImpl(values),cpuBackend.makeOutput(resultValues,x.shape,"float32")},absConfig={kernelName:Abs,backendName:"cpu",kernelFunc:abs2};function createSimpleBinaryKernelImpl(op2){return(aShape,bShape,aVals,bVals,dtype)=>{let newShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultRank=newShape.length,resultStrides=util_exports.computeStrides(newShape),resultSize=util_exports.sizeFromShape(newShape),result=util_exports.getTypedArrayFromDType(dtype,resultSize),aRank=aShape.length,bRank=bShape.length,aStrides=util_exports.computeStrides(aShape),bStrides=util_exports.computeStrides(bShape),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,newShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<result.length;++i)result[i]=op2(aVals[i%aVals.length],bVals[i%bVals.length]);else for(let i=0;i<result.length;++i){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides);result[i]=op2(aVals[aIndex],bVals[bIndex])}return[result,newShape]}}function complex2(args){let{inputs,backend:backend2}=args,{real:real4,imag:imag4}=inputs,realVals=backend2.data.get(real4.dataId).values,imagVals=backend2.data.get(imag4.dataId).values,complexInfo=backend2.makeTensorInfo(real4.shape,"complex64"),complex4=backend2.data.get(complexInfo.dataId);return complex4.complexTensorInfos={real:backend2.makeTensorInfo(real4.shape,"float32",realVals),imag:backend2.makeTensorInfo(imag4.shape,"float32",imagVals)},complexInfo}var complexConfig={kernelName:Complex,backendName:"cpu",kernelFunc:complex2};function zeros3(backend2,shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros3(backend2,shape,"float32"),imag4=zeros3(backend2,shape,"float32");return complex2({inputs:{real:real4,imag:imag4},backend:backend2})}let values=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape),dtype);return backend2.makeTensorInfo(shape,dtype,values)}function identity2(args){let{inputs,backend:backend2}=args,{x}=inputs;return backend2.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig={kernelName:Identity,backendName:"cpu",kernelFunc:identity2};function real2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,real4=backend2.data.get(input2.dataId).complexTensorInfos.real,realVal=backend2.data.get(real4.dataId).values;return backend2.makeTensorInfo(real4.shape,real4.dtype,realVal)}var realConfig={kernelName:Real,backendName:"cpu",kernelFunc:real2};function cast3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity2({inputs:{x},backend:backend2});let zerosTensorInfo=zeros3(backend2,x.shape,x.dtype),floatX=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex2({inputs:{real:floatX,imag:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),result=cast3({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity2({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32"){let values=backend2.data.get(x.dataId).values,resultValues=Int32Array.from(values);return backend2.makeTensorInfo(x.shape,"int32",resultValues)}if(dtype==="bool"){let xVals=backend2.data.get(x.dataId).values,zero=util_exports.toTypedArray([0],x.dtype),[resultData,resultShape]=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0)(x.shape,[],xVals,zero,"bool");return backend2.makeTensorInfo(resultShape,"bool",resultData)}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig={kernelName:Cast,backendName:"cpu",kernelFunc:cast3};function binaryKernelFunc(name,simpleImpl,complexImpl,dtype){return complexImpl==null?({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;assertNotComplex([a,b],name);let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}:({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;if(a.dtype==="complex64"||b.dtype==="complex64"){let $aComplex=cast3({inputs:{x:a},backend:cpuBackend,attrs:{dtype:"complex64"}}),$aComplexVals=cpuBackend.data.get($aComplex.dataId),aReal=$aComplexVals.complexTensorInfos.real,aImag=$aComplexVals.complexTensorInfos.imag,aRealVals=cpuBackend.data.get(aReal.dataId).values,aImagVals=cpuBackend.data.get(aImag.dataId).values,$bComplex=cast3({inputs:{x:b},backend:cpuBackend,attrs:{dtype:"complex64"}}),$bComplexVals=cpuBackend.data.get($bComplex.dataId),bReal=$bComplexVals.complexTensorInfos.real,bImag=$bComplexVals.complexTensorInfos.imag,bRealVals=cpuBackend.data.get(bReal.dataId).values,bImagVals=cpuBackend.data.get(bImag.dataId).values,[resultRealData,resultImagData,resultShape]=complexImpl(a.shape,b.shape,aRealVals,aImagVals,bRealVals,bImagVals),resultReal=cpuBackend.makeTensorInfo(resultShape,"float32",resultRealData),resultImag=cpuBackend.makeTensorInfo(resultShape,"float32",resultImagData),result=complex2({inputs:{real:resultReal,imag:resultImag},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($aComplex),cpuBackend.disposeIntermediateTensorInfo($bComplex),cpuBackend.disposeIntermediateTensorInfo(resultReal),cpuBackend.disposeIntermediateTensorInfo(resultImag),result}else{let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}}}function createComplexBinaryKernelImpl(op2){return(aShape,bShape,aRealVals,aImagVals,bRealVals,bImagVals)=>{let resultShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultSize=util_exports.sizeFromShape(resultShape),resultRank=resultShape.length,resultStrides=util_exports.computeStrides(resultShape),resultRealVals=util_exports.getTypedArrayFromDType("float32",resultSize),resultImagVals=util_exports.getTypedArrayFromDType("float32",resultSize),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,resultShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,resultShape),aVals=backend_util_exports.mergeRealAndImagArrays(aRealVals,aImagVals),bVals=backend_util_exports.mergeRealAndImagArrays(bRealVals,bImagVals),aRank=aShape.length,aStrides=util_exports.computeStrides(aShape),bRank=bShape.length,bStrides=util_exports.computeStrides(bShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resultRealVals.length;i++){let aIdx=i%aVals.length,bIdx=i%bVals.length,result=op2(aVals[aIdx*2],aVals[aIdx*2+1],bVals[bIdx*2],bVals[bIdx*2+1]);resultRealVals[i]=result.real,resultImagVals[i]=result.imag}else for(let i=0;i<resultRealVals.length;i++){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides),opResult=op2(aVals[aIndex*2],aVals[aIndex*2+1],bVals[bIndex*2],bVals[bIndex*2+1]);resultRealVals[i]=opResult.real,resultImagVals[i]=opResult.imag}return[resultRealVals,resultImagVals,resultShape]}}var addImpl=createSimpleBinaryKernelImpl((a,b)=>a+b),addComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal+bReal,imag:aImag+bImag})),add4=binaryKernelFunc(Add,addImpl,addComplexImpl),addConfig={kernelName:Add,backendName:"cpu",kernelFunc:add4};function bincountImpl(xVals,weightsVals,weightsDtype,weightsShape,size){let weightsSize=util_exports.sizeFromShape(weightsShape),outVals=util_exports.makeZerosTypedArray(size,weightsDtype);for(let i=0;i<xVals.length;i++){let value=xVals[i];if(value<0)throw new Error("Input x must be non-negative!");value>=size||(weightsSize>0?outVals[value]+=weightsVals[i]:outVals[value]+=1)}return outVals}function bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput=!1){let numRows=xBuf.shape[0],numCols=xBuf.shape[1],outBuf=buffer([numRows,size],weightsBuf.dtype);for(let i=0;i<numRows;i++)for(let j=0;j<numCols;j++){let value=xBuf.get(i,j);if(value<0)throw new Error("Input x must be non-negative!");value>=size||(binaryOutput?outBuf.set(1,i,value):weightsBuf.size>0?outBuf.set(outBuf.get(i,value)+weightsBuf.get(i,j),i,value):outBuf.set(outBuf.get(i,value)+1,i,value))}return outBuf}function createSimpleUnaryImpl(op2){return(values,dtype,attrs)=>{let newValues=util_exports.getTypedArrayFromDType(dtype,values.length);for(let i=0;i<values.length;++i)newValues[i]=op2(values[i],attrs);return newValues}}function unaryKernelFunc(name,op2,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),$dtype=dtype||x.dtype,newValues=util_exports.getArrayFromDType($dtype,xSize);for(let i=0;i<xSize;++i)newValues[i]=op2(values[i],attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}function unaryKernelFuncFromImpl(name,unaryImpl,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,$dtype=dtype||x.dtype,newValues=unaryImpl(values,$dtype,attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}var ceilImpl=createSimpleUnaryImpl(xi=>Math.ceil(xi)),ceil2=unaryKernelFuncFromImpl(Ceil,ceilImpl),ceilConfig={kernelName:Ceil,backendName:"cpu",kernelFunc:ceil2};function concatImpl(inputs,outShape,dtype,simplyConcat){let outVals=util_exports.getArrayFromDType(dtype,util_exports.sizeFromShape(outShape));if(simplyConcat&&dtype!=="string"){let offset=0;inputs.forEach(input2=>{let size=util_exports.sizeFromShape(input2.shape);outVals.set(input2.vals,offset),offset+=size})}else{let colOffset=0;inputs.forEach(input2=>{let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(input2.vals):input2.vals,tIdx=0;for(let row=0;row<input2.shape[0];++row){let resIdx=row*outShape[1]+colOffset;for(let col=0;col<input2.shape[1];++col)outVals[resIdx+col]=decodedData[tIdx++]}colOffset+=input2.shape[1]})}return outVals}var expImpl=createSimpleUnaryImpl(xi=>Math.exp(xi)),exp2=unaryKernelFuncFromImpl(Exp,expImpl),expConfig={kernelName:Exp,backendName:"cpu",kernelFunc:exp2},expm1Impl=createSimpleUnaryImpl(xi=>Math.expm1(xi)),expm12=unaryKernelFuncFromImpl(Expm1,expm1Impl),expm1Config={kernelName:Expm1,backendName:"cpu",kernelFunc:expm12},floorImpl=createSimpleUnaryImpl(xi=>Math.floor(xi)),floor2=unaryKernelFuncFromImpl(Floor,floorImpl),floorConfig={kernelName:Floor,backendName:"cpu",kernelFunc:floor2};function gatherV2Impl(xBuf,indicesBuf,flattenOutputShape){let outBuf=buffer(flattenOutputShape,xBuf.dtype);for(let i=0;i<outBuf.size;++i){let originalLoc=outBuf.indexToLoc(i).slice(),batchIdx=originalLoc[0],indicesIdx=originalLoc[2],indicesIndex=indicesBuf.locToIndex([batchIdx,indicesIdx]);originalLoc[2]=indicesBuf.values[indicesIndex];let originalIndex=xBuf.locToIndex(originalLoc);outBuf.values[i]=xBuf.values[originalIndex]}return outBuf}var greaterImpl=createSimpleBinaryKernelImpl((a,b)=>a>b?1:0),greater3=binaryKernelFunc(Greater,greaterImpl,null,"bool"),greaterConfig={kernelName:Greater,backendName:"cpu",kernelFunc:greater3},lessImpl=createSimpleBinaryKernelImpl((a,b)=>a<b?1:0),less3=binaryKernelFunc(Less,lessImpl,null,"bool"),lessConfig={kernelName:Less,backendName:"cpu",kernelFunc:less3};function linSpaceImpl(start,stop,num){let step4=(stop-start)/(num-1),values=util_exports.makeZerosTypedArray(num,"float32");values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var logImpl=createSimpleUnaryImpl(xi=>Math.log(xi)),log3=unaryKernelFuncFromImpl(Log,logImpl),logConfig={kernelName:Log,backendName:"cpu",kernelFunc:log3};function maxImpl(aVals,reduceSize,outShape,dtype){let vals=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(outShape));for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value)}vals[i]=max6}return vals}var maximumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.max(aValue,bValue)),maximum3=binaryKernelFunc(Maximum,maximumImpl),maximumConfig={kernelName:Maximum,backendName:"cpu",kernelFunc:maximum3},minimumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.min(aValue,bValue)),minimum3=binaryKernelFunc(Minimum,minimumImpl),minimumConfig={kernelName:Minimum,backendName:"cpu",kernelFunc:minimum3},multiplyImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue*bValue),multiplyComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal*bReal-aImag*bImag,imag:aReal*bImag+aImag*bReal})),multiply2=binaryKernelFunc(Multiply,multiplyImpl,multiplyComplexImpl),multiplyConfig={kernelName:Multiply,backendName:"cpu",kernelFunc:multiply2};function negImpl(xVals,xShape,xDtype){let minusOne=util_exports.createScalarValue(-1,xDtype);return multiplyImpl([],xShape,minusOne,xVals,xDtype)}function neg2(args){let{inputs,backend:backend2}=args,{x}=inputs;assertNotComplex(x,"neg");let xVals=backend2.data.get(x.dataId).values,[res,newShape]=negImpl(xVals,x.shape,x.dtype);return backend2.makeTensorInfo(newShape,x.dtype,res)}var negConfig={kernelName:Neg,backendName:"cpu",kernelFunc:neg2},notEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0),notEqual2=binaryKernelFunc(NotEqual,notEqualImpl,null,"bool"),notEqualConfig={kernelName:NotEqual,backendName:"cpu",kernelFunc:notEqual2};function transposeImpl(xVals,xShape,dtype,perm,newShape){let xRank=xShape.length,xSize=util_exports.sizeFromShape(xShape),xStrides=util_exports.computeStrides(xShape),newStrides=util_exports.computeStrides(newShape),result=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(newShape));for(let i=0;i<xSize;++i){let loc=util_exports.indexToLoc(i,xRank,xStrides),newLoc=new Array(loc.length);for(let i2=0;i2<newLoc.length;i2++)newLoc[i2]=loc[perm[i2]];let newIndex=util_exports.locToIndex(newLoc,xRank,newStrides);result[newIndex]=xVals[i]}return result}function transpose2(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{perm}=attrs;assertNotComplex(x,"transpose");let xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let values=backend2.data.get(x.dataId).values,result=transposeImpl(values,x.shape,x.dtype,perm,newShape);return{dataId:backend2.write(result,newShape,x.dtype),shape:newShape,dtype:x.dtype}}var transposeConfig={kernelName:Transpose,backendName:"cpu",kernelFunc:transpose2};function prodImpl(xShape,xDtype,xVals,reductionAxes){let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,reductionAxes),outDtype=upcastType(xDtype,"int32"),outVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),outDtype),reduceSize=util_exports.sizeFromShape(reduceShape);for(let i=0;i<outVals.length;++i){let offset=i*reduceSize,prod5=1;for(let j=0;j<reduceSize;++j)prod5*=xVals[offset+j];outVals[i]=prod5}return{outVals,outShape,outDtype}}function prod2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"prod");let xRank=x.shape.length,axes=util_exports.parseAxisParam(axis,x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=x,intermediateTensorInfos=[];permutation!=null&&(permutedX=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}),intermediateTensorInfos.push(permutedX),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank));let xVals=backend2.data.get(permutedX.dataId).values,{outVals,outShape,outDtype}=prodImpl(permutedX.shape,permutedX.dtype,xVals,reductionAxes),resultShape=outShape;return keepDims&&(resultShape=backend_util_exports.expandShapeToKeepDim(outShape,axes)),intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(resultShape,outDtype,outVals)}var prodConfig={kernelName:Prod,backendName:"cpu",kernelFunc:prod2};function rangeImpl(start,stop,step4,dtype){let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step4<0,decreasingRangePositiveStep=stop<start&&step4>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return util_exports.makeZerosTypedArray(0,dtype);let numElements=Math.abs(Math.ceil((stop-start)/step4)),values=util_exports.makeZerosTypedArray(numElements,dtype);stop<start&&step4===1&&(step4=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var rsqrtImpl=createSimpleUnaryImpl(xi=>1/Math.sqrt(xi)),rsqrt2=unaryKernelFuncFromImpl(Rsqrt,rsqrtImpl),rsqrtConfig={kernelName:Rsqrt,backendName:"cpu",kernelFunc:rsqrt2};function sliceImpl(vals,begin,size,shape,dtype){let isContinous=slice_util_exports.isSliceContinous(shape,begin,size),length=util_exports.sizeFromShape(size),xStrides=util_exports.computeStrides(shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin,xStrides);return dtype==="string"?vals.slice(flatOffset,flatOffset+length):vals.subarray(flatOffset,flatOffset+length)}let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(vals):vals,inBuf=buffer(shape,dtype,decodedData),outBuf=buffer(size,dtype);for(let i=0;i<outBuf.size;++i){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.map((idx,j)=>idx+begin[j]);outBuf.set(inBuf.get(...inLoc),...outLoc)}return dtype==="string"?backend_util_exports.fromStringArrayToUint8(outBuf.values):outBuf.values}function slice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs;assertNotComplex(x,"slice");let[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);slice_util_exports.assertParamsValid(x,$begin,$size);let vals=backend2.data.get(x.dataId).values,outVals=sliceImpl(vals,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outVals)}var sliceConfig={kernelName:Slice,backendName:"cpu",kernelFunc:slice2},squaredDifferenceImpl=createSimpleBinaryKernelImpl((a,b)=>{let diff=a-b;return diff*diff}),squaredDifference2=binaryKernelFunc(SquaredDifference,squaredDifferenceImpl),squaredDifferenceConfig={kernelName:SquaredDifference,backendName:"cpu",kernelFunc:squaredDifference2};function stridedSliceImpl(outShape,xBuf,strides,begin){let outBuf=buffer(outShape,xBuf.dtype);for(let i=0;i<outBuf.size;i++){let loc=outBuf.indexToLoc(i),newLoc=new Array(loc.length);for(let j=0;j<newLoc.length;j++)newLoc[j]=loc[j]*strides[j]+begin[j];outBuf.set(xBuf.get(...newLoc),...loc)}return outBuf}var subImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue-bValue),subComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal-bReal,imag:aImag-bImag})),sub2=binaryKernelFunc(Sub,subImpl,subComplexImpl),subConfig={kernelName:Sub,backendName:"cpu",kernelFunc:sub2};function tileImpl(xBuf,reps){let newShape=new Array(xBuf.rank);for(let i=0;i<newShape.length;i++)newShape[i]=xBuf.shape[i]*reps[i];let result=buffer(newShape,xBuf.dtype);for(let i=0;i<result.values.length;++i){let newLoc=result.indexToLoc(i),originalLoc=new Array(xBuf.rank);for(let j=0;j<originalLoc.length;j++)originalLoc[j]=newLoc[j]%xBuf.shape[j];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result}function topKImpl(x,xShape,xDtype,k,sorted){let lastDim=xShape[xShape.length-1],[batch,size]=[x.length/lastDim,lastDim],allTopKVals=util_exports.getTypedArrayFromDType(xDtype,batch*k),allTopKIndices=util_exports.getTypedArrayFromDType("int32",batch*k);for(let b=0;b<batch;b++){let offset=b*size,vals=x.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value);let outOffset=b*k,topKVals=allTopKVals.subarray(outOffset,outOffset+k),topKIndices=allTopKIndices.subarray(outOffset,outOffset+k);for(let i=0;i<k;i++)topKVals[i]=valAndInd[i].value,topKIndices[i]=valAndInd[i].index}let outputShape=xShape.slice();return outputShape[outputShape.length-1]=k,[buffer(outputShape,xDtype,allTopKVals),buffer(outputShape,"int32",allTopKIndices)]}function uniqueImpl(values,axis,shape,dtype){let $axis=util_exports.parseAxisParam(axis,shape)[0],newShape=[1,shape[0],1];for(let i=0;i<$axis;i++)newShape[0]*=shape[i];newShape[1]=shape[$axis];for(let i=$axis+1;i<shape.length;i++)newShape[2]*=shape[i];let uniqueElements={},indices=new Int32Array(shape[$axis]),inputBuffer=new TensorBuffer(newShape,dtype,values),uniqueIndices=[],is1DTensor=newShape[0]===1&&newShape[2]===1;for(let i=0;i<shape[$axis];i++){let element;if(is1DTensor)element=values[i].toString();else{let axisValues=[];for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)axisValues.push(inputBuffer.get(m,i,n));element=axisValues.join(",")}if(uniqueElements[element]!==void 0)indices[i]=uniqueElements[element];else{let uniqueIndex=Object.keys(uniqueElements).length;uniqueElements[element]=uniqueIndex,indices[i]=uniqueIndex,uniqueIndices.push(i)}}let outputTmpShape=newShape.slice();outputTmpShape[1]=Object.keys(uniqueElements).length;let outputBuffer=new TensorBuffer(outputTmpShape,dtype);uniqueIndices.forEach((uniqueElementIndex,i)=>{for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)outputBuffer.set(inputBuffer.get(m,uniqueElementIndex,n),m,i,n)});let outputShape=shape.slice();return outputShape[$axis]=outputTmpShape[1],{outputValues:outputBuffer.values,outputShape,indices}}var version5="2.8.3";registerBackend("cpu",()=>new MathBackendCPU,1);var elu4=unaryKernelFunc(Elu,xi=>xi>=0?xi:Math.exp(xi)-1),eluConfig={kernelName:Elu,backendName:"cpu",kernelFunc:elu4};function leakyRelu2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{alpha}=attrs;assertNotComplex([x],"leakyRelu");let xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outVals=util_exports.getTypedArrayFromDType("float32",xSize);for(let i=0;i<xVals.length;i++)outVals[i]=xVals[i]<0?alpha*xVals[i]:xVals[i];return backend2.makeTensorInfo(x.shape,"float32",outVals)}var leakyReluConfig={kernelName:LeakyRelu,backendName:"cpu",kernelFunc:leakyRelu2},preluImpl=createSimpleBinaryKernelImpl((xValue,aValue)=>xValue<0?aValue*xValue:xValue);function prelu3(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs;assertNotComplex([x,alpha],"prelu");let aVals=backend2.data.get(x.dataId).values,bVals=backend2.data.get(alpha.dataId).values,[resultData,resultShape]=preluImpl(x.shape,alpha.shape,aVals,bVals,x.dtype);return backend2.makeTensorInfo(resultShape,x.dtype,resultData)}var preluConfig={kernelName:Prelu,backendName:"cpu",kernelFunc:prelu3},relu2=unaryKernelFunc(Relu,xi=>Math.max(0,xi)),reluConfig={kernelName:Relu,backendName:"cpu",kernelFunc:relu2},relu62=unaryKernelFunc(Relu6,xi=>Math.min(Math.max(0,xi),6)),relu6Config={kernelName:Relu6,backendName:"cpu",kernelFunc:relu62};function applyActivation2(backend2,x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return identity2({inputs:{x},backend:backend2});if(activation2==="relu")return relu2({inputs:{x},backend:backend2});if(activation2==="elu")return elu4({inputs:{x},backend:backend2});if(activation2==="relu6")return relu62({inputs:{x},backend:backend2});if(activation2==="prelu")return prelu3({inputs:{x,alpha:preluActivationWeights},backend:backend2});if(activation2==="leakyrelu")return leakyRelu2({inputs:{x},backend:backend2,attrs:{alpha:leakyreluAlpha}});throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`)}function reshape3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`),backend2.incRef(x.dataId);let xData=backend2.data.get(x.dataId);if(xData.complexTensorInfos!=null){let real4=xData.complexTensorInfos.real,imag4=xData.complexTensorInfos.imag;real4.shape=$shape,imag4.shape=$shape}return{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig={kernelName:Reshape,backendName:"cpu",kernelFunc:reshape3};function batchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;assertNotComplex([a,b],"matMul");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape3({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape3({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),sharedDim=transposeA?a3d.shape[1]:a3d.shape[2],leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),a3dValues=backend2.data.get(a3d.dataId).values,b3dValues=backend2.data.get(b3d.dataId).values,a3dStrides=util_exports.computeStrides(a3d.shape),b3dStrides=util_exports.computeStrides(b3d.shape),[aBatch,aOuterStep,aInnerStep]=transposeA?[a3dStrides[0],1,a3dStrides[1]]:[a3dStrides[0],a3dStrides[1],1],[bInnerStep,bOuterStep,bBatch]=transposeB?[1,b3dStrides[1],b3dStrides[0]]:[b3dStrides[1],1,b3dStrides[0]],size=leftDim*rightDim,result=buffer([batchDim,leftDim,rightDim],a3d.dtype),resVals=result.values,blockSize=backend2.blockSize;for(let bi=0;bi<batchDim;bi++)for(let i0=0;i0<leftDim;i0+=blockSize)for(let j0=0;j0<rightDim;j0+=blockSize)for(let k0=0;k0<sharedDim;k0+=blockSize){let iBlock=Math.min(i0+blockSize,leftDim),jBlock=Math.min(j0+blockSize,rightDim),kBlock=Math.min(k0+blockSize,sharedDim);for(let i=i0;i<iBlock;i++)for(let j=j0;j<jBlock;j++){let sum6=0;for(let k=k0;k<kBlock;k++){let batchOffsetA=Math.min(bi,batchDimA-1)*aBatch,batchOffsetB=Math.min(bi,batchDimB-1)*bBatch,aVal=a3dValues[batchOffsetA+i*aOuterStep+k*aInnerStep],bVal=b3dValues[k*bInnerStep+j*bOuterStep+batchOffsetB];sum6+=aVal*bVal}resVals[bi*size+(i*rightDim+j)]+=sum6}}return backend2.disposeIntermediateTensorInfo(a3d),backend2.disposeIntermediateTensorInfo(b3d),backend2.makeTensorInfo(outShape,result.dtype,result.values)}var batchMatMulConfig={kernelName:BatchMatMul,backendName:"cpu",kernelFunc:batchMatMul};function _fusedMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs,{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,current,addRes,activationRes,intermediates=[];current=batchMatMul({inputs:{a,b},attrs:{transposeA,transposeB},backend:backend2}),bias&&(addRes=add4({inputs:{a:current,b:bias},backend:backend2}),intermediates.push(current),current=addRes),activation2&&(activationRes=applyActivation2(backend2,current,activation2,preluActivationWeights,leakyreluAlpha),intermediates.push(current),current=activationRes);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return current}var _fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"cpu",kernelFunc:_fusedMatMul},acos2=unaryKernelFunc(Acos,xi=>Math.acos(xi)),acosConfig={kernelName:Acos,backendName:"cpu",kernelFunc:acos2},acosh2=unaryKernelFunc(Acosh,xi=>Math.acosh(xi)),acoshConfig={kernelName:Acosh,backendName:"cpu",kernelFunc:acosh2};function addN2(args){let{inputs,backend:backend2}=args,tensors=inputs;assertNotComplex(inputs,"addN");let vals=tensors.map(t=>backend2.data.get(t.dataId).values),outBuf=buffer(tensors[0].shape,tensors[0].dtype),outVals=outBuf.values;for(let i=0;i<tensors.length;i++){let currVals=vals[i];for(let j=0;j<outVals.length;j++)outVals[j]+=currVals[j]}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var addNConfig={kernelName:AddN,backendName:"cpu",kernelFunc:addN2};function all2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"all");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("all",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,all4=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];all4=all4&&value}vals[i]=all4}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var allConfig={kernelName:All,backendName:"cpu",kernelFunc:all2};function any2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"any");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("any",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,anyVal=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];anyVal=anyVal||value}vals[i]=anyVal}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var anyConfig={kernelName:Any,backendName:"cpu",kernelFunc:any2};function argMax2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMax");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMax",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset],maxIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value,maxIndex=j)}vals[i]=maxIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMaxConfig={kernelName:ArgMax,backendName:"cpu",kernelFunc:argMax2};function argMin2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMin");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMin",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset],minIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value,minIndex=j)}vals[i]=minIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMinConfig={kernelName:ArgMin,backendName:"cpu",kernelFunc:argMin2},asin2=unaryKernelFunc(Asin,xi=>Math.asin(xi)),asinConfig={kernelName:Asin,backendName:"cpu",kernelFunc:asin2},asinh2=unaryKernelFunc(Asinh,xi=>Math.asinh(xi)),asinhConfig={kernelName:Asinh,backendName:"cpu",kernelFunc:asinh2},atan3=unaryKernelFunc(Atan,xi=>Math.atan(xi)),atanConfig={kernelName:Atan,backendName:"cpu",kernelFunc:atan3},atan2Impl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.atan2(aValue,bValue)),atan22=binaryKernelFunc(Atan2,atan2Impl),atan2Config={kernelName:Atan2,backendName:"cpu",kernelFunc:atan22},atanh2=unaryKernelFunc(Atanh,xi=>Math.atanh(xi)),atanhConfig={kernelName:Atanh,backendName:"cpu",kernelFunc:atanh2};function pool2(xValues,xShape,dtype,strides,convInfo,poolType){let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3],outputRowStrides=convInfo.outShape[2]*convInfo.outShape[3],outputColStrides=convInfo.outShape[3];for(let b=0;b<convInfo.batchSize;++b){let outputBatchOffset=b*outputBatchStrides,inputBatchOffset=b*strides[0];for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=Math.max(0,xRCorner),xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner),outputRowOffset=outputBatchOffset+yR*outputRowStrides;for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=Math.max(0,xCCorner),xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),minMaxValue=initialValue,avgValue=0,count2=0;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let xROffset=inputBatchOffset+xR*strides[1];for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let xCOffset=xROffset+xC*strides[2],pixel=xValues[xCOffset+d];poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++)}if(isNaN(minMaxValue))break}let outputOffset=outputRowOffset+yC*outputColStrides+d;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}return output}function maxPoolPositions(xValues,xShape,dtype,convInfo,flattenPositions=!1,includeBatchInIndex=!1){let maxPositions=buffer(convInfo.outShape,"int32"),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=buffer(xShape,dtype,xValues);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=xRCorner;for(;xRMin<0;)xRMin+=dilationHeight;let xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner);for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=xCCorner;for(;xCMin<0;)xCMin+=dilationWidth;let xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let wR=xR-xRCorner;for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let wC=xC-xCCorner,pixel=xBuf.get(b,xR,xC,d);pixel>maxValue&&(maxValue=pixel,flattenPositions?maxPosition=includeBatchInIndex?((b*convInfo.inHeight+xR)*convInfo.inWidth+xC)*convInfo.inChannels+d:(xR*convInfo.inWidth+xC)*convInfo.inChannels+d:maxPosition=wR*effectiveFilterWidth+wC)}}maxPositions.set(maxPosition,b,yR,yC,d)}}return maxPositions}function pool3d2(xValues,xShape,dtype,strides,convInfo,poolType){let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputDepthStrides=convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputRowStrides=convInfo.outShape[3]*convInfo.outShape[4],outputColStrides=convInfo.outShape[4];for(let batch=0;batch<convInfo.batchSize;++batch){let outputBatchOffset=batch*outputBatchStrides,inputBatchOffset=batch*strides[0];for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner),outputDepthOffset=outputBatchOffset+yDepth*outputDepthStrides;for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner),outputRowOffset=outputDepthOffset+yRow*outputRowStrides;for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),outputColOffset=outputRowOffset+yCol*outputColStrides,minMaxValue=initialValue,avgValue=0,count2=0;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let xDepthOffset=inputBatchOffset+xDepth*strides[1];for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let xRowOffset=xDepthOffset+xRow*strides[2];for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let xColOffset=xRowOffset+xCol*strides[3],pixel=xValues[xColOffset+channel];if(poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++),isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}let outputOffset=outputColOffset+channel;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}}return output}function maxPool3dPositions(xBuf,convInfo){let maxPositions=buffer(convInfo.outShape,"int32"),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner);for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner);for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let wDepth=xDepth-xDepthCorner;for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let wRow=xRow-xRowCorner;for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let wCol=xCol-xColCorner,pixel=xBuf.get(batch,xDepth,xRow,xCol,channel);pixel>=maxValue&&(maxValue=pixel,maxPosition=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterHeight+wCol)}}}maxPositions.set(maxPosition,batch,yDepth,yRow,yCol,channel)}}}return maxPositions}function avgPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"avgPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"avg");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var avgPoolConfig={kernelName:AvgPool,backendName:"cpu",kernelFunc:avgPool2};function avgPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"avgPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"avg");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var avgPool3DConfig={kernelName:AvgPool3D,backendName:"cpu",kernelFunc:avgPool3D};function avgPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"avgPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),avgMultiplier=1/(filterDepth*filterHeight*filterWidth),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol||(dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel))}}}dx.set(dotProd*avgMultiplier,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPool3DGradConfig2={kernelName:AvgPool3DGrad,backendName:"cpu",kernelFunc:avgPool3DGrad};function avgPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex([dy,input2],"avgPoolGrad");let{filterSize,strides,pad:pad3}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterHeight*filterWidth),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC||(dotProd+=dyBuf.get(b,dyR,dyC,d))}}dx.set(dotProd*avgMultiplier,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPoolGradConfig2={kernelName:AvgPoolGrad,backendName:"cpu",kernelFunc:avgPoolGrad2};function batchNorm2(args){let{inputs,backend:backend2,attrs}=args,{x,scale:scale22,offset,mean:mean4,variance}=inputs;util_exports.assert(mean4.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean4.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale22==null||mean4.shape.length===scale22.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),assertNotComplex([x,mean4,variance,scale22,offset],"batchNorm");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let xVals=backend2.data.get(x.dataId).values,mVals=backend2.data.get(mean4.dataId).values,varVals=backend2.data.get(variance.dataId).values,sVals=scale22?backend2.data.get(scale22.dataId).values:new Float32Array([1]),offVals=offset?backend2.data.get(offset.dataId).values:new Float32Array([0]),outVals=new Float32Array(xVals.length),offValsLength=offVals.length,sValsLength=sVals.length,varValsLength=varVals.length,mValsLength=mVals.length,offi=0,mi=0,si=0,vi=0;for(let i=0;i<xVals.length;++i)outVals[i]=offVals[offi++]+(xVals[i]-mVals[mi++])*sVals[si++]/Math.sqrt(varVals[vi++]+varianceEpsilon),offi>=offValsLength&&(offi=0),mi>=mValsLength&&(mi=0),si>=sValsLength&&(si=0),vi>=varValsLength&&(vi=0);return backend2.makeTensorInfo(x.shape,x.dtype,outVals)}var batchNormConfig={kernelName:FusedBatchNorm,backendName:"cpu",kernelFunc:batchNorm2};function batchToSpaceND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;assertNotComplex([x],"batchToSpaceND");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),xReshaped=reshape3({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),xTransposed=transpose2({inputs:{x:xReshaped},backend:backend2,attrs:{perm:permuted}}),xTransposedReshaped=reshape3({inputs:{x:xTransposed},backend:backend2,attrs:{shape:reshapedPermuted}}),result=slice2({inputs:{x:xTransposedReshaped},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return backend2.disposeIntermediateTensorInfo(xReshaped),backend2.disposeIntermediateTensorInfo(xTransposed),backend2.disposeIntermediateTensorInfo(xTransposedReshaped),result}var batchToSpaceNDConfig={kernelName:BatchToSpaceND,backendName:"cpu",kernelFunc:batchToSpaceND2};function bincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig={kernelName:Bincount,backendName:"cpu",kernelFunc:bincount2},clip=unaryKernelFunc(ClipByValue,(xi,attrs)=>{let clipAttrs=attrs;return xi>clipAttrs.clipValueMax?clipAttrs.clipValueMax:xi<clipAttrs.clipValueMin?clipAttrs.clipValueMin:xi}),clipConfig={kernelName:ClipByValue,backendName:"cpu",kernelFunc:clip},complexAbs=args=>{let{x}=args.inputs,cpuBackend=args.backend,resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),complexVals=cpuBackend.data.get(x.dataId),real4=complexVals.complexTensorInfos.real,imag4=complexVals.complexTensorInfos.imag,realVals=cpuBackend.data.get(real4.dataId).values,imagVals=cpuBackend.data.get(imag4.dataId).values;for(let i=0;i<realVals.length;i++){let real5=realVals[i],imag5=imagVals[i];resultValues[i]=Math.hypot(real5,imag5)}return cpuBackend.makeOutput(resultValues,x.shape,"float32")},complexAbsConfig={kernelName:ComplexAbs,backendName:"cpu",kernelFunc:complexAbs};function imag2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,imag4=backend2.data.get(input2.dataId).complexTensorInfos.imag,imagVal=backend2.data.get(imag4.dataId).values;return backend2.makeTensorInfo(imag4.shape,imag4.dtype,imagVal)}var imagConfig={kernelName:Imag,backendName:"cpu",kernelFunc:imag2};function concat2(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity2({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,$axis),$inputs[0].dtype==="complex64"){let reals=$inputs.map(t=>real2({inputs:{input:t},backend:backend2})),imags=$inputs.map(t=>imag2({inputs:{input:t},backend:backend2})),realConcated=concat2({inputs:reals,backend:backend2,attrs:{axis:$axis}}),imagConcated=concat2({inputs:imags,backend:backend2,attrs:{axis:$axis}}),result=complex2({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result}let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice($axis));return reshape3({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.data.get(t.dataId).values,shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),$axis),outInfo=backend2.makeTensorInfo(finalOutShape,inputs[0].dtype,outVals);return inputs2D.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}var concatConfig={kernelName:Concat,backendName:"cpu",kernelFunc:concat2};function conv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"conv2d");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,padLeft=convInfo.padInfo.left,padTop=convInfo.padInfo.top,isChannelsLast=convInfo.dataFormat==="channelsLast",y=new TensorBuffer(convInfo.outShape,x.dtype),xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),xBatchStride=xStrides[0],xRowStride=isChannelsLast?xStrides[1]:xStrides[2],xColStride=isChannelsLast?xStrides[2]:1,xChannelStride=isChannelsLast?1:xStrides[1],yBatchStride=y.strides[0],yRowStride=isChannelsLast?y.strides[1]:y.strides[2],yColStride=isChannelsLast?y.strides[2]:1,yChannelStride=isChannelsLast?1:y.strides[1],xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xBatchStride,yOffset1=b*yBatchStride;for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*yRowStride,xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xRowStride;for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*yColStride,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*xColStride,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1*xChannelStride];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset3+d2*yChannelStride]+=xVal*wVals[wOffset3+d2];wOffset3+=convInfo.outChannels}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,yVals)}var conv2DConfig={kernelName:Conv2D,backendName:"cpu",kernelFunc:conv2D};function conv2DBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"conv2dBackpropFilter");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,isChannelsLast=convInfo.dataFormat==="channelsLast",dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,xVals=backend2.data.get(x.dataId).values,dyVals=backend2.data.get(dy.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d1=0;d1<convInfo.inChannels;++d1)for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;isChannelsLast?dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2):dotProd+=xBuf.get(b,d1,xR,xC)*dyBuf.get(b,d2,yR,yC)}}dW.set(dotProd,wR,wC,d1,d2)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var conv2DBackpropFilterConfig={kernelName:Conv2DBackpropFilter,backendName:"cpu",kernelFunc:conv2DBackpropFilter2};function conv2DBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{inputShape,strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;assertNotComplex([dy,filter],"conv2dBackpropInput");let filterStrides=util_exports.computeStrides(filter.shape),dyStrides=util_exports.computeStrides(dy.shape),$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,dyValues=backend2.data.get(dy.dataId).values,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo;$dataFormat=convInfo.dataFormat;let topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=$dataFormat==="channelsLast",xBatchStride=dx.strides[0],xRowStride=isChannelsLast?dx.strides[1]:dx.strides[2],xColStride=isChannelsLast?dx.strides[2]:1,xChannelStride=isChannelsLast?1:dx.strides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1];for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=yBatchStride*b+yRowStride*yR+yColStride*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+yChannelStride*d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}let dxOffset=xBatchStride*b+xRowStride*xR+xColStride*xC+xChannelStride*d1;dxValues[dxOffset]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv2DBackpropInputConfig={kernelName:Conv2DBackpropInput,backendName:"cpu",kernelFunc:conv2DBackpropInput2};function conv3D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs;assertNotComplex([x,filter],"conv3d");let convInfo=backend_util_exports.computeConv3DInfo(x.shape,filter.shape,strides,dilations,pad3),{filterDepth,filterHeight,filterWidth,dilationDepth,dilationHeight,dilationWidth,padInfo}=convInfo,padFront=padInfo.front,padLeft=padInfo.left,padTop=padInfo.top,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values,xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yF=0;yF<convInfo.outDepth;++yF){let yOffset2=yOffset1+yF*y.strides[1],xFCorner=yF*convInfo.strideDepth-padFront;for(let wF=0;wF<filterDepth;++wF){let xF=xFCorner+wF*dilationDepth;if(xF<0||xF>=convInfo.inDepth)continue;let wOffset1=wF*filterStrides[0],xOffset2=xOffset1+xF*xStrides[1];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset3=yOffset2+yR*y.strides[2],xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset2=wOffset1+wR*filterStrides[1],xOffset3=xOffset2+xR*xStrides[2];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset4=yOffset3+yC*convInfo.outChannels,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset3=wOffset2+wC*filterStrides[2],xOffset4=xOffset3+xC*convInfo.inChannels,wOffset4=wOffset3;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset4+d1];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset4+d2]+=xVal*wVals[wOffset4+d2];wOffset4+=convInfo.outChannels}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var conv3DConfig={kernelName:Conv3D,backendName:"cpu",kernelFunc:conv3D};function conv3DBackpropFilterV2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,filterShape}=attrs;assertNotComplex([x,dy],"conv3dBackpropFilterV2");let xStrides=util_exports.computeStrides(x.shape),dyStrides=util_exports.computeStrides(dy.shape),convInfo=backend_util_exports.computeConv3DInfo(x.shape,filterShape,strides,1,pad3),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dw=new TensorBuffer(convInfo.filterShape,"float32"),dwValues=dw.values,[dwS0,dwS1,dwS2,dwS3]=dw.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,xValues=backend2.data.get(x.dataId).values,[xS0,xS1,xS2,xS3]=xStrides,frontPad=convInfo.padInfo.front,leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top;for(let wF=0;wF<filterDepth;++wF){let yFMin=Math.max(0,Math.ceil((frontPad-wF)/strideDepth)),yFMax=Math.min(convInfo.outDepth,(convInfo.inDepth+frontPad-wF)/strideDepth),wOffset1=wF*dwS0;for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight),wOffset2=wR*dwS1+wOffset1;for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth),wOffset3=wC*dwS2+wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let wOffset4=d1*dwS3+wOffset3;for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xS0,yOffset1=b*dyS0;for(let yF=yFMin;yF<yFMax;++yF){let xOffset2=(wF+yF*strideDepth-frontPad)*xS1+xOffset1,yOffset2=yF*dyS1+yOffset1;for(let yR=yRMin;yR<yRMax;++yR){let xOffset3=(wR+yR*strideHeight-topPad)*xS2+xOffset2,yOffset3=yR*dyS2+yOffset2;for(let yC=yCMin;yC<yCMax;++yC){let xOffset4=(wC+yC*strideWidth-leftPad)*xS3+xOffset3,yOffset4=yC*dyS3+yOffset3;dotProd+=xValues[xOffset4+d1]*dyValues[yOffset4+d2]}}}}dwValues[wOffset4+d2]=dotProd}}}}}return backend2.makeTensorInfo(dw.shape,dw.dtype,dw.values)}var conv3DBackpropFilterV2Config={kernelName:Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:conv3DBackpropFilterV2};function conv3DBackpropInputV2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{pad:pad3,strides,inputShape}=attrs;assertNotComplex([dy],"conv3dBackpropInputV2");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv3DInfo(inputShape,filter.shape,strides,1,pad3),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2,dxS3]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2,fltS3]=filterStrides,{batchSize,filterDepth,filterHeight,filterWidth,inChannels,inDepth,inHeight,inWidth,outChannels,outDepth,outHeight,outWidth,strideDepth,strideHeight,strideWidth}=convInfo,frontPad=filterDepth-1-convInfo.padInfo.front,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xF=0;xF<inDepth;++xF){let xFCorner=xF-frontPad,xFMin=Math.max(0,Math.ceil(xFCorner/strideDepth)),yFMax=Math.min(outDepth,(filterDepth+xFCorner)/strideDepth);for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yF=xFMin;yF<yFMax;++yF){let wF=yF*strideDepth-xFCorner;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yF+dyS2*yR+dyS3*yC,fltOffset=fltS0*(filterDepth-1-wF)+fltS1*(filterHeight-1-wR)+fltS2*(filterWidth-1-wC)+fltS3*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}}dxValues[dxS0*b+dxS1*xF+dxS2*xR+dxS3*xC+d1]=dotProd}}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv3DBackpropInputV2Config={kernelName:Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:conv3DBackpropInputV2},cos2=unaryKernelFunc(Cos,xi=>Math.cos(xi)),cosConfig={kernelName:Cos,backendName:"cpu",kernelFunc:cos2},cosh2=unaryKernelFunc(Cosh,xi=>Math.cosh(xi)),coshConfig={kernelName:Cosh,backendName:"cpu",kernelFunc:cosh2};function cropAndResize2(args){let{inputs,backend:backend2,attrs}=args,{image:image3,boxes,boxInd}=inputs,{cropSize,method,extrapolationValue}=attrs,[batch,imageHeight,imageWidth,numChannels]=image3.shape,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,output=buffer([numBoxes,cropHeight,cropWidth,numChannels],"float32"),boxVals=backend2.data.get(boxes.dataId).values,boxIndVals=backend2.data.get(boxInd.dataId).values,imageVals=backend2.data.get(image3.dataId).values,inStride=util_exports.computeStrides(image3.shape),outStride=util_exports.computeStrides(output.shape);for(let b=0;b<numBoxes;b++){let startInd=b*4,y1=boxVals[startInd],x1=boxVals[startInd+1],y2=boxVals[startInd+2],x2=boxVals[startInd+3],bInd=boxIndVals[b];if(bInd>=batch)continue;let heightScale=cropHeight>1?(y2-y1)*(imageHeight-1)/(cropHeight-1):0,widthScale=cropWidth>1?(x2-x1)*(imageWidth-1)/(cropWidth-1):0;for(let y=0;y<cropHeight;y++){let yInd=cropHeight>1?y1*(imageHeight-1)+y*heightScale:.5*(y1+y2)*(imageHeight-1);if(yInd<0||yInd>imageHeight-1){for(let x=0;x<cropWidth;x++)for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}if(method==="bilinear"){let topInd=Math.floor(yInd),bottomInd=Math.ceil(yInd),yLerp=yInd-topInd;for(let x=0;x<cropWidth;x++){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let leftInd=Math.floor(xInd),rightInd=Math.ceil(xInd),xLerp=xInd-leftInd;for(let c=0;c<numChannels;c++){let ind=c+leftInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0],topLeft=imageVals[ind];ind=c+rightInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];let topRight=imageVals[ind];ind=c+leftInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomLeft=imageVals[ind];ind=c+rightInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomRight=imageVals[ind],top=topLeft+(topRight-topLeft)*xLerp,bottom=bottomLeft+(bottomRight-bottomLeft)*xLerp;ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0],output.values[ind]=top+(bottom-top)*yLerp}}}else for(let x=0;x<cropWidth;++x){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let closestX=Math.round(xInd),closestY=Math.round(yInd);for(let c=0;c<numChannels;c++){let inInd=c+closestX*inStride[2]+closestY*inStride[1]+bInd*inStride[0],outInd=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[outInd]=imageVals[inInd]}}}}return backend2.makeTensorInfo(output.shape,output.dtype,output.values)}var cropAndResizeConfig={kernelName:CropAndResize,backendName:"cpu",kernelFunc:cropAndResize2};function cumsum2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs;assertNotComplex(x,"cumsum");let permutation=backend_util_exports.getAxesPermutation([axis],x.shape.length),$x=x;permutation!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,x.shape.length)[0];if(permutedAxis!==$x.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length-1} but got axis=${permutedAxis}`);let resultDtype=upcastType($x.dtype,"int32"),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape),resultDtype),aVals=backend2.data.get($x.dataId).values,finalDim=$x.shape[$x.shape.length-1],indexAdjuster=reverse5?(i,j)=>i+finalDim-j-1:(i,j)=>i+j;for(let i=0;i<aVals.length;i+=finalDim)for(let j=0;j<finalDim;j++){let idx=indexAdjuster(i,j);if(j===0)vals[idx]=exclusive?0:aVals[idx];else{let prevIdx=indexAdjuster(i,j-1);vals[idx]=exclusive?aVals[prevIdx]+vals[prevIdx]:aVals[idx]+vals[prevIdx]}}let result=backend2.makeTensorInfo($x.shape,resultDtype,vals);if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose2({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo($x),reverseTransposedResult}return result}var cumsumConfig={kernelName:Cumsum,backendName:"cpu",kernelFunc:cumsum2};function denseBincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig={kernelName:DenseBincount,backendName:"cpu",kernelFunc:denseBincount2};function depthToSpace2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(dataFormat==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`),util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=x.shape[1],inputWidth=x.shape[2],inputDepth=x.shape[3],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),xValues=backend2.data.get(x.dataId).values,result=new Float32Array(batchSize*outputHeight*outputWidth*outputDepth),outputIdx=0;for(let b=0;b<batchSize;++b)for(let h=0;h<outputHeight;++h){let inH=Math.floor(h/blockSize),offsetH=h%blockSize;for(let w=0;w<outputWidth;++w){let inW=Math.floor(w/blockSize),offsetW=w%blockSize,offsetD=(offsetH*blockSize+offsetW)*outputDepth;for(let d=0;d<outputDepth;++d){let inputIdx=d+offsetD+inputDepth*(inW+inputWidth*(inH+inputHeight*b));result[outputIdx++]=xValues[inputIdx]}}}return backend2.makeTensorInfo([batchSize,outputHeight,outputWidth,outputDepth],x.dtype,result)}var depthToSpaceConfig={kernelName:DepthToSpace,backendName:"cpu",kernelFunc:depthToSpace2};function depthwiseConv2dNative(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"depthwiseConv2DNative");let xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),{filterHeight,filterWidth,dilationHeight,dilationWidth,padInfo}=convInfo,padLeft=padInfo.left,padTop=padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*y.strides[1],xRCorner=yR*convInfo.strideHeight-padLeft;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xStrides[1];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*y.strides[2],xCCorner=yC*convInfo.strideWidth-padTop;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*convInfo.inChannels,yOffset4=yOffset3,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1];for(let q=0;q<chMul;++q)yVals[yOffset4+q]+=xVal*wVals[wOffset3+q];yOffset4+=chMul,wOffset3+=chMul}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var depthwiseConv2dNativeConfig={kernelName:DepthwiseConv2dNative,backendName:"cpu",kernelFunc:depthwiseConv2dNative};function depthwiseConv2dNativeBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"depthwiseConv2dNativeBackpropFilter");let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,dilations,pad3,dimRoundingMode,!0),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,xVals=backend2.data.get(x.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyVals=backend2.data.get(dy.dataId).values,dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d2=0;d2<convInfo.outChannels;++d2){let d1=Math.trunc(d2/chMul),dm=d2%chMul,dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2)}}dW.set(dotProd,wR,wC,d1,dm)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var depthwiseConv2dNativeBackpropFilterConfig={kernelName:DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropFilter2};function depthwiseConv2dNativeBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,inputShape}=attrs;assertNotComplex([dy,filter],"depthwiseConv2DNativeBackpropInput");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,chMul=outChannels/inChannels;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yR+dyS2*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let dm=0;dm<chMul;++dm){let d2=d1*chMul+dm,pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+dm];dotProd+=pixel*weight}}}dxValues[dxS0*b+dxS1*xR+dxS2*xC+d1]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var depthwiseConv2dNativeBackpropInputConfig={kernelName:DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropInput2};function diag2(args){let{inputs,backend:backend2}=args,{x}=inputs,xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outBuf=buffer([xSize,xSize],x.dtype),vals=outBuf.values;for(let i=0;i<xVals.length;i++)vals[i*xSize+i]=xVals[i];let outShape=[...x.shape,...x.shape];return backend2.makeTensorInfo(outShape,outBuf.dtype,outBuf.values)}var diagConfig={kernelName:Diag,backendName:"cpu",kernelFunc:diag2},dilation2dConfig={kernelName:Dilation2D,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,xVals=cpuBackend.data.get(x.dataId).values,xRank=x.shape.length,filterVals=cpuBackend.data.get(filter.dataId).values,filterRank=filter.shape.length,{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations),outSize=util_exports.sizeFromShape(outShape),outRank=outShape.length,outputVals=util_exports.getArrayFromDType(x.dtype,outSize);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let xIndex=util_exports.locToIndex([b,hIn,wIn,d],xRank,util_exports.computeStrides(x.shape)),filterIndex=util_exports.locToIndex([h,w,d],filterRank,util_exports.computeStrides(filter.shape)),val=xVals[xIndex]+filterVals[filterIndex];val>curVal&&(curVal=val)}}}let outputIndex=util_exports.locToIndex([b,hOut,wOut,d],outRank,util_exports.computeStrides(outShape));outputVals[outputIndex]=curVal}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(outputVals,x.dtype),outShape,x.dtype),shape:outShape,dtype:x.dtype}}},dilation2dBackpropFilterConfig={kernelName:Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(filter.shape,filter.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hMax=0,wMax=0;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hMax=h,wMax=w)}}}gradients[hMax][wMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),filter.shape,filter.dtype),shape:filter.shape,dtype:filter.dtype}}},dilation2dBackpropInputConfig={kernelName:Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(x.shape,x.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hInMax=hBeg<0?0:hBeg,wInMax=wBeg<0?0:wBeg;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hInMax=hIn,wInMax=wIn)}}}gradients[b][hInMax][wInMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}};function eluGrad(args){let{inputs,backend:backend2}=args,{dy,y}=inputs;assertNotComplex([dy,y],"eluGrad");let resultValues=new Float32Array(util_exports.sizeFromShape(y.shape)),values=backend2.data.get(y.dataId).values,dyValues=backend2.data.get(dy.dataId).values;for(let i=0;i<values.length;++i){let v=values[i];v>=1?resultValues[i]=dyValues[i]:resultValues[i]=dyValues[i]*(v+1)}return backend2.makeTensorInfo(y.shape,"float32",resultValues)}var eluGradConfig2={kernelName:EluGrad,backendName:"cpu",kernelFunc:eluGrad},equalImpl=createSimpleBinaryKernelImpl((a,b)=>a===b?1:0),equal2=binaryKernelFunc(Equal,equalImpl,null,"bool"),equalConfig={kernelName:Equal,backendName:"cpu",kernelFunc:equal2},p=backend_util_exports.ERF_P,a1=backend_util_exports.ERF_A1,a2=backend_util_exports.ERF_A2,a3=backend_util_exports.ERF_A3,a4=backend_util_exports.ERF_A4,a5=backend_util_exports.ERF_A5,erf2=unaryKernelFunc(Erf,xi=>{let sign4=Math.sign(xi),v=Math.abs(xi),t=1/(1+p*v);return sign4*(1-((((a5*t+a4)*t+a3)*t+a2)*t+a1)*t*Math.exp(-v*v))}),erfConfig={kernelName:Erf,backendName:"cpu",kernelFunc:erf2};function expandDims3(args){let{inputs,backend:backend2,attrs}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig={kernelName:ExpandDims,backendName:"cpu",kernelFunc:expandDims3},realDivImpl=createSimpleBinaryKernelImpl((a,b)=>a/b),div2=binaryKernelFunc(RealDiv,realDivImpl),realDivConfig={kernelName:RealDiv,backendName:"cpu",kernelFunc:div2};function fftBatch(input2,inverse,cpuBackend){let inputShape=input2.shape,batch=inputShape[0],innerDim=inputShape[1],inputVals=cpuBackend.data.get(input2.dataId),real2D=inputVals.complexTensorInfos.real,imag2D=inputVals.complexTensorInfos.imag,resultShape=[batch,innerDim],resultSize=util_exports.sizeFromShape(resultShape),resultReal=util_exports.getTypedArrayFromDType("float32",resultSize),resultImag=util_exports.getTypedArrayFromDType("float32",resultSize);for(let b=0;b<batch;b++){let r=slice2({inputs:{x:real2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),i=slice2({inputs:{x:imag2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),input3=complex2({inputs:{real:r,imag:i},backend:cpuBackend}),{real:real4,imag:imag4}=fftImpl(input3,inverse,cpuBackend),res=backend_util_exports.mergeRealAndImagArrays(real4,imag4);for(let d=0;d<innerDim;d++){let c=backend_util_exports.getComplexWithIndex(res,d);resultReal[b*innerDim+d]=c.real,resultImag[b*innerDim+d]=c.imag}cpuBackend.disposeIntermediateTensorInfo(r),cpuBackend.disposeIntermediateTensorInfo(i),cpuBackend.disposeIntermediateTensorInfo(input3)}let $realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultReal),$imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultImag),result=complex2({inputs:{real:$realInfo,imag:$imagInfo},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($realInfo),cpuBackend.disposeIntermediateTensorInfo($imagInfo),result}function fftImpl(input2,inverse,cpuBackend){let inputSize=util_exports.sizeFromShape(input2.shape),inputVals=cpuBackend.data.get(input2.dataId),realVals=cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values,imagVals=cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;if(isExponentOf2(inputSize)){let result=fftRadix2(realVals,imagVals,inputSize,inverse,cpuBackend),resultShape=[input2.shape[0],input2.shape[1]];if(inverse){let realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.real),imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.imag),sizeInfo=cpuBackend.makeTensorInfo([],"float32",util_exports.createScalarValue(inputSize,"float32")),sizeInfoCopy=identity2({inputs:{x:sizeInfo},backend:cpuBackend}),divRealInfo=realDivConfig.kernelFunc({inputs:{a:realInfo,b:sizeInfo},backend:cpuBackend}),divImagInfo=realDivConfig.kernelFunc({inputs:{a:imagInfo,b:sizeInfoCopy},backend:cpuBackend}),divRealVals=cpuBackend.data.get(divRealInfo.dataId).values,divImagVals=cpuBackend.data.get(divImagInfo.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(realInfo),cpuBackend.disposeIntermediateTensorInfo(imagInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy),cpuBackend.disposeIntermediateTensorInfo(divRealInfo),cpuBackend.disposeIntermediateTensorInfo(divImagInfo),{real:divRealVals,imag:divImagVals}}return result}else{let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),rawOutput=fourierTransformByMatmul(data,inputSize,inverse);return backend_util_exports.splitRealAndImagArrays(rawOutput)}}function isExponentOf2(size){return(size&size-1)==0}function fftRadix2(realVals,imagVals,size,inverse,cpuBackend){if(size===1)return{real:realVals,imag:imagVals};let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),half=size/2,evenComplex=backend_util_exports.complexWithEvenIndex(data),evenRealVals=evenComplex.real,evenImagVals=evenComplex.imag,evenShape=[evenRealVals.length],evenRealInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenRealVals),evenImagInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenImagVals),evenTensorInfo=complex2({inputs:{real:evenRealInfo,imag:evenImagInfo},backend:cpuBackend}),oddComplex=backend_util_exports.complexWithOddIndex(data),oddRealVals=oddComplex.real,oddImagVals=oddComplex.imag,oddShape=[oddRealVals.length],oddRealInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddRealVals),oddImagInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddImagVals),oddTensorInfo=complex2({inputs:{real:oddRealInfo,imag:oddImagInfo},backend:cpuBackend}),$evenComplex=fftRadix2(evenRealVals,evenImagVals,half,inverse,cpuBackend),$evenRealVals=$evenComplex.real,$evenImagVals=$evenComplex.imag,$evenShape=[$evenRealVals.length],$evenRealInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenRealVals),$evenImagInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenImagVals),$evenTensorInfo=complex2({inputs:{real:$evenRealInfo,imag:$evenImagInfo},backend:cpuBackend}),$oddComplex=fftRadix2(oddRealVals,oddImagVals,half,inverse,cpuBackend),$oddRealVals=$oddComplex.real,$oddImagVals=$oddComplex.imag,$oddShape=[$oddRealVals.length],$oddRealInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddRealVals),$oddImagInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddImagVals),$oddTensorInfo=complex2({inputs:{real:$oddRealInfo,imag:$oddImagInfo},backend:cpuBackend}),e=backend_util_exports.exponents(size,inverse),eShape=[e.real.length],eRealInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.real),eImagInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.imag),complexInfo=complex2({inputs:{real:eRealInfo,imag:eImagInfo},backend:cpuBackend}),exponentInfo=multiply2({inputs:{a:complexInfo,b:$oddTensorInfo},backend:cpuBackend}),addPart=add4({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),subPart=sub2({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),addPartReal=real2({inputs:{input:addPart},backend:cpuBackend}),subPartReal=real2({inputs:{input:subPart},backend:cpuBackend}),addPartImag=imag2({inputs:{input:addPart},backend:cpuBackend}),subPartImag=imag2({inputs:{input:subPart},backend:cpuBackend}),$real=concat2({inputs:[addPartReal,subPartReal],backend:cpuBackend,attrs:{axis:0}}),$imag=concat2({inputs:[addPartImag,subPartImag],backend:cpuBackend,attrs:{axis:0}}),$realVals=cpuBackend.data.get($real.dataId).values,$imagVals=cpuBackend.data.get($imag.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(evenRealInfo),cpuBackend.disposeIntermediateTensorInfo(evenImagInfo),cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo(oddRealInfo),cpuBackend.disposeIntermediateTensorInfo(oddImagInfo),cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo($evenRealInfo),cpuBackend.disposeIntermediateTensorInfo($evenImagInfo),cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo($oddRealInfo),cpuBackend.disposeIntermediateTensorInfo($oddImagInfo),cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo(eRealInfo),cpuBackend.disposeIntermediateTensorInfo(eImagInfo),cpuBackend.disposeIntermediateTensorInfo(complexInfo),cpuBackend.disposeIntermediateTensorInfo(exponentInfo),cpuBackend.disposeIntermediateTensorInfo(addPart),cpuBackend.disposeIntermediateTensorInfo(subPart),cpuBackend.disposeIntermediateTensorInfo(addPartReal),cpuBackend.disposeIntermediateTensorInfo(addPartImag),cpuBackend.disposeIntermediateTensorInfo(subPartReal),cpuBackend.disposeIntermediateTensorInfo(subPartImag),cpuBackend.disposeIntermediateTensorInfo($real),cpuBackend.disposeIntermediateTensorInfo($imag),{real:$realVals,imag:$imagVals}}function fourierTransformByMatmul(data,size,inverse){let ret=new Float32Array(size*2);for(let r=0;r<size;r++){let real4=0,imag4=0;for(let c=0;c<size;c++){let e=backend_util_exports.exponent(r*c,size,inverse),term=backend_util_exports.getComplexWithIndex(data,c);real4+=term.real*e.real-term.imag*e.imag,imag4+=term.real*e.imag+term.imag*e.real}inverse&&(real4/=size,imag4/=size),backend_util_exports.assignToTypedArray(ret,real4,imag4,r)}return ret}function fft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!1,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var fftConfig={kernelName:FFT,backendName:"cpu",kernelFunc:fft2};function fill2(args){let{backend:backend2,attrs}=args,{shape,value,dtype}=attrs,$dtype=dtype||util_exports.inferDtype(value),values=util_exports.getArrayFromDType($dtype,util_exports.sizeFromShape(shape));return fillValues(values,value,$dtype),backend2.makeTensorInfo(shape,$dtype,values)}var fillConfig={kernelName:Fill,backendName:"cpu",kernelFunc:fill2};function fillValues(values,value,dtype){values.fill(value)}var flipLeftRightConfig={kernelName:FlipLeftRight,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let x=[batch,row,col,channel][2],coordX=Math.round(imageWidth-x),outIdx=batchOffset+rowOffset+colOffset+channel,outputValue=imageVals[outIdx];if(coordX>=0&&coordX<imageWidth){let rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}},floorDivImpl=createSimpleBinaryKernelImpl((a,b)=>Math.floor(a/b)),floorDiv2=binaryKernelFunc(FloorDiv,floorDivImpl,null,"int32"),floorDivConfig={kernelName:FloorDiv,backendName:"cpu",kernelFunc:floorDiv2};function fusedConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=conv2D({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let resultOld=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(resultOld)}if(activation2){let resultOld=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(resultOld)}return result}var fusedConv2DConfig={kernelName:FusedConv2D,backendName:"cpu",kernelFunc:fusedConv2D};function fusedDepthwiseConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=depthwiseConv2dNative({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let oldResult=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(oldResult)}if(activation2){let oldResult=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(oldResult)}return result}var fusedDepthwiseConv2DConfig={kernelName:FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:fusedDepthwiseConv2D};function gatherNd(args){let{inputs,backend:backend2}=args,{params,indices}=inputs,paramsSize=util_exports.sizeFromShape(params.shape),indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(params,indices);if(numSlices===0)return backend2.makeTensorInfo(resultShape,params.dtype,[]);let outBuf=buffer([numSlices,sliceSize],params.dtype),indicesData=backend2.data.get(indices.dataId).values,paramsData=backend2.data.get(params.dataId).values;for(let i=0;i<numSlices;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];flattenIndex+=dim*strides[j],index.push(dim)}if(flattenIndex<0||flattenIndex>=paramsSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${params.shape}`);for(let k=0;k<sliceSize;k++)outBuf.values[i*sliceSize+k]=paramsData[flattenIndex*sliceSize+k]}return backend2.makeTensorInfo(resultShape,outBuf.dtype,outBuf.values)}var gatherNdConfig={kernelName:GatherNd,backendName:"cpu",kernelFunc:gatherNd};function gatherV2(args){let{inputs,backend:backend2,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs;assertNotComplex([x,indices],"gatherV2");let $batchDims=batchDims;batchDims==null&&($batchDims=0);let indicesSize=util_exports.sizeFromShape(indices.shape),parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,$batchDims),flattenX=reshape3({inputs:{x},backend:backend2,attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]}}),flattenIndex=reshape3({inputs:{x:indices},backend:backend2,attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]}}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],indicesBuf=backend2.bufferSync(flattenIndex),xBuf=backend2.bufferSync(flattenX),outBuf=gatherV2Impl(xBuf,indicesBuf,flattenOutputShape);return backend2.disposeIntermediateTensorInfo(flattenX),backend2.disposeIntermediateTensorInfo(flattenIndex),backend2.makeTensorInfo(shapeInfo.outputShape,outBuf.dtype,outBuf.values)}var gatherV2Config={kernelName:GatherV2,backendName:"cpu",kernelFunc:gatherV2},greaterEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a>=b?1:0),greaterEqual2=binaryKernelFunc(GreaterEqual,greaterEqualImpl,null,"bool"),greaterEqualConfig={kernelName:GreaterEqual,backendName:"cpu",kernelFunc:greaterEqual2};function ifft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!0,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var ifftConfig={kernelName:IFFT,backendName:"cpu",kernelFunc:ifft2},isFinite3=unaryKernelFunc(IsFinite,xi=>Number.isFinite(xi)?1:0,"bool"),isFiniteConfig={kernelName:IsFinite,backendName:"cpu",kernelFunc:isFinite3},isInf2=unaryKernelFunc(IsInf,xi=>Math.abs(xi)===Infinity?1:0,"bool"),isInfConfig={kernelName:IsInf,backendName:"cpu",kernelFunc:isInf2},isNaN3=unaryKernelFunc(IsNan,xi=>Number.isNaN(xi)?1:0,"bool"),isNaNConfig={kernelName:IsNan,backendName:"cpu",kernelFunc:isNaN3},lessEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a<=b?1:0),lessEqual2=binaryKernelFunc(LessEqual,lessEqualImpl,null,"bool"),lessEqualConfig={kernelName:LessEqual,backendName:"cpu",kernelFunc:lessEqual2};function linSpace(args){let{backend:backend2,attrs}=args,{start,stop,num}=attrs,outVals=linSpaceImpl(start,stop,num);return backend2.makeTensorInfo([outVals.length],"float32",outVals)}var linSpaceConfig={kernelName:LinSpace,backendName:"cpu",kernelFunc:linSpace},log1p2=unaryKernelFunc(Log1p,xi=>Math.log1p(xi)),log1pConfig={kernelName:Log1p,backendName:"cpu",kernelFunc:log1p2},logicalAndImpl=createSimpleBinaryKernelImpl((a,b)=>a&&b),logicalAnd2=binaryKernelFunc(LogicalAnd,logicalAndImpl,null,"bool"),logicalAndConfig={kernelName:LogicalAnd,backendName:"cpu",kernelFunc:logicalAnd2},logicalNot2=unaryKernelFunc(LogicalNot,xi=>xi?0:1,"bool"),logicalNotConfig={kernelName:LogicalNot,backendName:"cpu",kernelFunc:logicalNot2},logicalOrImpl=createSimpleBinaryKernelImpl((a,b)=>a||b),logicalOr2=binaryKernelFunc(LogicalOr,logicalOrImpl,null,"bool"),logicalOrConfig={kernelName:LogicalOr,backendName:"cpu",kernelFunc:logicalOr2};function lRN(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(x,"LRN");let channels=x.shape[3],maxD=channels-1,xValues=backend2.data.get(x.dataId).values,size=util_exports.sizeFromShape(x.shape),result=new Float32Array(size);function sumAcrossChannels(offset){let currentChannel=offset%channels,beginSumOffset=offset-currentChannel+Math.max(0,currentChannel-depthRadius),endSumOffset=offset-currentChannel+Math.min(currentChannel+depthRadius,maxD),sum6=0;for(;beginSumOffset<=endSumOffset;beginSumOffset++){let z=xValues[beginSumOffset];sum6+=z*z}return sum6}for(let offset=0;offset<size;offset++){let sum6=sumAcrossChannels(offset),val=xValues[offset]*Math.pow(bias+alpha*sum6,-beta);result[offset]=val}return backend2.makeTensorInfo(x.shape,x.dtype,result)}var lRNConfig={kernelName:LRN,backendName:"cpu",kernelFunc:lRN};function lRNGrad(args){let{inputs,backend:backend2,attrs}=args,{x,y,dy}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(dy,"LRNGrad");let dySize=util_exports.sizeFromShape(dy.shape),channels=dy.shape[3],dyValues=backend2.data.get(dy.dataId).values,xValues=backend2.data.get(x.dataId).values,yValues=backend2.data.get(y.dataId).values,result=new Float32Array(dySize),size=dySize;for(let offset=0;offset<size;offset++){let currentChannel=offset%channels,depthBegin=offset-currentChannel+Math.max(0,currentChannel-depthRadius),depthEnd=offset-currentChannel+Math.min(channels,currentChannel+depthRadius+1),norm2=0;for(let k=depthBegin;k<depthEnd;k++)norm2+=Math.pow(xValues[k],2);norm2=alpha*norm2+bias;for(let k=depthBegin;k<depthEnd;k++){let dyi=-2*alpha*beta*xValues[k]*yValues[offset]/norm2;offset===k&&(dyi+=Math.pow(norm2,-beta)),dyi*=dyValues[offset],result[k]+=dyi}}return backend2.makeTensorInfo(dy.shape,x.dtype,result)}var lRNGradConfig={kernelName:LRNGrad,backendName:"cpu",kernelFunc:lRNGrad};function max3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reductionIndices,keepDims}=attrs,cpuBackend=backend2,xShape=x.shape,xRank=xShape.length,origAxes=util_exports.parseAxisParam(reductionIndices,xShape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xVals=cpuBackend.data.get(x.dataId).values;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];xVals=transposeImpl(xVals,xShape,x.dtype,permutedAxes,newShape),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xShape=newShape}assertNotComplex(x,"max"),backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),result=maxImpl(xVals,reduceSize,maxOutShape,x.dtype),dataId=cpuBackend.write(result,maxOutShape,x.dtype),outShape=maxOutShape;return keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes)),{dataId,shape:outShape,dtype:x.dtype}}var maxConfig={kernelName:Max,backendName:"cpu",kernelFunc:max3};function maxPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"maxPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"max");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var maxPoolConfig={kernelName:MaxPool,backendName:"cpu",kernelFunc:maxPool2};function maxPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"maxPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"max");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var maxPool3DConfig={kernelName:MaxPool3D,backendName:"cpu",kernelFunc:maxPool3D};function maxPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"maxPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),inputBuf=backend2.bufferSync(input2),maxPosBuf=maxPool3dPositions(inputBuf,convInfo),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;let maxPos=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(batch,dyDepth,dyRow,dyCol,channel),curPos=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterWidth+wCol,mask=maxPos===curPos?1:0;mask!==0&&(dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel)*mask)}}}dx.set(dotProd,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPool3DGradConfig2={kernelName:MaxPool3DGrad,backendName:"cpu",kernelFunc:maxPool3DGrad};function maxPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex([input2,output],"maxPoolGrad");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),xValues=backend2.data.get(x.dataId).values,maxPosBuf=buffer(convInfo.outShape,x.dtype,maxPoolPositions(xValues,x.shape,x.dtype,convInfo).values),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;let maxPos=effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(b,dyR,dyC,d),curPos=wR*effectiveFilterWidth+wC,mask=maxPos===curPos?1:0;mask!==0&&(dotProd+=dyBuf.get(b,dyR,dyC,d)*mask)}}dx.set(dotProd,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPoolGradConfig2={kernelName:MaxPoolGrad,backendName:"cpu",kernelFunc:maxPoolGrad2};function maxPoolWithArgmaxImpl(xValues,xShape,dtype,includeBatchInIndex,convInfo){let strides=util_exports.computeStrides(xShape),maxPools=pool2(xValues,xShape,dtype,strides,convInfo,"max"),maxPositions=maxPoolPositions(xValues,xShape,dtype,convInfo,!0,includeBatchInIndex);return[maxPools.values,maxPositions.values]}var maxPoolWithArgmaxConfig={kernelName:MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{x}=inputs,{filterSize,strides,pad:pad3,includeBatchInIndex}=attrs,cpuBackend=backend2;assertNotComplex(x,"MaxPoolWithArgmax");let values=cpuBackend.data.get(x.dataId).values,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,[1,1],pad3),[pooled,indexes]=maxPoolWithArgmaxImpl(values,x.shape,x.dtype,includeBatchInIndex,convInfo),pooledDataId=cpuBackend.write(pooled,convInfo.outShape,x.dtype),indexesDataId=cpuBackend.write(indexes,convInfo.outShape,x.dtype);return[{dataId:pooledDataId,shape:convInfo.outShape,dtype:x.dtype},{dataId:indexesDataId,shape:convInfo.outShape,dtype:"int32"}]}};function sum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"sum");let $x;x.dtype==="bool"?$x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"int32"}}):$x=identity2({inputs:{x},backend:backend2});let xRank=$x.shape.length,axes=util_exports.parseAxisParam(axis,$x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose2({inputs:{x:$x},backend:backend2,attrs:{perm:permutation}}),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank)),backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,permutedX.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(permutedX.shape,reductionAxes),resultDtype=backend_util_exports.upcastType(permutedX.dtype,"int32"),result=zeros3(backend2,outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=backend2.data.get(result.dataId).values,aVals=backend2.data.get(permutedX.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,sum6=0;for(let j=0;j<reduceSize;++j)sum6+=aVals[offset+j];vals[i]=sum6}if(keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(result.shape,axes),oldResult=result;result=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:newShape}}),backend2.disposeIntermediateTensorInfo(oldResult)}return backend2.disposeIntermediateTensorInfo($x),permutation!=null&&backend2.disposeIntermediateTensorInfo(permutedX),result}var sumConfig={kernelName:Sum,backendName:"cpu",kernelFunc:sum3};function mean2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs,axes=util_exports.parseAxisParam(axis,x.shape),reduceShape=backend_util_exports.computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=util_exports.sizeFromShape(reduceShape),toDispose=[],reduceSizeScalar=backend2.makeTensorInfo([],"float32",new Float32Array([reduceSize]));toDispose.push(reduceSizeScalar);let $x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}});toDispose.push($x);let res=div2({inputs:{a:$x,b:reduceSizeScalar},backend:backend2});toDispose.push(res);let result=sum3({inputs:{x:res},backend:backend2,attrs:{axis,keepDims}});return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var meanConfig={kernelName:Mean,backendName:"cpu",kernelFunc:mean2};function min3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"min");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("min",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value)}vals[i]=min6}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var minConfig={kernelName:Min,backendName:"cpu",kernelFunc:min3};function mirrorPad2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,mode}=attrs;assertNotComplex(x,"mirrorPad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),end=paddings.map((p2,i)=>p2[0]+x.shape[i]),offset=mode==="reflect"?0:1,xVals=backend2.data.get(x.dataId).values,xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);for(let i=0;i<resultSize;i++){let coords2=util_exports.indexToLoc(i,resultRank,resultStrides);for(let i2=0;i2<resultRank;i2++)coords2[i2]<start[i2]?coords2[i2]=start[i2]*2-coords2[i2]-offset:coords2[i2]>=end[i2]&&(coords2[i2]=(end[i2]-1)*2-coords2[i2]+offset);coords2=coords2.map((c,i2)=>c-start[i2]);let inIndex=util_exports.locToIndex(coords2,xRank,xStrides);resVals[i]=xVals[inIndex]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var mirrorPadConfig={kernelName:MirrorPad,backendName:"cpu",kernelFunc:mirrorPad2},modImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>{let rem=aValue%bValue;return aValue<0&&bValue<0||aValue>=0&&bValue>=0?rem:(rem+bValue)%bValue}),mod2=binaryKernelFunc(Mod,modImpl),modConfig={kernelName:Mod,backendName:"cpu",kernelFunc:mod2},seedrandom4=__toModule2(require_seedrandom2());function softmax3(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{dim}=attrs,logitsRank=logits.shape.length,$dim=dim;if($dim===-1&&($dim=logitsRank-1),$dim!==logitsRank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);let axes=util_exports.parseAxisParam([$dim],logits.shape),maxLogit=max3({inputs:{x:logits},backend:backend2,attrs:{reductionIndices:axes,keepDims:!1}}),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),maxLogitReshaped=reshape3({inputs:{x:maxLogit},backend:backend2,attrs:{shape:expandedShape}}),a=sub2({inputs:{a:logits,b:maxLogitReshaped},backend:backend2}),b=exp2({inputs:{x:a},backend:backend2}),sumExp=sum3({inputs:{x:b},backend:backend2,attrs:{axis:axes,keepDims:!1}}),sumReshaped=reshape3({inputs:{x:sumExp},backend:backend2,attrs:{shape:expandedShape}}),result=div2({inputs:{a:b,b:sumReshaped},backend:backend2});return backend2.disposeIntermediateTensorInfo(maxLogit),backend2.disposeIntermediateTensorInfo(maxLogitReshaped),backend2.disposeIntermediateTensorInfo(a),backend2.disposeIntermediateTensorInfo(b),backend2.disposeIntermediateTensorInfo(sumExp),backend2.disposeIntermediateTensorInfo(sumReshaped),result}var softmaxConfig={kernelName:Softmax,backendName:"cpu",kernelFunc:softmax3};function multinomial2(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{numSamples,seed,normalized}=attrs;assertNotComplex(logits,"multinomial");let probabilities=normalized?logits:softmax3({inputs:{logits},backend:backend2,attrs:{dim:-1}}),batchSize=probabilities.shape[0],numEvents=probabilities.shape[1],probVals=backend2.data.get(probabilities.dataId).values,resShape=[batchSize,numSamples],resVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape),"int32");for(let b=0;b<batchSize;++b){let offset=b*numEvents,cdf=new Float32Array(numEvents-1);cdf[0]=probVals[offset];for(let event=1;event<cdf.length;++event)cdf[event]=cdf[event-1]+probVals[offset+event];let random=seedrandom4.alea(seed.toString()),outOffset=b*numSamples;for(let sampleId=0;sampleId<numSamples;++sampleId){let r=random();resVals[outOffset+sampleId]=cdf.length;for(let event=0;event<cdf.length;event++)if(r<cdf[event]){resVals[outOffset+sampleId]=event;break}}}return normalized||backend2.disposeIntermediateTensorInfo(probabilities),backend2.makeTensorInfo(resShape,"int32",resVals)}var multinomialConfig={kernelName:Multinomial,backendName:"cpu",kernelFunc:multinomial2},nonMaxSuppressionV3Impl2=kernel_impls_exports.nonMaxSuppressionV3Impl;function nonMaxSuppressionV3(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold}=attrs;assertNotComplex(boxes,"NonMaxSuppression");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices}=nonMaxSuppressionV3Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices))}var nonMaxSuppressionV3Config={kernelName:NonMaxSuppressionV3,backendName:"cpu",kernelFunc:nonMaxSuppressionV3},nonMaxSuppressionV4Impl2=kernel_impls_exports.nonMaxSuppressionV4Impl;function nonMaxSuppressionV4(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs;assertNotComplex(boxes,"NonMaxSuppressionPadded");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([],"int32",new Int32Array([validOutputs]))]}var nonMaxSuppressionV4Config={kernelName:NonMaxSuppressionV4,backendName:"cpu",kernelFunc:nonMaxSuppressionV4},nonMaxSuppressionV5Impl2=kernel_impls_exports.nonMaxSuppressionV5Impl;function nonMaxSuppressionV5(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs;assertNotComplex(boxes,"NonMaxSuppressionWithScore");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl2(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([selectedScores.length],"float32",new Float32Array(selectedScores))]}var nonMaxSuppressionV5Config={kernelName:NonMaxSuppressionV5,backendName:"cpu",kernelFunc:nonMaxSuppressionV5};function oneHot2(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs;assertNotComplex(indices,"oneHot");let indicesSize=util_exports.sizeFromShape(indices.shape),res=new Float32Array(indicesSize*depth);res.fill(offValue);let indicesVal=backend2.data.get(indices.dataId).values;for(let event=0;event<indicesSize;++event)indicesVal[event]>=0&&indicesVal[event]<depth&&(res[event*depth+indicesVal[event]]=onValue);return backend2.makeTensorInfo([...indices.shape,depth],"int32",res)}var oneHotConfig={kernelName:OneHot,backendName:"cpu",kernelFunc:oneHot2};function zerosLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=zerosLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:0,dtype:x.dtype}})}var zerosLikeConfig={kernelName:ZerosLike,backendName:"cpu",kernelFunc:zerosLike2};function onesLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=onesLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:1,dtype:x.dtype}})}var onesLikeConfig={kernelName:OnesLike,backendName:"cpu",kernelFunc:onesLike2};function pack(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims3({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let intermediateTensorInfos=[],expandedTensors=inputs.map(t=>{let expandedT=expandDims3({inputs:{input:t},backend:backend2,attrs:{dim:axis}});return intermediateTensorInfos.push(expandedT),expandedT}),result=concat2({inputs:expandedTensors,backend:backend2,attrs:{axis}});return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var packConfig={kernelName:Pack,backendName:"cpu",kernelFunc:pack};function padV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,constantValue}=attrs;assertNotComplex(x,"pad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),xVals=backend2.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);constantValue!==0&&resVals.fill(constantValue);for(let i=0;i<xSize;i++){let outCoords=util_exports.indexToLoc(i,xRank,xStrides).map((c,i2)=>c+start[i2]),outIndex=util_exports.locToIndex(outCoords,resultRank,resultStrides);resVals[outIndex]=xVals[i]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var padV2Config={kernelName:PadV2,backendName:"cpu",kernelFunc:padV2},powImpl=createSimpleBinaryKernelImpl((a,b)=>Math.pow(a,b)),pow2=binaryKernelFunc(Pow,powImpl),powConfig={kernelName:Pow,backendName:"cpu",kernelFunc:pow2};function range3(args){let{backend:backend2,attrs}=args,{start,stop,dtype,step:step4}=attrs,values=rangeImpl(start,stop,step4,dtype);return backend2.makeTensorInfo([values.length],dtype,values)}var rangeConfig={kernelName:Range,backendName:"cpu",kernelFunc:range3},reciprocal2=unaryKernelFunc(Reciprocal,xi=>1/xi),reciprocalConfig={kernelName:Reciprocal,backendName:"cpu",kernelFunc:reciprocal2};function resizeBilinear2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeBilinear");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,result=new Float32Array(util_exports.sizeFromShape([batch,newHeight,newWidth,numChannels])),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],outputIdx=0,effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];for(let b=0;b<batch;b++)for(let r=0;r<newHeight;r++){let sourceFracRow;halfPixelCenters?sourceFracRow=effectiveRowSizeRatio*(r+.5)-.5:sourceFracRow=effectiveRowSizeRatio*r;let sourceRowFloor=Math.max(0,Math.floor(sourceFracRow)),rowFrac=sourceFracRow-sourceRowFloor,sourceRowCeil=Math.min(oldHeight-1,Math.ceil(sourceFracRow)),topRowOffset=b*imagesStrides[0]+sourceRowFloor*imagesStrides[1],botRowOffset=b*imagesStrides[0]+sourceRowCeil*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol;halfPixelCenters?sourceFracCol=effectiveColSizeRatio*(c+.5)-.5:sourceFracCol=effectiveColSizeRatio*c;let sourceColFloor=Math.max(0,Math.floor(sourceFracCol)),colFrac=sourceFracCol-sourceColFloor,sourceColCeil=Math.min(oldWidth-1,Math.ceil(sourceFracCol)),topLeftOffest=topRowOffset+sourceColFloor*imagesStrides[2],botLeftOffset=botRowOffset+sourceColFloor*imagesStrides[2],topRightOffset=topRowOffset+sourceColCeil*imagesStrides[2],botRightOffest=botRowOffset+sourceColCeil*imagesStrides[2];for(let d=0;d<numChannels;d++){let topLeft=xValues[topLeftOffest+d],bottomLeft=xValues[botLeftOffset+d],topRight=xValues[topRightOffset+d],bottomRight=xValues[botRightOffest+d],top=topLeft+(topRight-topLeft)*colFrac,bottom=bottomLeft+(bottomRight-bottomLeft)*colFrac,newValue=top+(bottom-top)*rowFrac;result[outputIdx++]=newValue}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],"float32",result)}var resizeBilinearConfig={kernelName:ResizeBilinear,backendName:"cpu",kernelFunc:resizeBilinear2};function resizeBilinearGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeBilinearGrad");let imagesStrides=util_exports.computeStrides(images.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],dyValues=backend2.data.get(dy.dataId).values,offset=0;for(let b=0;b<batch;b++){let bOffset=b*imagesStrides[0];for(let r=0;r<yHeight;r++){let dxR=r*heightScale,topDxRIndex=Math.floor(dxR),bottomDxRIndex=Math.min(Math.ceil(dxR),xHeight-1),topDxROffset=bOffset+topDxRIndex*imagesStrides[1],bottomDxROffset=bOffset+bottomDxRIndex*imagesStrides[1],dxRLerp=dxR-topDxRIndex,inverseDxRLerp=1-dxRLerp;for(let c=0;c<yWidth;c++){let dxC=c*widthScale,leftDxCIndex=Math.floor(dxC),rightDxCIndex=Math.min(Math.ceil(dxC),xWidth-1),dxCLerp=dxC-leftDxCIndex,inverseDxCLerp=1-dxCLerp,topLeftRCOffset=topDxROffset+leftDxCIndex*imagesStrides[2],topRightRCOffset=topDxROffset+rightDxCIndex*imagesStrides[2],bottomLeftRCOffset=bottomDxROffset+leftDxCIndex*imagesStrides[2],bottomRightRCOffset=bottomDxROffset+rightDxCIndex*imagesStrides[2],inverseDxRLerpTimesInverseDxCLerp=inverseDxRLerp*inverseDxCLerp,inverseDxRLerpTimesDxCLerp=inverseDxRLerp*dxCLerp,dxRLerpTimesInverseDxCLerp=dxRLerp*inverseDxCLerp,dxRLerpTimesDxCLerp=dxRLerp*dxCLerp;for(let d=0;d<depth;d++){let dyVal=dyValues[offset++];output[topLeftRCOffset+d]+=dyVal*inverseDxRLerpTimesInverseDxCLerp,output[topRightRCOffset+d]+=dyVal*inverseDxRLerpTimesDxCLerp,output[bottomLeftRCOffset+d]+=dyVal*dxRLerpTimesInverseDxCLerp,output[bottomRightRCOffset+d]+=dyVal*dxRLerpTimesDxCLerp}}}}return backend2.makeTensorInfo([batch,xWidth,xHeight,depth],"float32",output)}var resizeBilinearGradConfig2={kernelName:ResizeBilinearGrad,backendName:"cpu",kernelFunc:resizeBilinearGrad};function resizeNearestNeighbor2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeNearestNeighbor");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,output=new Float32Array(batch*newHeight*newWidth*numChannels),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1],outputOffset=0;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<newHeight;r++){let sourceFracRow=halfPixelCenters?effectiveRowSizeRatio*(r+.5):effectiveRowSizeRatio*r,sourceNearestRow=Math.min(oldHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));halfPixelCenters&&(sourceNearestRow=Math.max(0,sourceNearestRow));let rowOffset=batchOffset+sourceNearestRow*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=halfPixelCenters?effectiveColSizeRatio*(c+.5):effectiveColSizeRatio*c,sourceNearestCol=Math.min(oldWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));halfPixelCenters&&(sourceNearestCol=Math.max(0,sourceNearestCol));let colOffset=rowOffset+sourceNearestCol*imagesStrides[2];for(let d=0;d<numChannels;d++){let newVal=xValues[colOffset+d];output[outputOffset++]=newVal}}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],images.dtype,output)}var resizeNearestNeighborConfig={kernelName:ResizeNearestNeighbor,backendName:"cpu",kernelFunc:resizeNearestNeighbor2};function resizeNearestNeighborGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeNearestNeighborGrad");let imagesStrides=util_exports.computeStrides(images.shape),dyStrides=util_exports.computeStrides(dy.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),dyValues=backend2.data.get(dy.dataId).values,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<xHeight;r++){let rowOffset=batchOffset+r*imagesStrides[1],startRLerp=Math.floor(r*invHeightScale),startDyR=Math.floor(startRLerp-winHeight/2);for(let c=0;c<xWidth;c++){let colOffset=rowOffset+c*imagesStrides[2],startCLerp=Math.floor(c*invWidthScale),startDyC=Math.floor(startCLerp-winWidth/2);for(let d=0;d<depth;d++){let accum=0;for(let dyRIndex=0;dyRIndex<winHeight;dyRIndex++){let dyR=dyRIndex+startDyR;if(dyR<0||dyR>=yHeight)continue;let dyROffset=batchOffset+dyR*dyStrides[1],sourceFracRow=dyR*heightScale,sourceNearestRow=Math.min(xHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));if(r===sourceNearestRow)for(let dyCIndex=0;dyCIndex<winWidth;dyCIndex++){let dyC=dyCIndex+startDyC;if(dyC<0||dyC>=yWidth)continue;let dyCOffset=dyROffset+dyC*dyStrides[2],sourceFracCol=dyC*widthScale,sourceNearestCol=Math.min(xWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));c===sourceNearestCol&&(accum+=dyValues[dyCOffset+d])}}output[colOffset+d]=accum}}}}return backend2.makeTensorInfo(images.shape,images.dtype,output)}var resizeNearestNeighborGradConfig2={kernelName:ResizeNearestNeighborGrad,backendName:"cpu",kernelFunc:resizeNearestNeighborGrad};function reverse2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs;assertNotComplex(x,"reverse");let xRank=x.shape.length,$dims=util_exports.parseAxisParam(dims,x.shape);if(xRank===0)return identity2({inputs:{x},backend:backend2});let outBuf=new TensorBuffer(x.shape,x.dtype),xBuf=backend2.bufferSync(x);for(let i=0;i<outBuf.size;i++){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.slice();$dims.forEach(d=>inLoc[d]=x.shape[d]-1-inLoc[d]),outBuf.set(xBuf.get(...inLoc),...outLoc)}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var reverseConfig={kernelName:Reverse,backendName:"cpu",kernelFunc:reverse2},rotateWithOffsetConfig={kernelName:RotateWithOffset,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fullOpacityValue=255,sinFactor=Math.sin(radians),cosFactor=Math.cos(radians),imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],y=coords2[1],coordX=(x-centerX)*cosFactor-(y-centerY)*sinFactor,coordY=(x-centerX)*sinFactor+(y-centerY)*cosFactor;coordX=Math.round(coordX+centerX),coordY=Math.round(coordY+centerY);let outputValue=fillValue;if(typeof fillValue!="number"&&(channel===3?outputValue=fullOpacityValue:outputValue=fillValue[channel]),coordX>=0&&coordX<imageWidth&&coordY>=0&&coordY<imageHeight){let rotatedRowOffset=coordY*(imageWidth*numChannels),rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rotatedRowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}let outIdx=batchOffset+rowOffset+colOffset+channel;output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}},round3=unaryKernelFunc(Round,xi=>{let base2=Math.floor(xi);return xi-base2<.5?Math.floor(xi):xi-base2>.5?Math.ceil(xi):base2%2==0?base2:base2+1}),roundConfig={kernelName:Round,backendName:"cpu",kernelFunc:round3};function scatterImpl(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices){let flattenShape=[outputSize/sliceSize,sliceSize],indicesData=indices.values,updatesData=updates.values;if(outputSize===0)return buffer(shape,updates.dtype);let outBuf=buffer(flattenShape,updates.dtype);outBuf.values.fill(defaultValue);for(let i=0;i<numUpdates;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];index.push(dim),flattenIndex+=dim*strides[j]}if(flattenIndex<0||flattenIndex>=outputSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${shape}`);for(let k=0;k<sliceSize;k++)sumDupeIndices?outBuf.values[flattenIndex*sliceSize+k]+=updatesData[i*sliceSize+k]:outBuf.values[flattenIndex*sliceSize+k]=updates.rank===0?updatesData[0]:updatesData[i*sliceSize+k]}return outBuf}function scatterNd(args){let{inputs,backend:backend2,attrs}=args,{indices,updates}=inputs,{shape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),sumDupeIndices=!0,indicesBuf=backend2.bufferSync(indices),updatesBuf=backend2.bufferSync(updates),outBuf=scatterImpl(indicesBuf,updatesBuf,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,0,sumDupeIndices);return backend2.makeTensorInfo(shape,outBuf.dtype,outBuf.values)}var scatterNdConfig={kernelName:ScatterNd,backendName:"cpu",kernelFunc:scatterNd};function select(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs;assertNotComplex([condition,t,e],"select");let conditionRank=condition.shape.length,values=backend2.data.get(condition.dataId).values,tValues=backend2.data.get(t.dataId).values,eValues=backend2.data.get(e.dataId).values,resultDtype=upcastType(t.dtype,e.dtype),newValues=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape),resultDtype),index=0,offset=conditionRank===0||conditionRank>1||t.shape.length===1?1:util_exports.sizeFromShape(t.shape.slice(1));for(let i=0;i<values.length;i++)for(let j=0;j<offset;j++)values[i]===1?newValues[index++]=tValues[i]:newValues[index++]=eValues[i];return backend2.makeTensorInfo(t.shape,resultDtype,newValues)}var selectConfig={kernelName:Select,backendName:"cpu",kernelFunc:select},scaleAlpha=backend_util_exports.SELU_SCALEALPHA,scale=backend_util_exports.SELU_SCALE,selu2=unaryKernelFunc(Selu,xi=>xi>=0?scale*xi:scaleAlpha*(Math.exp(xi)-1)),seluConfig={kernelName:Selu,backendName:"cpu",kernelFunc:selu2},sigmoid2=unaryKernelFunc(Sigmoid,xi=>1/(1+Math.exp(-xi))),sigmoidConfig={kernelName:Sigmoid,backendName:"cpu",kernelFunc:sigmoid2},sign2=unaryKernelFunc(Sign,xi=>xi<0?-1:xi>0?1:0),signConfig={kernelName:Sign,backendName:"cpu",kernelFunc:sign2},sin2=unaryKernelFunc(Sin,xi=>Math.sin(xi)),sinConfig={kernelName:Sin,backendName:"cpu",kernelFunc:sin2},sinh2=unaryKernelFunc(Sinh,xi=>Math.sinh(xi)),sinhConfig={kernelName:Sinh,backendName:"cpu",kernelFunc:sinh2},epsilon2=11920928955078125e-23,threshold=Math.log(epsilon2)+2,softplus2=unaryKernelFunc(Softplus,xi=>{let tooLarge=xi>-threshold,tooSmall=xi<threshold,expX=Math.exp(xi),result;return tooSmall?result=expX:tooLarge?result=xi:result=Math.log(1+expX),result}),softplusConfig={kernelName:Softplus,backendName:"cpu",kernelFunc:softplus2};function spaceToBatchND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;assertNotComplex([x],"spaceToBatchND");let prod5=util_exports.sizeFromShape(blockShape),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=padV2Config.kernelFunc({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),paddedXReshaped=reshape3({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose2({inputs:{x:paddedXReshaped},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape3({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return backend2.disposeIntermediateTensorInfo(paddedX),backend2.disposeIntermediateTensorInfo(paddedXReshaped),backend2.disposeIntermediateTensorInfo(paddedXT),result}var spaceToBatchNDConfig={kernelName:SpaceToBatchND,backendName:"cpu",kernelFunc:spaceToBatchND2};function sparseToDense2(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,indicesBuf=backend2.bufferSync(sparseIndices),updatesBuf=backend2.bufferSync(sparseValues),$defaultValue=backend2.data.get(defaultValue.dataId).values[0],outBuf=scatterImpl(indicesBuf,updatesBuf,outputShape,outputSize,sliceSize,numUpdates,sliceRank,strides,$defaultValue,sumDupeIndices);return backend2.makeTensorInfo(outputShape,outBuf.dtype,outBuf.values)}var sparseToDenseConfig={kernelName:SparseToDense,backendName:"cpu",kernelFunc:sparseToDense2};function splitV(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[$axis]=s;let sliceT=slice2({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[$axis]+=s,sliceT})}var splitVConfig={kernelName:SplitV,backendName:"cpu",kernelFunc:splitV},sqrt2=unaryKernelFunc(Sqrt,xi=>Math.sqrt(xi)),sqrtConfig={kernelName:Sqrt,backendName:"cpu",kernelFunc:sqrt2},squareConfig={kernelName:Square,backendName:"cpu",kernelFunc:({inputs,backend:backend2})=>{let{x}=inputs,cpuBackend=backend2;assertNotComplex(x,"square");let values=cpuBackend.data.get(x.dataId).values,newValues=new Float32Array(values.length);for(let i=0;i<values.length;++i){let value=values[i];newValues[i]=value*value}return{dataId:cpuBackend.write(newValues,x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}},step2=unaryKernelFunc(Step,(xi,attrs)=>{let stepAttrs=attrs;return isNaN(xi)?NaN:xi>0?1:stepAttrs.alpha}),stepConfig={kernelName:Step,backendName:"cpu",kernelFunc:step2};function stridedSlice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs;assertNotComplex(x,"stridedSlice");let{nonStrided,$begin,$strides,size,newShape,outShape}=slice_util_exports.sliceInfo(x.shape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask),$x=reshape3({inputs:{x},backend:backend2,attrs:{shape:newShape}}),result;if(nonStrided){let sliced=slice2({inputs:{x:$x},backend:backend2,attrs:{begin:$begin,size}});result=reshape3({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(sliced)}else if(outShape.some(axis=>axis===0))result=backend2.makeTensorInfo(outShape,x.dtype,[]);else{let xBuf=backend2.bufferSync($x),outBuf=stridedSliceImpl(outShape,xBuf,$strides,$begin);result=backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:outShape}});return backend2.disposeIntermediateTensorInfo($x),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var stridedSliceConfig={kernelName:StridedSlice,backendName:"cpu",kernelFunc:stridedSlice2},tan2=unaryKernelFunc(Tan,xi=>Math.tan(xi)),tanConfig={kernelName:Tan,backendName:"cpu",kernelFunc:tan2},tanh3=unaryKernelFunc(Tanh,xi=>Math.tanh(xi)),tanhConfig={kernelName:Tanh,backendName:"cpu",kernelFunc:tanh3};function tile3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reps}=attrs;assertNotComplex(x,"tile");let outBuf=tileImpl(backend2.bufferSync(x),reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var tileConfig={kernelName:Tile,backendName:"cpu",kernelFunc:tile3};function topK(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs;assertNotComplex(x,"topk");let xVals=backend2.data.get(x.dataId).values,[allTopKVals,allTopKIndices]=topKImpl(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig={kernelName:TopK,backendName:"cpu",kernelFunc:topK};function unique3(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex(x,"unique");let values=backend2.data.get(x.dataId).values,{outputValues,outputShape,indices}=uniqueImpl(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig={kernelName:Unique,backendName:"cpu",kernelFunc:unique3};function unpack(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let valueRank=value.shape.length,num=value.shape[axis],outShape=new Array(valueRank-1),outIndex=0;for(let i=0;i<valueRank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let begin=new Array(valueRank).fill(0),size=value.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let tempRes=slice2({inputs:{x:value},backend:backend2,attrs:{begin,size}});res[i]=reshape3({inputs:{x:tempRes},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(tempRes)}return res}var unpackConfig={kernelName:Unpack,backendName:"cpu",kernelFunc:unpack};function unsortedSegmentSum2(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs;assertNotComplex(x,"unsortedSegmentSum");let xRank=x.shape.length,segmentIdsRank=segmentIds.shape.length,res=[],intermediates=[],numIters=xRank-segmentIdsRank,$segmentIds=segmentIds;for(let i=0;i<numIters;++i){let expanded=expandDims3({inputs:{input:$segmentIds},backend:backend2,attrs:{dim:i+1}});$segmentIds=expanded,intermediates.push(expanded)}for(let i=0;i<numSegments;++i){let scalarValue=util_exports.createScalarValue(i,"int32"),segmentId=backend2.makeTensorInfo([],"int32",scalarValue),mask=equal2({inputs:{a:segmentId,b:$segmentIds},backend:backend2}),maskCasted=cast3({inputs:{x:mask},backend:backend2,attrs:{dtype:"float32"}}),mul2=multiply2({inputs:{a:maskCasted,b:x},backend:backend2}),sumTensorInfo=sum3({inputs:{x:mul2},backend:backend2,attrs:{axis:0,keepDims:!1}});res.push(sumTensorInfo),intermediates.push(segmentId),intermediates.push(mask),intermediates.push(maskCasted),intermediates.push(mul2),intermediates.push(sumTensorInfo)}let result=pack({inputs:res,backend:backend2,attrs:{axis:0}});return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig={kernelName:UnsortedSegmentSum,backendName:"cpu",kernelFunc:unsortedSegmentSum2},kernelConfigs=[_fusedMatMulConfig,absConfig,acosConfig,acoshConfig,addConfig,addNConfig,allConfig,anyConfig,argMaxConfig,argMinConfig,asinConfig,asinhConfig,atanConfig,atan2Config,atanhConfig,avgPoolConfig,avgPool3DConfig,avgPool3DGradConfig2,avgPoolGradConfig2,batchMatMulConfig,batchNormConfig,batchToSpaceNDConfig,bincountConfig,castConfig,ceilConfig,clipConfig,complexConfig,complexAbsConfig,concatConfig,conv2DBackpropFilterConfig,conv2DBackpropInputConfig,conv2DConfig,conv3DBackpropFilterV2Config,conv3DBackpropInputV2Config,conv3DConfig,cosConfig,coshConfig,cropAndResizeConfig,cumsumConfig,denseBincountConfig,depthToSpaceConfig,depthwiseConv2dNativeConfig,depthwiseConv2dNativeBackpropFilterConfig,depthwiseConv2dNativeBackpropInputConfig,diagConfig,dilation2dConfig,dilation2dBackpropInputConfig,dilation2dBackpropFilterConfig,realDivConfig,eluConfig,eluGradConfig2,equalConfig,erfConfig,expConfig,expandDimsConfig,expm1Config,fftConfig,fillConfig,flipLeftRightConfig,floorConfig,floorDivConfig,fusedConv2DConfig,fusedDepthwiseConv2DConfig,gatherNdConfig,gatherV2Config,greaterConfig,greaterEqualConfig,identityConfig,ifftConfig,imagConfig,isFiniteConfig,isInfConfig,isNaNConfig,leakyReluConfig,lessConfig,lessEqualConfig,linSpaceConfig,logConfig,log1pConfig,logicalAndConfig,logicalNotConfig,logicalOrConfig,lRNConfig,lRNGradConfig,maximumConfig,maxPoolConfig,maxPool3DConfig,maxPool3DGradConfig2,maxPoolGradConfig2,maxPoolWithArgmaxConfig,maxConfig,meanConfig,minConfig,minimumConfig,mirrorPadConfig,modConfig,multinomialConfig,multiplyConfig,negConfig,nonMaxSuppressionV3Config,nonMaxSuppressionV4Config,nonMaxSuppressionV5Config,notEqualConfig,oneHotConfig,onesLikeConfig,packConfig,padV2Config,powConfig,preluConfig,prodConfig,rangeConfig,realConfig,reciprocalConfig,reluConfig,relu6Config,reshapeConfig,resizeBilinearConfig,resizeBilinearGradConfig2,resizeNearestNeighborConfig,resizeNearestNeighborGradConfig2,reverseConfig,rotateWithOffsetConfig,roundConfig,rsqrtConfig,scatterNdConfig,selectConfig,seluConfig,sigmoidConfig,signConfig,sinConfig,sinhConfig,sliceConfig,softmaxConfig,softplusConfig,spaceToBatchNDConfig,sparseToDenseConfig,splitVConfig,sqrtConfig,squareConfig,squaredDifferenceConfig,stepConfig,stridedSliceConfig,subConfig,sumConfig,tanConfig,tanhConfig,tileConfig,topKConfig,transposeConfig,uniqueConfig,unpackConfig,unsortedSegmentSumConfig,zerosLikeConfig];for(let kernelConfig of kernelConfigs)registerKernel(kernelConfig);var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(webGLVersion,gl){contexts[webGLVersion]=gl}function getWebGLContext(webGLVersion){if(!(webGLVersion in contexts)){let newCtx=getWebGLRenderingContext(webGLVersion);if(newCtx!==null)contexts[webGLVersion]=newCtx;else return console.log("Could not get context for WebGL version",webGLVersion),null}let gl=contexts[webGLVersion];return gl.isContextLost()?(delete contexts[webGLVersion],getWebGLContext(webGLVersion)):(gl.disable(gl.DEPTH_TEST),gl.disable(gl.STENCIL_TEST),gl.disable(gl.BLEND),gl.disable(gl.DITHER),gl.disable(gl.POLYGON_OFFSET_FILL),gl.disable(gl.SAMPLE_COVERAGE),gl.enable(gl.SCISSOR_TEST),gl.enable(gl.CULL_FACE),gl.cullFace(gl.BACK),contexts[webGLVersion])}function createCanvas(webGLVersion){if(typeof OffscreenCanvas!="undefined"&&webGLVersion===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function getWebGLRenderingContext(webGLVersion){if(webGLVersion!==1&&webGLVersion!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let canvas=createCanvas(webGLVersion);return canvas.addEventListener("webglcontextlost",ev=>{ev.preventDefault(),delete contexts[webGLVersion]},!1),webGLVersion===1?canvas.getContext("webgl",WEBGL_ATTRIBUTES)||canvas.getContext("experimental-webgl",WEBGL_ATTRIBUTES):canvas.getContext("webgl2",WEBGL_ATTRIBUTES)}var PackingScheme;(function(PackingScheme2){PackingScheme2[PackingScheme2.DENSE=0]="DENSE",PackingScheme2[PackingScheme2.SHARED_BATCH=1]="SHARED_BATCH"})(PackingScheme||(PackingScheme={}));var TextureUsage;(function(TextureUsage2){TextureUsage2[TextureUsage2.RENDER=0]="RENDER",TextureUsage2[TextureUsage2.UPLOAD=1]="UPLOAD",TextureUsage2[TextureUsage2.PIXELS=2]="PIXELS",TextureUsage2[TextureUsage2.DOWNLOAD=3]="DOWNLOAD"})(TextureUsage||(TextureUsage={}));var PhysicalTextureType;(function(PhysicalTextureType2){PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(PhysicalTextureType||(PhysicalTextureType={}));function getUnpackedMatrixTextureShapeWidthHeight(rows,columns){return[columns,rows]}function getUnpackedArraySizeFromMatrixSize(matrixSize,channelsPerTexture){return matrixSize*channelsPerTexture}function getDenseTexShape(shape){let size=util_exports.sizeFromShape(shape),texelsNeeded=Math.ceil(size/4);return util_exports.sizeToSquarishShape(texelsNeeded)}function getPackedMatrixTextureShapeWidthHeight(rows,columns){return[Math.max(1,Math.ceil(columns/2)),Math.max(1,Math.ceil(rows/2))]}function getPackedRGBAArraySizeFromMatrixShape(rows,columns){let[w,h]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return w*h*4}function getTextureConfig(gl,textureHalfFloatExtension){let glany=gl,internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat;return env().getNumber("WEBGL_VERSION")===2?(internalFormatFloat=glany.R32F,internalFormatHalfFloat=glany.R16F,internalFormatPackedHalfFloat=glany.RGBA16F,internalFormatPackedFloat=glany.RGBA32F,textureFormatFloat=glany.RED,downloadUnpackNumChannels=4,defaultNumChannels=1,textureTypeHalfFloat=glany.HALF_FLOAT,textureTypeFloat=glany.FLOAT):(internalFormatFloat=gl.RGBA,internalFormatHalfFloat=gl.RGBA,internalFormatPackedHalfFloat=gl.RGBA,internalFormatPackedFloat=glany.RGBA,textureFormatFloat=gl.RGBA,downloadUnpackNumChannels=4,defaultNumChannels=4,textureTypeHalfFloat=textureHalfFloatExtension!=null?textureHalfFloatExtension.HALF_FLOAT_OES:null,textureTypeFloat=gl.FLOAT),downloadTextureFormat=gl.RGBA,{internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat}}function callAndCheck(gl,func2){let returnValue=func2();return env().getBool("DEBUG")&&checkWebGLError(gl),returnValue}function checkWebGLError(gl){let error=gl.getError();if(error!==gl.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(gl,error))}var MIN_FLOAT16=596e-10,MAX_FLOAT16=65504;function canBeRepresented(num){return!!(env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||num===0||MIN_FLOAT16<Math.abs(num)&&Math.abs(num)<MAX_FLOAT16)}function getWebGLErrorMessage(gl,status){switch(status){case gl.NO_ERROR:return"NO_ERROR";case gl.INVALID_ENUM:return"INVALID_ENUM";case gl.INVALID_VALUE:return"INVALID_VALUE";case gl.INVALID_OPERATION:return"INVALID_OPERATION";case gl.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case gl.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case gl.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${status}`}}function getExtensionOrThrow(gl,extensionName){return throwIfNull(gl,()=>gl.getExtension(extensionName),'Extension "'+extensionName+'" not supported on this browser.')}function createVertexShader(gl,vertexShaderSource){let vertexShader=throwIfNull(gl,()=>gl.createShader(gl.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(vertexShader,vertexShaderSource)),callAndCheck(gl,()=>gl.compileShader(vertexShader)),gl.getShaderParameter(vertexShader,gl.COMPILE_STATUS)===!1)throw console.log(gl.getShaderInfoLog(vertexShader)),new Error("Failed to compile vertex shader.");return vertexShader}function createFragmentShader(gl,fragmentShaderSource){let fragmentShader=throwIfNull(gl,()=>gl.createShader(gl.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(fragmentShader,fragmentShaderSource)),callAndCheck(gl,()=>gl.compileShader(fragmentShader)),gl.getShaderParameter(fragmentShader,gl.COMPILE_STATUS)===!1)throw logShaderSourceAndInfoLog(fragmentShaderSource,gl.getShaderInfoLog(fragmentShader)),new Error("Failed to compile fragment shader.");return fragmentShader}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(shaderSource,shaderInfoLog){let lineNumberRegexResult=lineNumberRegex.exec(shaderInfoLog);if(lineNumberRegexResult==null){console.log(`Couldn't parse line number in error: ${shaderInfoLog}`),console.log(shaderSource);return}let lineNumber=+lineNumberRegexResult[1],shaderLines=shaderSource.split(`
 `),pad3=shaderLines.length.toString().length+2,linesWithLineNumbers=shaderLines.map((line,lineNumber2)=>util_exports.rightPad((lineNumber2+1).toString(),pad3)+line),maxLineLength=0;for(let i=0;i<linesWithLineNumbers.length;i++)maxLineLength=Math.max(linesWithLineNumbers[i].length,maxLineLength);let beforeErrorLines=linesWithLineNumbers.slice(0,lineNumber-1),errorLine=linesWithLineNumbers.slice(lineNumber-1,lineNumber),afterErrorLines=linesWithLineNumbers.slice(lineNumber);console.log(beforeErrorLines.join(`
 `)),console.log(shaderInfoLog.split(`
 `)[0]),console.log(`%c ${util_exports.rightPad(errorLine[0],maxLineLength)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(afterErrorLines.join(`
-`))}function createProgram(gl){return throwIfNull(gl,()=>gl.createProgram(),"Unable to create WebGLProgram.")}function linkProgram(gl,program){if(callAndCheck(gl,()=>gl.linkProgram(program)),gl.getProgramParameter(program,gl.LINK_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(gl,program){if(callAndCheck(gl,()=>gl.validateProgram(program)),gl.getProgramParameter(program,gl.VALIDATE_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createStaticIndexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createTexture(gl){return throwIfNull(gl,()=>gl.createTexture(),"Unable to create WebGLTexture.")}function validateTextureSize(width,height){let maxTextureSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(width<=0||height<=0){let requested=`[${width}x${height}]`;throw new Error("Requested texture size "+requested+" is invalid.")}if(width>maxTextureSize||height>maxTextureSize){let requested=`[${width}x${height}]`,max6=`[${maxTextureSize}x${maxTextureSize}]`;throw new Error("Requested texture size "+requested+" greater than WebGL maximum on this browser / GPU "+max6+".")}}function createFramebuffer(gl){return throwIfNull(gl,()=>gl.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(gl,program,attribute,buffer2,arrayEntriesPerItem,itemStrideInBytes,itemOffsetInBytes){let loc=gl.getAttribLocation(program,attribute);return loc===-1?!1:(callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.vertexAttribPointer(loc,arrayEntriesPerItem,gl.FLOAT,!1,itemStrideInBytes,itemOffsetInBytes)),callAndCheck(gl,()=>gl.enableVertexAttribArray(loc)),!0)}function bindTextureUnit(gl,texture,textureUnit){validateTextureUnit(gl,textureUnit),callAndCheck(gl,()=>gl.activeTexture(gl.TEXTURE0+textureUnit)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture))}function getProgramUniformLocationOrThrow(gl,program,uniformName){return throwIfNull(gl,()=>gl.getUniformLocation(program,uniformName),'uniform "'+uniformName+'" not present in program.')}function getProgramUniformLocation(gl,program,uniformName){return gl.getUniformLocation(program,uniformName)}function bindTextureToProgramUniformSampler(gl,texture,uniformSamplerLocation,textureUnit){callAndCheck(gl,()=>bindTextureUnit(gl,texture,textureUnit)),callAndCheck(gl,()=>gl.uniform1i(uniformSamplerLocation,textureUnit))}function bindColorTextureToFramebuffer(gl,texture,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0))}function unbindColorTextureFromFramebuffer(gl,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,null,0))}function validateFramebuffer(gl){let status=gl.checkFramebufferStatus(gl.FRAMEBUFFER);if(status!==gl.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(gl,status))}function getFramebufferErrorMessage(gl,status){switch(status){case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case gl.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${status}`}}function throwIfNull(gl,returnTOrNull,failureMessage){let tOrNull=callAndCheck(gl,()=>returnTOrNull());if(tOrNull==null)throw new Error(failureMessage);return tOrNull}function validateTextureUnit(gl,textureUnit){let maxTextureUnit=gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,glTextureUnit=textureUnit+gl.TEXTURE0;if(glTextureUnit<gl.TEXTURE0||glTextureUnit>maxTextureUnit){let textureUnitRange=`[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;throw new Error(`textureUnit must be in ${textureUnitRange}.`)}}function getBatchDim(shape,dimsToSkip=2){return util_exports.sizeFromShape(shape.slice(0,shape.length-dimsToSkip))}function getRowsCols(shape){if(shape.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[shape.length>1?shape[shape.length-2]:1,shape[shape.length-1]]}function getShapeAs3D(shape){let shapeAs3D=[1,1,1];return shape.length===0||shape.length===1&&shape[0]===1||(shapeAs3D=[getBatchDim(shape),...getRowsCols(shape)]),shapeAs3D}function getTextureShapeFromLogicalShape(logShape,isPacked=!1){let maxTexSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");isPacked&&(maxTexSize=maxTexSize*2,logShape=logShape.map((d,i)=>i>=logShape.length-2?util_exports.nearestLargerEven(logShape[i]):logShape[i]),logShape.length===1&&(logShape=[2,logShape[0]])),logShape.length!==2&&(logShape=util_exports.squeezeShape(logShape).newShape);let size=util_exports.sizeFromShape(logShape);if(logShape.length<=1&&size<=maxTexSize)return[1,size];if(logShape.length===2&&logShape[0]<=maxTexSize&&logShape[1]<=maxTexSize)return logShape;if(logShape.length===3&&logShape[0]*logShape[1]<=maxTexSize&&logShape[2]<=maxTexSize)return[logShape[0]*logShape[1],logShape[2]];if(logShape.length===3&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]];if(logShape.length===4&&logShape[0]*logShape[1]*logShape[2]<=maxTexSize&&logShape[3]<=maxTexSize)return[logShape[0]*logShape[1]*logShape[2],logShape[3]];if(logShape.length===4&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]*logShape[3]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]*logShape[3]];if(isPacked){let batchDim=getBatchDim(logShape),rows=2,cols=2;return logShape.length&&([rows,cols]=getRowsCols(logShape)),size=batchDim*(rows/2)*(cols/2),util_exports.sizeToSquarishShape(size).map(d=>d*2)}return util_exports.sizeToSquarishShape(size)}function isEven(n){return n%2===0}function isReshapeFree(shape1,shape2){if(shape1=shape1.slice(-2),shape2=shape2.slice(-2),util_exports.arraysEqual(shape1,shape2))return!0;if(!shape1.length||!shape2.length)return!0;if(shape1[0]===0||shape1[1]===0||shape2[0]===0||shape2[1]===0)return!0;if(shape1.length!==shape2.length){let shape1Cols=shape1.slice(-1)[0],shape2Cols=shape2.slice(-1)[0];if(shape1Cols===shape2Cols)return!0;if(isEven(shape1Cols)&&isEven(shape2Cols)&&(shape1[0]===1||shape2[0]===1))return!0}return shape1[1]===shape2[1]&&isEven(shape1[0])&&isEven(shape2[0])}var MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER;function getWebGLMaxTextureSize(webGLVersion){if(MAX_TEXTURE_SIZE==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURE_SIZE=gl.getParameter(gl.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(webGLVersion){if(MAX_TEXTURES_IN_SHADER==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURES_IN_SHADER=gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(webGLVersion){if(webGLVersion===0)return 0;let queryTimerVersion,gl=getWebGLContext(webGLVersion);return hasExtension(gl,"EXT_disjoint_timer_query_webgl2")&&webGLVersion===2?queryTimerVersion=2:hasExtension(gl,"EXT_disjoint_timer_query")?queryTimerVersion=1:queryTimerVersion=0,queryTimerVersion}function hasExtension(gl,extensionName){return gl.getExtension(extensionName)!=null}function isWebGLVersionEnabled(webGLVersion){try{if(getWebGLContext(webGLVersion)!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function isCapableOfRenderingToFloatTexture(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1}else if(!hasExtension(gl,"EXT_color_buffer_float"))return!1;return createFloatTextureAndBindToFramebuffer(gl)}function isDownloadFloatTextureEnabled(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1;if(!hasExtension(gl,"WEBGL_color_buffer_float"))return!1}else{if(hasExtension(gl,"EXT_color_buffer_float"))return createFloatTextureAndBindToFramebuffer(gl);let COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(hasExtension(gl,COLOR_BUFFER_HALF_FLOAT)){let textureHalfFloatExtension=gl.getExtension(COLOR_BUFFER_HALF_FLOAT);return createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension)}return!1}return createFloatTextureAndBindToFramebuffer(gl)}function createFloatTextureAndBindToFramebuffer(gl){let texConfig=getTextureConfig(gl),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension){let texConfig=getTextureConfig(gl,textureHalfFloatExtension),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatHalfFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeHalfFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function isWebGLFenceEnabled(webGLVersion){return webGLVersion!==2?!1:getWebGLContext(webGLVersion).fenceSync!=null}function assertNotComplex2(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the WebGL backend.`)})}var ENV3=env();ENV3.registerFlag("HAS_WEBGL",()=>ENV3.getNumber("WEBGL_VERSION")>0);ENV3.registerFlag("WEBGL_VERSION",()=>isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0);ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ENV3.get("WEBGL_VERSION")===2);ENV3.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ENV3.registerFlag("WEBGL_PACK",()=>ENV3.getBool("HAS_WEBGL"));ENV3.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_CLIP",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_REDUCE",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_LAZILY_UNPACK",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_CONV_IM2COL",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let webGLVersion=ENV3.getNumber("WEBGL_VERSION");return webGLVersion===0?0:getWebGLDisjointQueryTimerVersion(webGLVersion)});ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!device_util_exports.isMobile());ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ENV3.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_FENCE_API_ENABLED",()=>isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,threshold2=>{if(threshold2<0&&threshold2!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`)});function getGlslDifferences(){let version102,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound;return env().getNumber("WEBGL_VERSION")===2?(version102="#version 300 es",attribute="in",varyingVs="out",varyingFs="in",texture2D="texture",output="outputColor",defineOutput="out vec4 outputColor;",defineSpecialNaN=`
+`))}function createProgram(gl){return throwIfNull(gl,()=>gl.createProgram(),"Unable to create WebGLProgram.")}function linkProgram(gl,program){if(callAndCheck(gl,()=>gl.linkProgram(program)),gl.getProgramParameter(program,gl.LINK_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(gl,program){if(callAndCheck(gl,()=>gl.validateProgram(program)),gl.getProgramParameter(program,gl.VALIDATE_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createStaticIndexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createTexture(gl){return throwIfNull(gl,()=>gl.createTexture(),"Unable to create WebGLTexture.")}function validateTextureSize(width,height){let maxTextureSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(width<=0||height<=0){let requested=`[${width}x${height}]`;throw new Error("Requested texture size "+requested+" is invalid.")}if(width>maxTextureSize||height>maxTextureSize){let requested=`[${width}x${height}]`,max6=`[${maxTextureSize}x${maxTextureSize}]`;throw new Error("Requested texture size "+requested+" greater than WebGL maximum on this browser / GPU "+max6+".")}}function createFramebuffer(gl){return throwIfNull(gl,()=>gl.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(gl,program,attribute,buffer2,arrayEntriesPerItem,itemStrideInBytes,itemOffsetInBytes){let loc=gl.getAttribLocation(program,attribute);return loc===-1?!1:(callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.vertexAttribPointer(loc,arrayEntriesPerItem,gl.FLOAT,!1,itemStrideInBytes,itemOffsetInBytes)),callAndCheck(gl,()=>gl.enableVertexAttribArray(loc)),!0)}function bindTextureUnit(gl,texture,textureUnit){validateTextureUnit(gl,textureUnit),callAndCheck(gl,()=>gl.activeTexture(gl.TEXTURE0+textureUnit)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture))}function getProgramUniformLocationOrThrow(gl,program,uniformName){return throwIfNull(gl,()=>gl.getUniformLocation(program,uniformName),'uniform "'+uniformName+'" not present in program.')}function getProgramUniformLocation(gl,program,uniformName){return gl.getUniformLocation(program,uniformName)}function bindTextureToProgramUniformSampler(gl,texture,uniformSamplerLocation,textureUnit){callAndCheck(gl,()=>bindTextureUnit(gl,texture,textureUnit)),callAndCheck(gl,()=>gl.uniform1i(uniformSamplerLocation,textureUnit))}function bindColorTextureToFramebuffer(gl,texture,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0))}function unbindColorTextureFromFramebuffer(gl,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,null,0))}function validateFramebuffer(gl){let status=gl.checkFramebufferStatus(gl.FRAMEBUFFER);if(status!==gl.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(gl,status))}function getFramebufferErrorMessage(gl,status){switch(status){case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case gl.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${status}`}}function throwIfNull(gl,returnTOrNull,failureMessage){let tOrNull=callAndCheck(gl,()=>returnTOrNull());if(tOrNull==null)throw new Error(failureMessage);return tOrNull}function validateTextureUnit(gl,textureUnit){let maxTextureUnit=gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,glTextureUnit=textureUnit+gl.TEXTURE0;if(glTextureUnit<gl.TEXTURE0||glTextureUnit>maxTextureUnit){let textureUnitRange=`[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;throw new Error(`textureUnit must be in ${textureUnitRange}.`)}}function getBatchDim(shape,dimsToSkip=2){return util_exports.sizeFromShape(shape.slice(0,shape.length-dimsToSkip))}function getRowsCols(shape){if(shape.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[shape.length>1?shape[shape.length-2]:1,shape[shape.length-1]]}function getShapeAs3D(shape){let shapeAs3D=[1,1,1];return shape.length===0||shape.length===1&&shape[0]===1||(shapeAs3D=[getBatchDim(shape),...getRowsCols(shape)]),shapeAs3D}function getTextureShapeFromLogicalShape(logShape,isPacked=!1){let maxTexSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");isPacked&&(maxTexSize=maxTexSize*2,logShape=logShape.map((d,i)=>i>=logShape.length-2?util_exports.nearestLargerEven(logShape[i]):logShape[i]),logShape.length===1&&(logShape=[2,logShape[0]])),logShape.length!==2&&(logShape=util_exports.squeezeShape(logShape).newShape);let size=util_exports.sizeFromShape(logShape);if(logShape.length<=1&&size<=maxTexSize)return[1,size];if(logShape.length===2&&logShape[0]<=maxTexSize&&logShape[1]<=maxTexSize)return logShape;if(logShape.length===3&&logShape[0]*logShape[1]<=maxTexSize&&logShape[2]<=maxTexSize)return[logShape[0]*logShape[1],logShape[2]];if(logShape.length===3&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]];if(logShape.length===4&&logShape[0]*logShape[1]*logShape[2]<=maxTexSize&&logShape[3]<=maxTexSize)return[logShape[0]*logShape[1]*logShape[2],logShape[3]];if(logShape.length===4&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]*logShape[3]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]*logShape[3]];if(isPacked){let batchDim=getBatchDim(logShape),rows=2,cols=2;return logShape.length&&([rows,cols]=getRowsCols(logShape)),size=batchDim*(rows/2)*(cols/2),util_exports.sizeToSquarishShape(size).map(d=>d*2)}return util_exports.sizeToSquarishShape(size)}function isEven(n){return n%2==0}function isReshapeFree(shape1,shape2){if(shape1=shape1.slice(-2),shape2=shape2.slice(-2),util_exports.arraysEqual(shape1,shape2)||!shape1.length||!shape2.length||shape1[0]===0||shape1[1]===0||shape2[0]===0||shape2[1]===0)return!0;if(shape1.length!==shape2.length){let shape1Cols=shape1.slice(-1)[0],shape2Cols=shape2.slice(-1)[0];if(shape1Cols===shape2Cols||isEven(shape1Cols)&&isEven(shape2Cols)&&(shape1[0]===1||shape2[0]===1))return!0}return shape1[1]===shape2[1]&&isEven(shape1[0])&&isEven(shape2[0])}var MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER;function getWebGLMaxTextureSize(webGLVersion){if(MAX_TEXTURE_SIZE==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURE_SIZE=gl.getParameter(gl.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(webGLVersion){if(MAX_TEXTURES_IN_SHADER==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURES_IN_SHADER=gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(webGLVersion){if(webGLVersion===0)return 0;let queryTimerVersion,gl=getWebGLContext(webGLVersion);return hasExtension(gl,"EXT_disjoint_timer_query_webgl2")&&webGLVersion===2?queryTimerVersion=2:hasExtension(gl,"EXT_disjoint_timer_query")?queryTimerVersion=1:queryTimerVersion=0,queryTimerVersion}function hasExtension(gl,extensionName){return gl.getExtension(extensionName)!=null}function isWebGLVersionEnabled(webGLVersion){try{if(getWebGLContext(webGLVersion)!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function isCapableOfRenderingToFloatTexture(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1}else if(!hasExtension(gl,"EXT_color_buffer_float"))return!1;return createFloatTextureAndBindToFramebuffer(gl)}function isDownloadFloatTextureEnabled(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float")||!hasExtension(gl,"WEBGL_color_buffer_float"))return!1}else{if(hasExtension(gl,"EXT_color_buffer_float"))return createFloatTextureAndBindToFramebuffer(gl);let COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(hasExtension(gl,COLOR_BUFFER_HALF_FLOAT)){let textureHalfFloatExtension=gl.getExtension(COLOR_BUFFER_HALF_FLOAT);return createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension)}return!1}return createFloatTextureAndBindToFramebuffer(gl)}function createFloatTextureAndBindToFramebuffer(gl){let texConfig=getTextureConfig(gl),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension){let texConfig=getTextureConfig(gl,textureHalfFloatExtension),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatHalfFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeHalfFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function isWebGLFenceEnabled(webGLVersion){return webGLVersion!==2?!1:getWebGLContext(webGLVersion).fenceSync!=null}function assertNotComplex2(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the WebGL backend.`)})}var ENV3=env();ENV3.registerFlag("HAS_WEBGL",()=>ENV3.getNumber("WEBGL_VERSION")>0);ENV3.registerFlag("WEBGL_VERSION",()=>isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0);ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ENV3.get("WEBGL_VERSION")===2);ENV3.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ENV3.registerFlag("WEBGL_PACK",()=>ENV3.getBool("HAS_WEBGL"));ENV3.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_CLIP",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_REDUCE",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_LAZILY_UNPACK",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_CONV_IM2COL",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let webGLVersion=ENV3.getNumber("WEBGL_VERSION");return webGLVersion===0?0:getWebGLDisjointQueryTimerVersion(webGLVersion)});ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!device_util_exports.isMobile());ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ENV3.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_FENCE_API_ENABLED",()=>isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,threshold2=>{if(threshold2<0&&threshold2!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`)});function getGlslDifferences(){let version102,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound;return env().getNumber("WEBGL_VERSION")===2?(version102="#version 300 es",attribute="in",varyingVs="out",varyingFs="in",texture2D="texture",output="outputColor",defineOutput="out vec4 outputColor;",defineSpecialNaN=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -269,7 +269,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return createVertexShader(gl,vertexShaderSource)}function createVertexBuffer(gl){let vertexArray=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return createStaticVertexBuffer(gl,vertexArray)}function createIndexBuffer(gl){let triangleVertexIndices=new Uint16Array([0,1,2,2,1,3]);return createStaticIndexBuffer(gl,triangleVertexIndices)}function createAndConfigureTexture(gl,width,height,internalFormat,textureFormat,textureType){validateTextureSize(width,height);let texture=createTexture(gl),tex2d=gl.TEXTURE_2D;return callAndCheck(gl,()=>gl.bindTexture(tex2d,texture)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MIN_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MAG_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texImage2D(tex2d,0,internalFormat,width,height,0,textureFormat,textureType,null)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null)),texture}function getInternalFormatForFloat32MatrixTexture(textureConfig){return textureConfig.internalFormatFloat}function createFloat32MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat32MatrixTexture(textureConfig),textureConfig.textureFormatFloat,gl.FLOAT)}function getInternalFormatForFloat16MatrixTexture(textureConfig){return textureConfig.internalFormatHalfFloat}function createFloat16MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16MatrixTexture(textureConfig),textureConfig.textureFormatFloat,textureConfig.textureTypeHalfFloat)}function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig){return textureConfig.downloadTextureFormat}function createUnsignedBytesMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForUnsignedBytesMatrixTexture(textureConfig),gl.RGBA,gl.UNSIGNED_BYTE)}function getInternalFormatForPackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedFloat}function createPackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForPackedMatrixTexture(textureConfig),gl.RGBA,gl.FLOAT)}function getInternalFormatForFloat16PackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedHalfFloat}function createFloat16PackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16PackedMatrixTexture(textureConfig),gl.RGBA,textureConfig.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(gl,program,vertexBuffer){let posOffset=0,uvOffset=3*4,stride=3*4+2*4;return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,vertexBuffer)),bindVertexBufferToProgramAttribute(gl,program,"clipSpacePos",vertexBuffer,3,stride,posOffset)&&bindVertexBufferToProgramAttribute(gl,program,"uv",vertexBuffer,2,stride,uvOffset)}function uploadDenseMatrixToTexture(gl,texture,width,height,data,textureConfig){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture));let dataForUpload,texelDataType,internalFormat;data instanceof Uint8Array?(dataForUpload=new Uint8Array(width*height*4),texelDataType=gl.UNSIGNED_BYTE,internalFormat=gl.RGBA):(dataForUpload=new Float32Array(width*height*4),texelDataType=gl.FLOAT,internalFormat=textureConfig.internalFormatPackedFloat),dataForUpload.set(data),callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,internalFormat,width,height,0,gl.RGBA,texelDataType,dataForUpload)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function uploadPixelDataToTexture(gl,texture,pixels){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture)),pixels.data instanceof Uint8Array?callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,pixels.width,pixels.height,0,gl.RGBA,gl.UNSIGNED_BYTE,pixels.data)):callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,pixels)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function createBufferFromOutputTexture(gl2,rows,columns,textureConfig){let buffer2=gl2.createBuffer();callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2));let bufferSizeBytes=4*4*rows*columns;return callAndCheck(gl2,()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER,bufferSizeBytes,gl2.STREAM_READ)),callAndCheck(gl2,()=>gl2.readPixels(0,0,columns,rows,gl2.RGBA,gl2.FLOAT,0)),callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null)),buffer2}function downloadFloat32MatrixFromBuffer(gl,buffer2,size){let gl2=gl,downloadTarget=new Float32Array(size);return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadByteEncodedFloatMatrixFromOutputTexture(gl,rows,columns,textureConfig){let[w,h]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns),numChannels=4,downloadTarget=new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows*columns,numChannels));return callAndCheck(gl,()=>gl.readPixels(0,0,w,h,textureConfig.downloadTextureFormat,gl.UNSIGNED_BYTE,downloadTarget)),new Float32Array(downloadTarget.buffer)}function downloadPackedMatrixFromBuffer(gl,buffer2,batch,rows,cols,physicalRows,physicalCols,textureConfig){let gl2=gl,downloadTarget=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows,physicalCols));return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadMatrixFromPackedOutputTexture(gl,physicalRows,physicalCols){let packedRGBA=new Float32Array(physicalRows*physicalCols*4);return callAndCheck(gl,()=>gl.readPixels(0,0,physicalCols,physicalRows,gl.RGBA,gl.FLOAT,packedRGBA)),packedRGBA}var GPGPUContext=class{constructor(gl){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let glVersion=env().getNumber("WEBGL_VERSION");gl!=null?(this.gl=gl,setWebGLContext(glVersion,gl)):this.gl=getWebGLContext(glVersion);let COLOR_BUFFER_FLOAT="WEBGL_color_buffer_float",COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(env().getNumber("WEBGL_VERSION")===1){let TEXTURE_FLOAT="OES_texture_float",TEXTURE_HALF_FLOAT="OES_texture_half_float";if(this.textureFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_FLOAT),hasExtension(this.gl,TEXTURE_HALF_FLOAT))this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT),hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=getExtensionOrThrow(this.gl,COLOR_BUFFER_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(COLOR_BUFFER_FLOAT="EXT_color_buffer_float",hasExtension(this.gl,COLOR_BUFFER_FLOAT))this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT);else if(hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}get debug(){return env().getBool("DEBUG")}dispose(){if(!this.disposed){this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let gl=this.gl;callAndCheck(gl,()=>gl.finish()),callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,null)),callAndCheck(gl,()=>gl.deleteFramebuffer(this.framebuffer)),callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.deleteBuffer(this.indexBuffer)),this.disposed=!0}}createFloat32MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,rows,columns,this.textureConfig)}createFloat16MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,rows,columns,this.textureConfig)}createUnsignedBytesMatrixTexture(rows,columns){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,rows,columns,this.textureConfig)}uploadPixelDataToTexture(texture,pixels){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,texture,pixels)}uploadDenseMatrixToTexture(texture,width,height,data){this.throwIfDisposed(),uploadDenseMatrixToTexture(this.gl,texture,width,height,data,this.textureConfig)}createFloat16PackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}createPackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}deleteMatrixTexture(texture){this.throwIfDisposed(),this.outputTexture===texture&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,()=>this.gl.deleteTexture(texture))}downloadByteEncodedFloatMatrixFromOutputTexture(texture,rows,columns){return this.downloadMatrixDriver(texture,()=>downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,rows,columns,this.textureConfig))}downloadPackedMatrixFromBuffer(buffer2,batch,rows,columns,physicalRows,physicalCols){return downloadPackedMatrixFromBuffer(this.gl,buffer2,batch,rows,columns,physicalRows,physicalCols,this.textureConfig)}downloadFloat32MatrixFromBuffer(buffer2,size){return downloadFloat32MatrixFromBuffer(this.gl,buffer2,size)}createBufferFromTexture(texture,rows,columns){this.bindTextureToFrameBuffer(texture);let result=createBufferFromOutputTexture(this.gl,rows,columns,this.textureConfig);return this.unbindTextureToFrameBuffer(),result}createAndWaitForFence(){let fenceContext=this.createFence(this.gl);return this.pollFence(fenceContext)}createFence(gl){let query,isFencePassed;if(env().getBool("WEBGL_FENCE_API_ENABLED")){let gl2=gl,sync=gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE,0);gl.flush(),isFencePassed=()=>{let status=gl2.clientWaitSync(sync,0,0);return status===gl2.ALREADY_SIGNALED||status===gl2.CONDITION_SATISFIED},query=sync}else env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(query=this.beginQuery(),this.endQuery(),isFencePassed=()=>this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):isFencePassed=()=>!0;return{query,isFencePassed}}downloadMatrixFromPackedTexture(texture,physicalRows,physicalCols){return this.downloadMatrixDriver(texture,()=>downloadMatrixFromPackedOutputTexture(this.gl,physicalRows,physicalCols))}createProgram(fragmentShaderSource){this.throwIfDisposed();let gl=this.gl,fragmentShader=createFragmentShader(gl,fragmentShaderSource),vertexShader=createVertexShader2(gl),program=createProgram(gl);return callAndCheck(gl,()=>gl.attachShader(program,vertexShader)),callAndCheck(gl,()=>gl.attachShader(program,fragmentShader)),linkProgram(gl,program),this.debug&&validateProgram(gl,program),this.vertexAttrsAreBound||(this.setProgram(program),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(gl,this.program,this.vertexBuffer)),program}deleteProgram(program){this.throwIfDisposed(),program===this.program&&(this.program=null),program!=null&&callAndCheck(this.gl,()=>this.gl.deleteProgram(program))}setProgram(program){this.throwIfDisposed(),this.program=program,this.program!=null&&this.debug&&validateProgram(this.gl,this.program),callAndCheck(this.gl,()=>this.gl.useProgram(program))}getUniformLocation(program,uniformName,shouldThrow=!0){return this.throwIfDisposed(),shouldThrow?getProgramUniformLocationOrThrow(this.gl,program,uniformName):getProgramUniformLocation(this.gl,program,uniformName)}getAttributeLocation(program,attribute){return this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.getAttribLocation(program,attribute))}getUniformLocationNoThrow(program,uniformName){return this.throwIfDisposed(),this.gl.getUniformLocation(program,uniformName)}setInputMatrixTexture(inputMatrixTexture,uniformLocation,textureUnit){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,inputMatrixTexture,uniformLocation,textureUnit)}setOutputMatrixTexture(outputMatrixTexture,rows,columns){this.setOutputMatrixTextureDriver(outputMatrixTexture,columns,rows)}setOutputPackedMatrixTexture(outputPackedMatrixTexture,rows,columns){this.throwIfDisposed();let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);this.setOutputMatrixTextureDriver(outputPackedMatrixTexture,width,height)}setOutputMatrixWriteRegion(startRow,numRows,startColumn,numColumns){this.setOutputMatrixWriteRegionDriver(startColumn,startRow,numColumns,numRows)}setOutputPackedMatrixWriteRegion(startRow,numRows,startColumn,numColumns){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let gl=this.gl;this.debug&&this.debugValidate(),callAndCheck(gl,()=>gl.drawElements(gl.TRIANGLES,6,gl.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2(),query2=gl2.createQuery();return gl2.beginQuery(ext2.TIME_ELAPSED_EXT,query2),query2}let ext=this.getQueryTimerExtensionWebGL1(),query=ext.createQueryEXT();return ext.beginQueryEXT(ext.TIME_ELAPSED_EXT,query),query}endQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2();gl2.endQuery(ext2.TIME_ELAPSED_EXT);return}let ext=this.getQueryTimerExtensionWebGL1();ext.endQueryEXT(ext.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(query){return await util_exports.repeatedTry(()=>this.disposed||this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(query,queryTimerVersion){if(queryTimerVersion===0)return null;if(queryTimerVersion===2){let gl2=this.gl;return gl2.getQueryParameter(query,gl2.QUERY_RESULT)/1e6}else{let ext=this.getQueryTimerExtensionWebGL1();return ext.getQueryObjectEXT(query,ext.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(query,queryTimerVersion){if(queryTimerVersion===0)return!0;if(queryTimerVersion===2){let gl2=this.gl,ext=this.getQueryTimerExtensionWebGL2(),available=gl2.getQueryParameter(query,gl2.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}else{let ext=this.getQueryTimerExtensionWebGL1(),available=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}}pollFence(fenceContext){return new Promise(resolve=>{this.addItemToPoll(()=>fenceContext.isFencePassed(),()=>resolve())})}pollItems(){let index=linearSearchLastTrue(this.itemsToPoll.map(x=>x.isDoneFn));for(let i=0;i<=index;++i){let{resolveFn}=this.itemsToPoll[i];resolveFn()}this.itemsToPoll=this.itemsToPoll.slice(index+1)}addItemToPoll(isDoneFn,resolveFn){(this.itemsToPoll.push({isDoneFn,resolveFn}),!(this.itemsToPoll.length>1))&&util_exports.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(texture){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,texture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(texture,downloadAndDecode){this.bindTextureToFrameBuffer(texture);let result=downloadAndDecode();return this.unbindTextureToFrameBuffer(),result}setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked,width,height){this.throwIfDisposed();let gl=this.gl;bindColorTextureToFramebuffer(gl,outputMatrixTextureMaybePacked,this.framebuffer),this.debug&&validateFramebuffer(gl),this.outputTexture=outputMatrixTextureMaybePacked,callAndCheck(gl,()=>gl.viewport(0,0,width,height)),callAndCheck(gl,()=>gl.scissor(0,0,width,height))}setOutputMatrixWriteRegionDriver(x,y,width,height){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.scissor(x,y,width,height))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function linearSearchLastTrue(arr){let i=0;for(;i<arr.length&&arr[i]();++i);return i-1}var{getBroadcastDims:getBroadcastDims2}=backend_util_exports;function makeShader(inputsInfo,outputShape,userCode,usesPackedTextures){let prefixSnippets=[];inputsInfo.forEach(x=>{let size=util_exports.sizeFromShape(x.shapeInfo.logicalShape);x.shapeInfo.isUniform?prefixSnippets.push(`uniform float ${x.name}${size>1?`[${size}]`:""};`):(prefixSnippets.push(`uniform sampler2D ${x.name};`),prefixSnippets.push(`uniform int offset${x.name};`))});let inputPrefixSnippet=prefixSnippets.join(`
+    }`;return createVertexShader(gl,vertexShaderSource)}function createVertexBuffer(gl){let vertexArray=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return createStaticVertexBuffer(gl,vertexArray)}function createIndexBuffer(gl){let triangleVertexIndices=new Uint16Array([0,1,2,2,1,3]);return createStaticIndexBuffer(gl,triangleVertexIndices)}function createAndConfigureTexture(gl,width,height,internalFormat,textureFormat,textureType){validateTextureSize(width,height);let texture=createTexture(gl),tex2d=gl.TEXTURE_2D;return callAndCheck(gl,()=>gl.bindTexture(tex2d,texture)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MIN_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MAG_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texImage2D(tex2d,0,internalFormat,width,height,0,textureFormat,textureType,null)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null)),texture}function getInternalFormatForFloat32MatrixTexture(textureConfig){return textureConfig.internalFormatFloat}function createFloat32MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat32MatrixTexture(textureConfig),textureConfig.textureFormatFloat,gl.FLOAT)}function getInternalFormatForFloat16MatrixTexture(textureConfig){return textureConfig.internalFormatHalfFloat}function createFloat16MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16MatrixTexture(textureConfig),textureConfig.textureFormatFloat,textureConfig.textureTypeHalfFloat)}function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig){return textureConfig.downloadTextureFormat}function createUnsignedBytesMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForUnsignedBytesMatrixTexture(textureConfig),gl.RGBA,gl.UNSIGNED_BYTE)}function getInternalFormatForPackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedFloat}function createPackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForPackedMatrixTexture(textureConfig),gl.RGBA,gl.FLOAT)}function getInternalFormatForFloat16PackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedHalfFloat}function createFloat16PackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16PackedMatrixTexture(textureConfig),gl.RGBA,textureConfig.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(gl,program,vertexBuffer){let posOffset=0,uvOffset=3*4,stride=3*4+2*4;return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,vertexBuffer)),bindVertexBufferToProgramAttribute(gl,program,"clipSpacePos",vertexBuffer,3,stride,posOffset)&&bindVertexBufferToProgramAttribute(gl,program,"uv",vertexBuffer,2,stride,uvOffset)}function uploadDenseMatrixToTexture(gl,texture,width,height,data,textureConfig){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture));let dataForUpload,texelDataType,internalFormat;data instanceof Uint8Array?(dataForUpload=new Uint8Array(width*height*4),texelDataType=gl.UNSIGNED_BYTE,internalFormat=gl.RGBA):(dataForUpload=new Float32Array(width*height*4),texelDataType=gl.FLOAT,internalFormat=textureConfig.internalFormatPackedFloat),dataForUpload.set(data),callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,internalFormat,width,height,0,gl.RGBA,texelDataType,dataForUpload)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function uploadPixelDataToTexture(gl,texture,pixels){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture)),pixels.data instanceof Uint8Array?callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,pixels.width,pixels.height,0,gl.RGBA,gl.UNSIGNED_BYTE,pixels.data)):callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,pixels)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function createBufferFromOutputTexture(gl2,rows,columns,textureConfig){let buffer2=gl2.createBuffer();callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2));let bufferSizeBytes=4*4*rows*columns;return callAndCheck(gl2,()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER,bufferSizeBytes,gl2.STREAM_READ)),callAndCheck(gl2,()=>gl2.readPixels(0,0,columns,rows,gl2.RGBA,gl2.FLOAT,0)),callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null)),buffer2}function downloadFloat32MatrixFromBuffer(gl,buffer2,size){let gl2=gl,downloadTarget=new Float32Array(size);return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadByteEncodedFloatMatrixFromOutputTexture(gl,rows,columns,textureConfig){let[w,h]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns),numChannels=4,downloadTarget=new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows*columns,numChannels));return callAndCheck(gl,()=>gl.readPixels(0,0,w,h,textureConfig.downloadTextureFormat,gl.UNSIGNED_BYTE,downloadTarget)),new Float32Array(downloadTarget.buffer)}function downloadPackedMatrixFromBuffer(gl,buffer2,batch,rows,cols,physicalRows,physicalCols,textureConfig){let gl2=gl,downloadTarget=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows,physicalCols));return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadMatrixFromPackedOutputTexture(gl,physicalRows,physicalCols){let packedRGBA=new Float32Array(physicalRows*physicalCols*4);return callAndCheck(gl,()=>gl.readPixels(0,0,physicalCols,physicalRows,gl.RGBA,gl.FLOAT,packedRGBA)),packedRGBA}var GPGPUContext=class{constructor(gl){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let glVersion=env().getNumber("WEBGL_VERSION");gl!=null?(this.gl=gl,setWebGLContext(glVersion,gl)):this.gl=getWebGLContext(glVersion);let COLOR_BUFFER_FLOAT="WEBGL_color_buffer_float",COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(env().getNumber("WEBGL_VERSION")===1){let TEXTURE_FLOAT="OES_texture_float",TEXTURE_HALF_FLOAT="OES_texture_half_float";if(this.textureFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_FLOAT),hasExtension(this.gl,TEXTURE_HALF_FLOAT))this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT),hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=getExtensionOrThrow(this.gl,COLOR_BUFFER_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(COLOR_BUFFER_FLOAT="EXT_color_buffer_float",hasExtension(this.gl,COLOR_BUFFER_FLOAT))this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT);else if(hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}get debug(){return env().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let gl=this.gl;callAndCheck(gl,()=>gl.finish()),callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,null)),callAndCheck(gl,()=>gl.deleteFramebuffer(this.framebuffer)),callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,rows,columns,this.textureConfig)}createFloat16MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,rows,columns,this.textureConfig)}createUnsignedBytesMatrixTexture(rows,columns){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,rows,columns,this.textureConfig)}uploadPixelDataToTexture(texture,pixels){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,texture,pixels)}uploadDenseMatrixToTexture(texture,width,height,data){this.throwIfDisposed(),uploadDenseMatrixToTexture(this.gl,texture,width,height,data,this.textureConfig)}createFloat16PackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}createPackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}deleteMatrixTexture(texture){this.throwIfDisposed(),this.outputTexture===texture&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,()=>this.gl.deleteTexture(texture))}downloadByteEncodedFloatMatrixFromOutputTexture(texture,rows,columns){return this.downloadMatrixDriver(texture,()=>downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,rows,columns,this.textureConfig))}downloadPackedMatrixFromBuffer(buffer2,batch,rows,columns,physicalRows,physicalCols){return downloadPackedMatrixFromBuffer(this.gl,buffer2,batch,rows,columns,physicalRows,physicalCols,this.textureConfig)}downloadFloat32MatrixFromBuffer(buffer2,size){return downloadFloat32MatrixFromBuffer(this.gl,buffer2,size)}createBufferFromTexture(texture,rows,columns){this.bindTextureToFrameBuffer(texture);let result=createBufferFromOutputTexture(this.gl,rows,columns,this.textureConfig);return this.unbindTextureToFrameBuffer(),result}createAndWaitForFence(){let fenceContext=this.createFence(this.gl);return this.pollFence(fenceContext)}createFence(gl){let query,isFencePassed;if(env().getBool("WEBGL_FENCE_API_ENABLED")){let gl2=gl,sync=gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE,0);gl.flush(),isFencePassed=()=>{let status=gl2.clientWaitSync(sync,0,0);return status===gl2.ALREADY_SIGNALED||status===gl2.CONDITION_SATISFIED},query=sync}else env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(query=this.beginQuery(),this.endQuery(),isFencePassed=()=>this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):isFencePassed=()=>!0;return{query,isFencePassed}}downloadMatrixFromPackedTexture(texture,physicalRows,physicalCols){return this.downloadMatrixDriver(texture,()=>downloadMatrixFromPackedOutputTexture(this.gl,physicalRows,physicalCols))}createProgram(fragmentShaderSource){this.throwIfDisposed();let gl=this.gl,fragmentShader=createFragmentShader(gl,fragmentShaderSource),vertexShader=createVertexShader2(gl),program=createProgram(gl);return callAndCheck(gl,()=>gl.attachShader(program,vertexShader)),callAndCheck(gl,()=>gl.attachShader(program,fragmentShader)),linkProgram(gl,program),this.debug&&validateProgram(gl,program),this.vertexAttrsAreBound||(this.setProgram(program),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(gl,this.program,this.vertexBuffer)),program}deleteProgram(program){this.throwIfDisposed(),program===this.program&&(this.program=null),program!=null&&callAndCheck(this.gl,()=>this.gl.deleteProgram(program))}setProgram(program){this.throwIfDisposed(),this.program=program,this.program!=null&&this.debug&&validateProgram(this.gl,this.program),callAndCheck(this.gl,()=>this.gl.useProgram(program))}getUniformLocation(program,uniformName,shouldThrow=!0){return this.throwIfDisposed(),shouldThrow?getProgramUniformLocationOrThrow(this.gl,program,uniformName):getProgramUniformLocation(this.gl,program,uniformName)}getAttributeLocation(program,attribute){return this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.getAttribLocation(program,attribute))}getUniformLocationNoThrow(program,uniformName){return this.throwIfDisposed(),this.gl.getUniformLocation(program,uniformName)}setInputMatrixTexture(inputMatrixTexture,uniformLocation,textureUnit){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,inputMatrixTexture,uniformLocation,textureUnit)}setOutputMatrixTexture(outputMatrixTexture,rows,columns){this.setOutputMatrixTextureDriver(outputMatrixTexture,columns,rows)}setOutputPackedMatrixTexture(outputPackedMatrixTexture,rows,columns){this.throwIfDisposed();let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);this.setOutputMatrixTextureDriver(outputPackedMatrixTexture,width,height)}setOutputMatrixWriteRegion(startRow,numRows,startColumn,numColumns){this.setOutputMatrixWriteRegionDriver(startColumn,startRow,numColumns,numRows)}setOutputPackedMatrixWriteRegion(startRow,numRows,startColumn,numColumns){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let gl=this.gl;this.debug&&this.debugValidate(),callAndCheck(gl,()=>gl.drawElements(gl.TRIANGLES,6,gl.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2(),query2=gl2.createQuery();return gl2.beginQuery(ext2.TIME_ELAPSED_EXT,query2),query2}let ext=this.getQueryTimerExtensionWebGL1(),query=ext.createQueryEXT();return ext.beginQueryEXT(ext.TIME_ELAPSED_EXT,query),query}endQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2();gl2.endQuery(ext2.TIME_ELAPSED_EXT);return}let ext=this.getQueryTimerExtensionWebGL1();ext.endQueryEXT(ext.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(query){return await util_exports.repeatedTry(()=>this.disposed||this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(query,queryTimerVersion){if(queryTimerVersion===0)return null;if(queryTimerVersion===2){let gl2=this.gl;return gl2.getQueryParameter(query,gl2.QUERY_RESULT)/1e6}else{let ext=this.getQueryTimerExtensionWebGL1();return ext.getQueryObjectEXT(query,ext.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(query,queryTimerVersion){if(queryTimerVersion===0)return!0;if(queryTimerVersion===2){let gl2=this.gl,ext=this.getQueryTimerExtensionWebGL2(),available=gl2.getQueryParameter(query,gl2.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}else{let ext=this.getQueryTimerExtensionWebGL1(),available=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}}pollFence(fenceContext){return new Promise(resolve=>{this.addItemToPoll(()=>fenceContext.isFencePassed(),()=>resolve())})}pollItems(){let index=linearSearchLastTrue(this.itemsToPoll.map(x=>x.isDoneFn));for(let i=0;i<=index;++i){let{resolveFn}=this.itemsToPoll[i];resolveFn()}this.itemsToPoll=this.itemsToPoll.slice(index+1)}addItemToPoll(isDoneFn,resolveFn){this.itemsToPoll.push({isDoneFn,resolveFn}),!(this.itemsToPoll.length>1)&&util_exports.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(texture){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,texture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(texture,downloadAndDecode){this.bindTextureToFrameBuffer(texture);let result=downloadAndDecode();return this.unbindTextureToFrameBuffer(),result}setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked,width,height){this.throwIfDisposed();let gl=this.gl;bindColorTextureToFramebuffer(gl,outputMatrixTextureMaybePacked,this.framebuffer),this.debug&&validateFramebuffer(gl),this.outputTexture=outputMatrixTextureMaybePacked,callAndCheck(gl,()=>gl.viewport(0,0,width,height)),callAndCheck(gl,()=>gl.scissor(0,0,width,height))}setOutputMatrixWriteRegionDriver(x,y,width,height){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.scissor(x,y,width,height))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function linearSearchLastTrue(arr){let i=0;for(;i<arr.length&&arr[i]();++i);return i-1}var{getBroadcastDims:getBroadcastDims2}=backend_util_exports;function makeShader(inputsInfo,outputShape,userCode,usesPackedTextures){let prefixSnippets=[];inputsInfo.forEach(x=>{let size=util_exports.sizeFromShape(x.shapeInfo.logicalShape);x.shapeInfo.isUniform?prefixSnippets.push(`uniform float ${x.name}${size>1?`[${size}]`:""};`):(prefixSnippets.push(`uniform sampler2D ${x.name};`),prefixSnippets.push(`uniform int offset${x.name};`))});let inputPrefixSnippet=prefixSnippets.join(`
 `),inputSamplingSnippet=inputsInfo.map(x=>getInputSamplingSnippet(x,outputShape,usesPackedTextures)).join(`
 `),outTexShape=outputShape.texShape,glsl=getGlslDifferences(),floatTextureSampleSnippet=getFloatTextureSampleSnippet(glsl),outputSamplingSnippet,floatTextureSetOutputSnippet,shaderPrefix=getShaderPrefix(glsl);return outputShape.isPacked?(outputSamplingSnippet=getPackedOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRGBASnippet(glsl)):(outputSamplingSnippet=getOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRSnippet(glsl)),usesPackedTextures&&(shaderPrefix+=SHADER_PACKED_PREFIX),[shaderPrefix,floatTextureSampleSnippet,floatTextureSetOutputSnippet,inputPrefixSnippet,outputSamplingSnippet,inputSamplingSnippet,userCode].join(`
 `)}function getSamplerFromInInfo(inInfo){let shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getSamplerScalar(inInfo);case 1:return getSampler1D(inInfo);case 2:return getSampler2D(inInfo);case 3:return getSampler3D(inInfo);case 4:return getSampler4D(inInfo);case 5:return getSampler5D(inInfo);case 6:return getSampler6D(inInfo);default:throw new Error(`${shape.length}-D input sampling is not yet supported`)}}function getPackedSamplerFromInInfo(inInfo){switch(inInfo.shapeInfo.logicalShape.length){case 0:return getPackedSamplerScalar(inInfo);case 1:return getPackedSampler1D(inInfo);case 2:return getPackedSampler2D(inInfo);case 3:return getPackedSampler3D(inInfo);default:return getPackedSamplerND(inInfo)}}function getInputSamplingSnippet(inInfo,outShapeInfo,usesPackedTextures=!1){let res="";usesPackedTextures?res+=getPackedSamplerFromInInfo(inInfo):res+=getSamplerFromInInfo(inInfo);let inShape=inInfo.shapeInfo.logicalShape,outShape=outShapeInfo.logicalShape;return inShape.length<=outShape.length&&(usesPackedTextures?res+=getPackedSamplerAtOutputCoords(inInfo,outShapeInfo):res+=getSamplerAtOutputCoords(inInfo,outShapeInfo)),res}function getPackedOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(outShape,outTexShape);case 2:return getOutputPacked2DCoords(outShape,outTexShape);case 3:return getOutputPacked3DCoords(outShape,outTexShape);default:return getOutputPackedNDCoords(outShape,outTexShape)}}function getOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(outShape,outTexShape);case 2:return getOutput2DCoords(outShape,outTexShape);case 3:return getOutput3DCoords(outShape,outTexShape);case 4:return getOutput4DCoords(outShape,outTexShape);case 5:return getOutput5DCoords(outShape,outTexShape);case 6:return getOutput6DCoords(outShape,outTexShape);default:throw new Error(`${outShape.length}-D output sampling is not yet supported`)}}function getFloatTextureSampleSnippet(glsl){return`
@@ -827,7 +827,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${coordsSnippet}
       return get${texFuncSnippet}(${unpackedCoordsSnippet});
     }
-  `}function getCoordsDataType(rank){if(rank<=1)return"int";if(rank===2)return"ivec2";if(rank===3)return"ivec3";if(rank===4)return"ivec4";if(rank===5)return"ivec5";if(rank===6)return"ivec6";throw Error(`GPU for rank ${rank} is not yet supported`)}function squeezeInputInfo(inInfo,squeezedShape){let newInputInfo=JSON.parse(JSON.stringify(inInfo));return newInputInfo.shapeInfo.logicalShape=squeezedShape,newInputInfo}function getSqueezedParams(params,keptDims){return keptDims.map(d=>params[d]).join(", ")}function compileProgram(gpgpu,program,inputs,output){let userCode=program.userCode,inputInfos=inputs.map((input2,i)=>{let shapeInfo={logicalShape:input2.shape,texShape:input2.isUniform?null:input2.texData.texShape,isUniform:input2.isUniform,isPacked:input2.isUniform?!1:input2.texData.isPacked,flatOffset:null};return input2.texData!=null&&input2.texData.slice!=null&&input2.texData.slice.flatOffset>0&&(shapeInfo.flatOffset=input2.texData.slice.flatOffset),{name:program.variableNames[i],shapeInfo}}),inShapeInfos=inputInfos.map(x=>x.shapeInfo),outShapeInfo={logicalShape:output.shape,texShape:output.texData.texShape,isUniform:!1,isPacked:output.texData.isPacked,flatOffset:null},source=makeShader(inputInfos,outShapeInfo,userCode,program.packedInputs),webGLProgram=gpgpu.createProgram(source),infLoc=null,nanLoc=gpgpu.getUniformLocation(webGLProgram,"NAN",!1);env().getNumber("WEBGL_VERSION")===1&&(infLoc=gpgpu.getUniformLocation(webGLProgram,"INFINITY",!1));let uniformLocations={};for(let i=0;i<program.variableNames.length;i++){let varName=program.variableNames[i],shouldThrow=!1;uniformLocations[varName]=gpgpu.getUniformLocation(webGLProgram,varName,shouldThrow),uniformLocations[`offset${varName}`]=gpgpu.getUniformLocation(webGLProgram,`offset${varName}`,shouldThrow)}return{program,source,webGLProgram,uniformLocations,inShapeInfos,outShapeInfo,infLoc,nanLoc}}function validateBinaryAndProgram(shapeInfos,inputs){if(shapeInfos.length!==inputs.length)throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);shapeInfos.forEach((s,i)=>{let shapeA=s.logicalShape,input2=inputs[i],shapeB=input2.shape;if(!util_exports.arraysEqual(shapeA,shapeB))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);if(!(s.isUniform&&input2.isUniform)){let texShapeA=s.texShape,texShapeB=input2.isUniform?null:input2.texData.texShape;if(!util_exports.arraysEqual(texShapeA,texShapeB))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`)}})}function runProgram(gpgpu,binary,inputs,output,customSetup){validateBinaryAndProgram(binary.inShapeInfos,inputs),validateBinaryAndProgram([binary.outShapeInfo],[output]);let outTex=output.texData.texture,outTexShape=output.texData.texShape;output.texData.isPacked?gpgpu.setOutputPackedMatrixTexture(outTex,outTexShape[0],outTexShape[1]):gpgpu.setOutputMatrixTexture(outTex,outTexShape[0],outTexShape[1]),gpgpu.setProgram(binary.webGLProgram),env().getNumber("WEBGL_VERSION")===1&&binary.infLoc!==null&&gpgpu.gl.uniform1f(binary.infLoc,Infinity),binary.nanLoc!==null&&gpgpu.gl.uniform1f(binary.nanLoc,NaN),inputs.forEach((input2,i)=>{let varName=binary.program.variableNames[i],varLoc=binary.uniformLocations[varName],varOffsetLoc=binary.uniformLocations[`offset${varName}`];if(varLoc!=null){if(input2.isUniform){if(util_exports.sizeFromShape(input2.shape)<2)gpgpu.gl.uniform1f(varLoc,input2.uniformValues[0]);else{let vals=input2.uniformValues;vals instanceof Float32Array||(vals=new Float32Array(vals)),gpgpu.gl.uniform1fv(varLoc,vals)}return}input2.texData.slice!=null&&varOffsetLoc!=null&&gpgpu.gl.uniform1i(varOffsetLoc,input2.texData.slice.flatOffset),gpgpu.setInputMatrixTexture(input2.texData.texture,varLoc,i)}}),customSetup!=null&&customSetup(gpgpu,binary.webGLProgram),gpgpu.executeProgram()}function makeShaderKey(program,inputs,output){let keyInputs="";inputs.concat(output).forEach(x=>{let hasOffset=x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0,texShape=x.isUniform?"uniform":x.texData.texShape;keyInputs+=`${x.shape}_${texShape}_${hasOffset}`});let keyUserCode=program.userCode,key=program.constructor.name;return key+="_"+keyInputs+"_"+keyUserCode,key}var{addImpl:addImplCPU,bincountImpl:bincountImplCPU,bincountReduceImpl:bincountReduceImplCPU,ceilImpl:ceilImplCPU,concatImpl:concatImplCPU,expImpl:expImplCPU,expm1Impl:expm1ImplCPU,floorImpl:floorImplCPU,gatherV2Impl:gatherV2ImplCPU,greaterImpl:greaterImplCPU,lessImpl:lessImplCPU,linSpaceImpl:linSpaceImplCPU,logImpl:logImplCPU,maxImpl:maxImplCPU,maximumImpl:maximumImplCPU,minimumImpl:minimumImplCPU,multiplyImpl:multiplyImplCPU,negImpl:negImplCPU,prodImpl:prodImplCPU,rangeImpl:rangeImplCPU,rsqrtImpl:rsqrtImplCPU,simpleAbsImpl:simpleAbsImplCPU,sliceImpl:sliceImplCPU,stridedSliceImpl:stridedSliceImplCPU,subImpl:subImplCPU,tileImpl:tileImplCPU,topKImpl:topKImplCPU,transposeImpl:transposeImplCPU,uniqueImpl:uniqueImplCPU}=shared_exports;function getVecChannels(name,rank){return["x","y","z","w","u","v"].slice(0,rank).map(d=>`${name}.${d}`)}function getChannels(name,rank){return rank===1?[name]:getVecChannels(name,rank)}function getSourceCoords(rank,dims){if(rank===1)return"rc";let coords2="";for(let i=0;i<rank;i++)coords2+=dims[i],i<rank-1&&(coords2+=",");return coords2}var PackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=outputShape;let rank=outputShape.length;if(rank===0)this.userCode=`
+  `}function getCoordsDataType(rank){if(rank<=1)return"int";if(rank===2)return"ivec2";if(rank===3)return"ivec3";if(rank===4)return"ivec4";if(rank===5)return"ivec5";if(rank===6)return"ivec6";throw Error(`GPU for rank ${rank} is not yet supported`)}function squeezeInputInfo(inInfo,squeezedShape){let newInputInfo=JSON.parse(JSON.stringify(inInfo));return newInputInfo.shapeInfo.logicalShape=squeezedShape,newInputInfo}function getSqueezedParams(params,keptDims){return keptDims.map(d=>params[d]).join(", ")}function compileProgram(gpgpu,program,inputs,output){let userCode=program.userCode,inputInfos=inputs.map((input2,i)=>{let shapeInfo={logicalShape:input2.shape,texShape:input2.isUniform?null:input2.texData.texShape,isUniform:input2.isUniform,isPacked:input2.isUniform?!1:input2.texData.isPacked,flatOffset:null};return input2.texData!=null&&input2.texData.slice!=null&&input2.texData.slice.flatOffset>0&&(shapeInfo.flatOffset=input2.texData.slice.flatOffset),{name:program.variableNames[i],shapeInfo}}),inShapeInfos=inputInfos.map(x=>x.shapeInfo),outShapeInfo={logicalShape:output.shape,texShape:output.texData.texShape,isUniform:!1,isPacked:output.texData.isPacked,flatOffset:null},source=makeShader(inputInfos,outShapeInfo,userCode,program.packedInputs),webGLProgram=gpgpu.createProgram(source),infLoc=null,nanLoc=gpgpu.getUniformLocation(webGLProgram,"NAN",!1);env().getNumber("WEBGL_VERSION")===1&&(infLoc=gpgpu.getUniformLocation(webGLProgram,"INFINITY",!1));let uniformLocations={};for(let i=0;i<program.variableNames.length;i++){let varName=program.variableNames[i],shouldThrow=!1;uniformLocations[varName]=gpgpu.getUniformLocation(webGLProgram,varName,shouldThrow),uniformLocations[`offset${varName}`]=gpgpu.getUniformLocation(webGLProgram,`offset${varName}`,shouldThrow)}return{program,source,webGLProgram,uniformLocations,inShapeInfos,outShapeInfo,infLoc,nanLoc}}function validateBinaryAndProgram(shapeInfos,inputs){if(shapeInfos.length!==inputs.length)throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);shapeInfos.forEach((s,i)=>{let shapeA=s.logicalShape,input2=inputs[i],shapeB=input2.shape;if(!util_exports.arraysEqual(shapeA,shapeB))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);if(s.isUniform&&input2.isUniform)return;let texShapeA=s.texShape,texShapeB=input2.isUniform?null:input2.texData.texShape;if(!util_exports.arraysEqual(texShapeA,texShapeB))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`)})}function runProgram(gpgpu,binary,inputs,output,customSetup){validateBinaryAndProgram(binary.inShapeInfos,inputs),validateBinaryAndProgram([binary.outShapeInfo],[output]);let outTex=output.texData.texture,outTexShape=output.texData.texShape;output.texData.isPacked?gpgpu.setOutputPackedMatrixTexture(outTex,outTexShape[0],outTexShape[1]):gpgpu.setOutputMatrixTexture(outTex,outTexShape[0],outTexShape[1]),gpgpu.setProgram(binary.webGLProgram),env().getNumber("WEBGL_VERSION")===1&&binary.infLoc!==null&&gpgpu.gl.uniform1f(binary.infLoc,Infinity),binary.nanLoc!==null&&gpgpu.gl.uniform1f(binary.nanLoc,NaN),inputs.forEach((input2,i)=>{let varName=binary.program.variableNames[i],varLoc=binary.uniformLocations[varName],varOffsetLoc=binary.uniformLocations[`offset${varName}`];if(varLoc!=null){if(input2.isUniform){if(util_exports.sizeFromShape(input2.shape)<2)gpgpu.gl.uniform1f(varLoc,input2.uniformValues[0]);else{let vals=input2.uniformValues;vals instanceof Float32Array||(vals=new Float32Array(vals)),gpgpu.gl.uniform1fv(varLoc,vals)}return}input2.texData.slice!=null&&varOffsetLoc!=null&&gpgpu.gl.uniform1i(varOffsetLoc,input2.texData.slice.flatOffset),gpgpu.setInputMatrixTexture(input2.texData.texture,varLoc,i)}}),customSetup!=null&&customSetup(gpgpu,binary.webGLProgram),gpgpu.executeProgram()}function makeShaderKey(program,inputs,output){let keyInputs="";inputs.concat(output).forEach(x=>{let hasOffset=x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0,texShape=x.isUniform?"uniform":x.texData.texShape;keyInputs+=`${x.shape}_${texShape}_${hasOffset}`});let keyUserCode=program.userCode,key=program.constructor.name;return key+="_"+keyInputs+"_"+keyUserCode,key}var{addImpl:addImplCPU,bincountImpl:bincountImplCPU,bincountReduceImpl:bincountReduceImplCPU,ceilImpl:ceilImplCPU,concatImpl:concatImplCPU,expImpl:expImplCPU,expm1Impl:expm1ImplCPU,floorImpl:floorImplCPU,gatherV2Impl:gatherV2ImplCPU,greaterImpl:greaterImplCPU,lessImpl:lessImplCPU,linSpaceImpl:linSpaceImplCPU,logImpl:logImplCPU,maxImpl:maxImplCPU,maximumImpl:maximumImplCPU,minimumImpl:minimumImplCPU,multiplyImpl:multiplyImplCPU,negImpl:negImplCPU,prodImpl:prodImplCPU,rangeImpl:rangeImplCPU,rsqrtImpl:rsqrtImplCPU,simpleAbsImpl:simpleAbsImplCPU,sliceImpl:sliceImplCPU,stridedSliceImpl:stridedSliceImplCPU,subImpl:subImplCPU,tileImpl:tileImplCPU,topKImpl:topKImplCPU,transposeImpl:transposeImplCPU,uniqueImpl:uniqueImplCPU}=shared_exports;function getVecChannels(name,rank){return["x","y","z","w","u","v"].slice(0,rank).map(d=>`${name}.${d}`)}function getChannels(name,rank){return rank===1?[name]:getVecChannels(name,rank)}function getSourceCoords(rank,dims){if(rank===1)return"rc";let coords2="";for(let i=0;i<rank;i++)coords2+=dims[i],i<rank-1&&(coords2+=",");return coords2}var PackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=outputShape;let rank=outputShape.length;if(rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
@@ -856,7 +856,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             0, 0`:`getA(${sourceCoords[0]}),
           cEdge ? 0. : getA(${sourceCoords[1]}),
           rEdge ? 0. : getA(${sourceCoords[2]}),
-          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`}var ReshapePackedProgram=class{constructor(outputShape,inputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let mainLoop="";for(let i=0;i<4;i++){let thisRC="thisRC = rc;";i%2===1&&(thisRC+="thisRC.z += 1;"),i>1&&(thisRC+="thisRC.y += 1;"),mainLoop+=`
+          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`}var ReshapePackedProgram=class{constructor(outputShape,inputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let mainLoop="";for(let i=0;i<4;i++){let thisRC="thisRC = rc;";i%2==1&&(thisRC+="thisRC.z += 1;"),i>1&&(thisRC+="thisRC.y += 1;"),mainLoop+=`
         ${thisRC}
         ${i>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -889,7 +889,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape)}
       return ivec3(r, c, d);
     }
-  `}var TextureManager=class{constructor(gpgpu){this.gpgpu=gpgpu,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(shapeRC,usage,isPacked){let physicalTexType=getPhysicalFromLogicalTextureType(usage,isPacked),shapeKey=getKeyFromTextureShape(shapeRC,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]),shapeKey in this.usedTextures||(this.usedTextures[shapeKey]=[]);let texBytes=computeBytes(shapeRC,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked);if(this.freeTextures[shapeKey].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=texBytes,this.log();let newTexture2=this.freeTextures[shapeKey].shift();return this.usedTextures[shapeKey].push(newTexture2),newTexture2}let newTexture;return physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT32?newTexture=this.gpgpu.createPackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT16?newTexture=this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT32?newTexture=this.gpgpu.createFloat32MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT16?newTexture=this.gpgpu.createFloat16MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(newTexture=this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0],shapeRC[1])),this.usedTextures[shapeKey].push(newTexture),this.numUsedTextures++,this._numBytesAllocated+=texBytes,this.log(),newTexture}releaseTexture(texture,shape,logicalTexType,isPacked){if(this.freeTextures!=null){let physicalTexType=getPhysicalFromLogicalTextureType(logicalTexType,isPacked),shapeKey=getKeyFromTextureShape(shape,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]);let texBytes=computeBytes(shape,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked),deleteTexThreshold=env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");deleteTexThreshold!==-1&&this._numBytesAllocated>deleteTexThreshold?(this.gpgpu.deleteMatrixTexture(texture),this._numBytesAllocated-=texBytes):(this.freeTextures[shapeKey].push(texture),this.numFreeTextures++,this._numBytesFree+=texBytes),this.numUsedTextures--;let texList=this.usedTextures[shapeKey],texIndex=texList.indexOf(texture);if(texIndex<0)throw new Error("Cannot release a texture that was never provided by this texture manager");texList.splice(texIndex,1),this.log()}}log(){if(this.logEnabled){let total=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${total})`);let freeRatio=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*freeRatio)}%)`)}}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let texShape in this.freeTextures)this.freeTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});for(let texShape in this.usedTextures)this.usedTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function numBytesForInternalFormat(gl,internalFormat){let glany=gl;if(internalFormat===glany.R32F)return 4;if(internalFormat===glany.R16F)return 2;if(internalFormat===glany.RGBA32F)return 16;if(internalFormat===gl.RGBA)return 16;if(internalFormat===glany.RGBA16F)return 8;throw new Error(`Unknown internal format ${internalFormat}`)}function computeBytes(shape,physicalTexType,gl,textureConfig,isPacked){let internalFormat=internalFormatForPhysicalTexType(physicalTexType,textureConfig),numElements;if(isPacked){let[packedWidth,packedHeight]=getPackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=packedWidth*packedHeight}else{let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=width*height}let bytesPerElement2=numBytesForInternalFormat(gl,internalFormat);return numElements*bytesPerElement2}function internalFormatForPhysicalTexType(physicalTexType,textureConfig){switch(physicalTexType){case PhysicalTextureType.PACKED_2X2_FLOAT32:return getInternalFormatForPackedMatrixTexture(textureConfig);case PhysicalTextureType.PACKED_2X2_FLOAT16:return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT32:return getInternalFormatForFloat32MatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT16:return getInternalFormatForFloat16MatrixTexture(textureConfig);case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);default:throw new Error(`Unknown physical texture type ${physicalTexType}`)}}function getPhysicalTextureForRendering(isPacked){return env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?isPacked?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32:isPacked?PhysicalTextureType.PACKED_2X2_FLOAT16:PhysicalTextureType.UNPACKED_FLOAT16}function getPhysicalFromLogicalTextureType(logicalTexType,isPacked){if(logicalTexType===TextureUsage.UPLOAD)return PhysicalTextureType.PACKED_2X2_FLOAT32;if(logicalTexType===TextureUsage.RENDER||logicalTexType==null)return getPhysicalTextureForRendering(isPacked);if(logicalTexType===TextureUsage.DOWNLOAD||logicalTexType===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${logicalTexType}`)}function getKeyFromTextureShape(shapeRowsCol,physicalTexType,isPacked){return`${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`}var UnaryOpProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
+  `}var TextureManager=class{constructor(gpgpu){this.gpgpu=gpgpu,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(shapeRC,usage,isPacked){let physicalTexType=getPhysicalFromLogicalTextureType(usage,isPacked),shapeKey=getKeyFromTextureShape(shapeRC,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]),shapeKey in this.usedTextures||(this.usedTextures[shapeKey]=[]);let texBytes=computeBytes(shapeRC,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked);if(this.freeTextures[shapeKey].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=texBytes,this.log();let newTexture2=this.freeTextures[shapeKey].shift();return this.usedTextures[shapeKey].push(newTexture2),newTexture2}let newTexture;return physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT32?newTexture=this.gpgpu.createPackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT16?newTexture=this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT32?newTexture=this.gpgpu.createFloat32MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT16?newTexture=this.gpgpu.createFloat16MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(newTexture=this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0],shapeRC[1])),this.usedTextures[shapeKey].push(newTexture),this.numUsedTextures++,this._numBytesAllocated+=texBytes,this.log(),newTexture}releaseTexture(texture,shape,logicalTexType,isPacked){if(this.freeTextures==null)return;let physicalTexType=getPhysicalFromLogicalTextureType(logicalTexType,isPacked),shapeKey=getKeyFromTextureShape(shape,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]);let texBytes=computeBytes(shape,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked),deleteTexThreshold=env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");deleteTexThreshold!==-1&&this._numBytesAllocated>deleteTexThreshold?(this.gpgpu.deleteMatrixTexture(texture),this._numBytesAllocated-=texBytes):(this.freeTextures[shapeKey].push(texture),this.numFreeTextures++,this._numBytesFree+=texBytes),this.numUsedTextures--;let texList=this.usedTextures[shapeKey],texIndex=texList.indexOf(texture);if(texIndex<0)throw new Error("Cannot release a texture that was never provided by this texture manager");texList.splice(texIndex,1),this.log()}log(){if(!this.logEnabled)return;let total=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${total})`);let freeRatio=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*freeRatio)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let texShape in this.freeTextures)this.freeTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});for(let texShape in this.usedTextures)this.usedTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function numBytesForInternalFormat(gl,internalFormat){let glany=gl;if(internalFormat===glany.R32F)return 4;if(internalFormat===glany.R16F)return 2;if(internalFormat===glany.RGBA32F||internalFormat===gl.RGBA)return 16;if(internalFormat===glany.RGBA16F)return 8;throw new Error(`Unknown internal format ${internalFormat}`)}function computeBytes(shape,physicalTexType,gl,textureConfig,isPacked){let internalFormat=internalFormatForPhysicalTexType(physicalTexType,textureConfig),numElements;if(isPacked){let[packedWidth,packedHeight]=getPackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=packedWidth*packedHeight}else{let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=width*height}let bytesPerElement2=numBytesForInternalFormat(gl,internalFormat);return numElements*bytesPerElement2}function internalFormatForPhysicalTexType(physicalTexType,textureConfig){switch(physicalTexType){case PhysicalTextureType.PACKED_2X2_FLOAT32:return getInternalFormatForPackedMatrixTexture(textureConfig);case PhysicalTextureType.PACKED_2X2_FLOAT16:return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT32:return getInternalFormatForFloat32MatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT16:return getInternalFormatForFloat16MatrixTexture(textureConfig);case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);default:throw new Error(`Unknown physical texture type ${physicalTexType}`)}}function getPhysicalTextureForRendering(isPacked){return env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?isPacked?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32:isPacked?PhysicalTextureType.PACKED_2X2_FLOAT16:PhysicalTextureType.UNPACKED_FLOAT16}function getPhysicalFromLogicalTextureType(logicalTexType,isPacked){if(logicalTexType===TextureUsage.UPLOAD)return PhysicalTextureType.PACKED_2X2_FLOAT32;if(logicalTexType===TextureUsage.RENDER||logicalTexType==null)return getPhysicalTextureForRendering(isPacked);if(logicalTexType===TextureUsage.DOWNLOAD||logicalTexType===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${logicalTexType}`)}function getKeyFromTextureShape(shapeRowsCol,physicalTexType,isPacked){return`${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`}var UnaryOpProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
       float unaryOperation(float x) {
         ${opSnippet}
       }
@@ -951,7 +951,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(getChannel(packedInput, ${coords2}));
       }
-    `}},whereImpl3=kernel_impls_exports.whereImpl,EPSILON_FLOAT322=1e-7,EPSILON_FLOAT162=1e-4,binaryCaches={};function getBinaryCache(webGLVersion){return webGLVersion in binaryCaches||(binaryCaches[webGLVersion]={}),binaryCaches[webGLVersion]}var CPU_HANDOFF_SIZE_THRESHOLD=128,BEFORE_PAGING_CONSTANT=600;function numMBBeforeWarning(){return env().global.screen==null?1024:env().global.screen.height*env().global.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024/1024}var MathBackendWebGL=class extends KernelBackend{constructor(gpgpu){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(gpgpu==null){let gl=getWebGLContext(env().getNumber("WEBGL_VERSION"));this.binaryCache=getBinaryCache(env().getNumber("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(gl),this.canvas=gl.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=gpgpu,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=gpgpu.gl.canvas;this.textureManager=new TextureManager(this.gpgpu),this.numMBBeforeWarning=numMBBeforeWarning(),this.texData=new DataStorage(this,engine())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(values,shape,dtype){if((env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||env().getBool("DEBUG"))&&this.checkNumericalProblems(values),dtype==="complex64"&&values!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let dataId={};return this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0}),dataId}incRef(dataId){let texData=this.texData.get(dataId);texData.refCount++}decRef(dataId){if(this.texData.has(dataId)){let texData=this.texData.get(dataId);texData.refCount--}}move(dataId,values,shape,dtype){if(env().getBool("DEBUG")&&this.checkNumericalProblems(values),dtype==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.texData.has(dataId)){let textureData=this.texData.get(dataId);textureData.refCount--,textureData.refCount<1&&this.disposeData(dataId)}}readSync(dataId){let texData=this.texData.get(dataId),{values,dtype,complexTensorInfos,slice:slice5,shape,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.readSync(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(dtype==="string")return values;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let result;if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);result=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else result=this.getValuesFromTexture(dataId);return shouldTimeProgram&&(this.downloadWaitMs+=util_exports.now()-start),this.convertAndCacheOnCPU(dataId,result)}async read(dataId){if(this.pendingRead.has(dataId)){let subscribers2=this.pendingRead.get(dataId);return new Promise(resolve=>subscribers2.push(resolve))}let texData=this.texData.get(dataId),{values,shape,slice:slice5,dtype,complexTensorInfos,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.read(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let buffer2=null,tmpDownloadTarget;if(dtype!=="complex64"&&env().get("WEBGL_BUFFER_SUPPORTED")){tmpDownloadTarget=this.decode(dataId);let tmpData=this.texData.get(tmpDownloadTarget.dataId);buffer2=this.gpgpu.createBufferFromTexture(tmpData.texture,...getDenseTexShape(shape))}this.pendingRead.set(dataId,[]),dtype!=="complex64"&&await this.gpgpu.createAndWaitForFence();let vals;if(dtype==="complex64"){let ps=await Promise.all([this.read(complexTensorInfos.real.dataId),this.read(complexTensorInfos.imag.dataId)]),realValues=ps[0],imagValues=ps[1];vals=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else if(buffer2==null)vals=this.getValuesFromTexture(dataId);else{let size=util_exports.sizeFromShape(shape);vals=this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2,size)}tmpDownloadTarget!=null&&this.disposeIntermediateTensorInfo(tmpDownloadTarget);let dTypeVals=this.convertAndCacheOnCPU(dataId,vals),subscribers=this.pendingRead.get(dataId);return this.pendingRead.delete(dataId),subscribers.forEach(resolve=>resolve(dTypeVals)),this.pendingDisposal.has(dataId)&&(this.pendingDisposal.delete(dataId),this.disposeData(dataId),this.pendingDeletes--),dTypeVals}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}checkNumericalProblems(values){if(values!=null)for(let i=0;i<values.length;i++){let num=values[i];if(!canBeRepresented(num))throw env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${num} cannot be represented on this device.`)}}getValuesFromTexture(dataId){let{shape,dtype,isPacked}=this.texData.get(dataId),size=util_exports.sizeFromShape(shape);if(env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let tmpTarget=this.decode(dataId),tmpData2=this.texData.get(tmpTarget.dataId),vals2=this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture,...getDenseTexShape(shape)).subarray(0,size);return this.disposeIntermediateTensorInfo(tmpTarget),vals2}let shouldUsePackedProgram=env().getBool("WEBGL_PACK")&&isPacked===!0,outputShape=shouldUsePackedProgram?getShapeAs3D(shape):shape,program=shouldUsePackedProgram?new EncodeFloatPackedProgram(outputShape):new EncodeFloatProgram(outputShape),output=this.runWebGLProgram(program,[{shape:outputShape,dtype,dataId}],"float32"),tmpData=this.texData.get(output.dataId),vals=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture,tmpData.texShape[0],tmpData.texShape[1]).subarray(0,size);return this.disposeIntermediateTensorInfo(output),vals}async time(f){let oldActiveTimers=this.activeTimers,newActiveTimers=[],outerMostTime=!1;this.programTimersStack==null?(this.programTimersStack=newActiveTimers,outerMostTime=!0):this.activeTimers.push(newActiveTimers),this.activeTimers=newActiveTimers,f();let flattenedActiveTimerQueries=util_exports.flatten(this.activeTimers.map(d=>d.query)).filter(d=>d!=null),flattenedActiveTimerNames=util_exports.flatten(this.activeTimers.map(d=>d.name)).filter(d=>d!=null);this.activeTimers=oldActiveTimers,outerMostTime&&(this.programTimersStack=null);let res={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let kernelMs=await Promise.all(flattenedActiveTimerQueries);res.kernelMs=util_exports.sum(kernelMs),res.getExtraProfileInfo=()=>kernelMs.map((d,i)=>({name:flattenedActiveTimerNames[i],ms:d})).map(d=>`${d.name}: ${d.ms}`).join(", ")}else res.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,res}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:util_exports.now(),endMs:null}}endTimer(query){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),query):(query.endMs=util_exports.now(),query)}async getQueryTime(query){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(query);let timerQuery=query;return timerQuery.endMs-timerQuery.startMs}disposeData(dataId){if(!this.pendingDisposal.has(dataId)){if(this.pendingRead.has(dataId)){this.pendingDisposal.add(dataId),this.pendingDeletes++;return}if(this.texData.has(dataId)){if(this.texData.get(dataId).complexParentRefCount>0){this.texData.get(dataId).refCount--;return}this.releaseGPUData(dataId);let{complexTensorInfos}=this.texData.get(dataId);complexTensorInfos!=null&&(this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.real),this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.imag)),this.texData.delete(dataId)}}}releaseGPUData(dataId){let{texture,dtype,texShape,usage,isPacked,slice:slice5}=this.texData.get(dataId),key=slice5&&slice5.origDataId||dataId,refCount=this.dataRefCount.get(key);refCount>1?this.dataRefCount.set(key,refCount-1):(this.dataRefCount.delete(key),texture!=null&&(this.numBytesInGPU-=this.computeBytes(texShape,dtype),this.textureManager.releaseTexture(texture,texShape,usage,isPacked)));let texData=this.texData.get(dataId);texData.texture=null,texData.texShape=null,texData.isPacked=!1,texData.slice=null}getTexture(dataId){return this.uploadToGPU(dataId),this.texData.get(dataId).texture}getDataInfo(dataId){return this.texData.get(dataId)}getCPUBackend(){return env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=engine().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(inputs,sizeThreshold=CPU_HANDOFF_SIZE_THRESHOLD){let cpuBackend=this.getCPUBackend();return!env().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&cpuBackend==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),cpuBackend!=null&&inputs.every(input2=>this.texData.get(input2.dataId).texture==null&&util_exports.sizeFromShape(input2.shape)<sizeThreshold)}getGPGPUContext(){return this.gpgpu}where(condition){backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let condVals=condition.dataSync();return whereImpl3(condition.shape,condVals)}packedUnaryOp(x,op2,dtype){let program=new UnaryOpPackedProgram(x.shape,op2);return this.compileAndRun(program,[x],dtype)}abs(x){if(this.shouldExecuteOnCPU([x])&&x.dtype!=="complex64"){let outValues=simpleAbsImplCPU(this.texData.get(x.dataId).values);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ABS,x.dtype);let program=new UnaryOpProgram(x.shape,ABS);return this.compileAndRun(program,[x])}makeTensorInfo(shape,dtype,values){let dataId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));dataId=this.write(encodedValues,shape,dtype)}else dataId=this.write(values,shape,dtype);return this.texData.get(dataId).usage=null,{dataId,shape,dtype}}makeOutput(shape,dtype,values){let{dataId}=this.makeTensorInfo(shape,dtype,values);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}unpackTensor(input2){let program=new UnpackProgram(input2.shape);return this.runWebGLProgram(program,[input2],input2.dtype)}packTensor(input2){let program=new PackProgram(input2.shape),preventEagerUnpackingOutput=!0;return this.runWebGLProgram(program,[input2],input2.dtype,null,preventEagerUnpackingOutput)}packedReshape(input2,afterShape){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=this.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}decode(dataId){let texData=this.texData.get(dataId),{isPacked,shape,dtype}=texData,shapeAs3D=getShapeAs3D(shape),program;isPacked?program=new DecodeMatrixPackedProgram(shapeAs3D):program=new DecodeMatrixProgram(shapeAs3D);let preventEagerUnpackingOfOutput=!0,out=this.runWebGLProgram(program,[{shape:shapeAs3D,dtype,dataId}],dtype,null,preventEagerUnpackingOfOutput);return{dtype,shape,dataId:out.dataId}}runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){let output=this.makeTensorInfo(program.outputShape,outputDtype),outData=this.texData.get(output.dataId);if(program.packedOutput&&(outData.isPacked=!0),program.outPackingScheme===PackingScheme.DENSE){let texelShape=getDenseTexShape(program.outputShape);outData.texShape=texelShape.map(d=>d*2)}if(program.outTexUsage!=null&&(outData.usage=program.outTexUsage),util_exports.sizeFromShape(output.shape)===0)return outData.values=util_exports.getTypedArrayFromDType(output.dtype,0),output;let dataToDispose=[],inputsData=inputs.map(input2=>{if(input2.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let texData=this.texData.get(input2.dataId);if(texData.texture==null){if(!program.packedInputs&&util_exports.sizeFromShape(input2.shape)<=env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:input2.shape,texData:null,isUniform:!0,uniformValues:texData.values};program.packedInputs&&(texData.isPacked=!0,texData.shape=input2.shape)}else if(!!texData.isPacked!==!!program.packedInputs)input2=texData.isPacked?this.unpackTensor(input2):this.packTensor(input2),dataToDispose.push(input2),texData=this.texData.get(input2.dataId);else if(texData.isPacked&&!isReshapeFree(texData.shape,input2.shape)){let savedInput=input2,targetShape=input2.shape;input2.shape=texData.shape,input2=this.packedReshape(input2,targetShape),dataToDispose.push(input2),texData=this.texData.get(input2.dataId),savedInput.shape=targetShape}return this.uploadToGPU(input2.dataId),{shape:input2.shape,texData,isUniform:!1}});this.uploadToGPU(output.dataId);let outputData={shape:output.shape,texData:outData,isUniform:!1},key=makeShaderKey(program,inputsData,outputData),binary=this.getAndSaveBinary(key,()=>compileProgram(this.gpgpu,program,inputsData,outputData)),shouldTimeProgram=this.activeTimers!=null,query;if(shouldTimeProgram&&(query=this.startTimer()),runProgram(this.gpgpu,binary,inputsData,outputData,customSetup),dataToDispose.forEach(info=>this.disposeIntermediateTensorInfo(info)),shouldTimeProgram&&(query=this.endTimer(query),this.activeTimers.push({name:program.constructor.name,query:this.getQueryTime(query)})),!env().getBool("WEBGL_LAZILY_UNPACK")&&outData.isPacked&&preventEagerUnpackingOfOutput===!1){let unpacked=this.unpackTensor(output);return this.disposeIntermediateTensorInfo(output),unpacked}return output}compileAndRun(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){outputDtype=outputDtype||inputs[0].dtype;let outInfo=this.runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput);return engine().makeTensorFromDataId(outInfo.dataId,outInfo.shape,outInfo.dtype)}getAndSaveBinary(key,getBinary){return key in this.binaryCache||(this.binaryCache[key]=getBinary()),this.binaryCache[key]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(env().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(key=>{this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram),delete this.binaryCache[key]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=tidy(()=>{if(!env().get("WEBGL_RENDER_FLOAT32_ENABLED")){let debugFlag=env().getBool("DEBUG");env().set("DEBUG",!1);let underflowCheckValue=this.abs(scalar(1e-8)).dataSync()[0];if(env().set("DEBUG",debugFlag),underflowCheckValue>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT322:EPSILON_FLOAT162}uploadToGPU(dataId){let texData=this.texData.get(dataId),{shape,dtype,values,texture,usage,isPacked}=texData;if(texture==null){let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let texShape=texData.texShape;if(texShape==null&&(texShape=getTextureShapeFromLogicalShape(shape,isPacked),texData.texShape=texShape),values!=null){let shapeAs3D=getShapeAs3D(shape),program,width=texShape[1],height=texShape[0],isByteArray=values instanceof Uint8Array;isPacked?([width,height]=getPackedMatrixTextureShapeWidthHeight(texShape[0],texShape[1]),program=new EncodeMatrixPackedProgram(shapeAs3D,[height,width],isByteArray)):program=new EncodeMatrixProgram(shapeAs3D,[height,width],isByteArray);let tempDenseInputHandle=this.makeTensorInfo([height,width],dtype);isByteArray?this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.PIXELS:this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId),width,height,values);let preventEagerUnpacking=!0,encodedOutputTarget=this.runWebGLProgram(program,[tempDenseInputHandle],dtype,null,preventEagerUnpacking),outputTexData=this.texData.get(encodedOutputTarget.dataId);texData.texture=outputTexData.texture,texData.texShape=outputTexData.texShape,texData.isPacked=outputTexData.isPacked,texData.usage=outputTexData.usage,this.disposeIntermediateTensorInfo(tempDenseInputHandle),this.texData.delete(encodedOutputTarget.dataId),texData.values=null,shouldTimeProgram&&(this.uploadWaitMs+=util_exports.now()-start)}else{let newTexture=this.acquireTexture(texShape,usage,dtype,isPacked);texData.texture=newTexture}}}convertAndCacheOnCPU(dataId,float32Values){let texData=this.texData.get(dataId),{dtype}=texData;return this.releaseGPUData(dataId),float32Values!=null&&(texData.values=float32ToTypedArray(float32Values,dtype)),texData.values}acquireTexture(texShape,texType,dtype,isPacked){if(this.numBytesInGPU+=this.computeBytes(texShape,dtype),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let mb=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(texShape,texType,isPacked)}computeBytes(shape,dtype){return shape[0]*shape[1]*util_exports.bytesPerElement(dtype)}};function float32ToTypedArray(a,dtype){if(dtype==="float32"||dtype==="complex64")return a;if(dtype==="int32"||dtype==="bool"){let result=dtype==="int32"?new Int32Array(a.length):new Uint8Array(a.length);for(let i=0;i<result.length;++i)result[i]=Math.round(a[i]);return result}else throw new Error(`Unknown dtype ${dtype}`)}var version6="2.8.2";device_util_exports.isBrowser()&&registerBackend("webgl",()=>new MathBackendWebGL,2);var CHECK_NAN_SNIPPET2=`
+    `}},whereImpl3=kernel_impls_exports.whereImpl,EPSILON_FLOAT322=1e-7,EPSILON_FLOAT162=1e-4,binaryCaches={};function getBinaryCache(webGLVersion){return webGLVersion in binaryCaches||(binaryCaches[webGLVersion]={}),binaryCaches[webGLVersion]}var CPU_HANDOFF_SIZE_THRESHOLD=128,BEFORE_PAGING_CONSTANT=600;function numMBBeforeWarning(){return env().global.screen==null?1024:env().global.screen.height*env().global.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024/1024}var MathBackendWebGL=class extends KernelBackend{constructor(gpgpu){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(gpgpu==null){let gl=getWebGLContext(env().getNumber("WEBGL_VERSION"));this.binaryCache=getBinaryCache(env().getNumber("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(gl),this.canvas=gl.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=gpgpu,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=gpgpu.gl.canvas;this.textureManager=new TextureManager(this.gpgpu),this.numMBBeforeWarning=numMBBeforeWarning(),this.texData=new DataStorage(this,engine())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(values,shape,dtype){if((env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||env().getBool("DEBUG"))&&this.checkNumericalProblems(values),dtype==="complex64"&&values!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let dataId={};return this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0}),dataId}incRef(dataId){let texData=this.texData.get(dataId);texData.refCount++}decRef(dataId){if(this.texData.has(dataId)){let texData=this.texData.get(dataId);texData.refCount--}}move(dataId,values,shape,dtype){if(env().getBool("DEBUG")&&this.checkNumericalProblems(values),dtype==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.texData.has(dataId)){let textureData=this.texData.get(dataId);textureData.refCount--,textureData.refCount<1&&this.disposeData(dataId)}}readSync(dataId){let texData=this.texData.get(dataId),{values,dtype,complexTensorInfos,slice:slice5,shape,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.readSync(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(dtype==="string")return values;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let result;if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);result=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else result=this.getValuesFromTexture(dataId);return shouldTimeProgram&&(this.downloadWaitMs+=util_exports.now()-start),this.convertAndCacheOnCPU(dataId,result)}async read(dataId){if(this.pendingRead.has(dataId)){let subscribers2=this.pendingRead.get(dataId);return new Promise(resolve=>subscribers2.push(resolve))}let texData=this.texData.get(dataId),{values,shape,slice:slice5,dtype,complexTensorInfos,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.read(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let buffer2=null,tmpDownloadTarget;if(dtype!=="complex64"&&env().get("WEBGL_BUFFER_SUPPORTED")){tmpDownloadTarget=this.decode(dataId);let tmpData=this.texData.get(tmpDownloadTarget.dataId);buffer2=this.gpgpu.createBufferFromTexture(tmpData.texture,...getDenseTexShape(shape))}this.pendingRead.set(dataId,[]),dtype!=="complex64"&&await this.gpgpu.createAndWaitForFence();let vals;if(dtype==="complex64"){let ps=await Promise.all([this.read(complexTensorInfos.real.dataId),this.read(complexTensorInfos.imag.dataId)]),realValues=ps[0],imagValues=ps[1];vals=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else if(buffer2==null)vals=this.getValuesFromTexture(dataId);else{let size=util_exports.sizeFromShape(shape);vals=this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2,size)}tmpDownloadTarget!=null&&this.disposeIntermediateTensorInfo(tmpDownloadTarget);let dTypeVals=this.convertAndCacheOnCPU(dataId,vals),subscribers=this.pendingRead.get(dataId);return this.pendingRead.delete(dataId),subscribers.forEach(resolve=>resolve(dTypeVals)),this.pendingDisposal.has(dataId)&&(this.pendingDisposal.delete(dataId),this.disposeData(dataId),this.pendingDeletes--),dTypeVals}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}checkNumericalProblems(values){if(values!=null)for(let i=0;i<values.length;i++){let num=values[i];if(!canBeRepresented(num))throw env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${num} cannot be represented on this device.`)}}getValuesFromTexture(dataId){let{shape,dtype,isPacked}=this.texData.get(dataId),size=util_exports.sizeFromShape(shape);if(env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let tmpTarget=this.decode(dataId),tmpData2=this.texData.get(tmpTarget.dataId),vals2=this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture,...getDenseTexShape(shape)).subarray(0,size);return this.disposeIntermediateTensorInfo(tmpTarget),vals2}let shouldUsePackedProgram=env().getBool("WEBGL_PACK")&&isPacked===!0,outputShape=shouldUsePackedProgram?getShapeAs3D(shape):shape,program=shouldUsePackedProgram?new EncodeFloatPackedProgram(outputShape):new EncodeFloatProgram(outputShape),output=this.runWebGLProgram(program,[{shape:outputShape,dtype,dataId}],"float32"),tmpData=this.texData.get(output.dataId),vals=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture,tmpData.texShape[0],tmpData.texShape[1]).subarray(0,size);return this.disposeIntermediateTensorInfo(output),vals}async time(f){let oldActiveTimers=this.activeTimers,newActiveTimers=[],outerMostTime=!1;this.programTimersStack==null?(this.programTimersStack=newActiveTimers,outerMostTime=!0):this.activeTimers.push(newActiveTimers),this.activeTimers=newActiveTimers,f();let flattenedActiveTimerQueries=util_exports.flatten(this.activeTimers.map(d=>d.query)).filter(d=>d!=null),flattenedActiveTimerNames=util_exports.flatten(this.activeTimers.map(d=>d.name)).filter(d=>d!=null);this.activeTimers=oldActiveTimers,outerMostTime&&(this.programTimersStack=null);let res={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let kernelMs=await Promise.all(flattenedActiveTimerQueries);res.kernelMs=util_exports.sum(kernelMs),res.getExtraProfileInfo=()=>kernelMs.map((d,i)=>({name:flattenedActiveTimerNames[i],ms:d})).map(d=>`${d.name}: ${d.ms}`).join(", ")}else res.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,res}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:util_exports.now(),endMs:null}}endTimer(query){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),query):(query.endMs=util_exports.now(),query)}async getQueryTime(query){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(query);let timerQuery=query;return timerQuery.endMs-timerQuery.startMs}disposeData(dataId){if(this.pendingDisposal.has(dataId))return;if(this.pendingRead.has(dataId)){this.pendingDisposal.add(dataId),this.pendingDeletes++;return}if(!this.texData.has(dataId))return;if(this.texData.get(dataId).complexParentRefCount>0){this.texData.get(dataId).refCount--;return}this.releaseGPUData(dataId);let{complexTensorInfos}=this.texData.get(dataId);complexTensorInfos!=null&&(this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.real),this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.imag)),this.texData.delete(dataId)}releaseGPUData(dataId){let{texture,dtype,texShape,usage,isPacked,slice:slice5}=this.texData.get(dataId),key=slice5&&slice5.origDataId||dataId,refCount=this.dataRefCount.get(key);refCount>1?this.dataRefCount.set(key,refCount-1):(this.dataRefCount.delete(key),texture!=null&&(this.numBytesInGPU-=this.computeBytes(texShape,dtype),this.textureManager.releaseTexture(texture,texShape,usage,isPacked)));let texData=this.texData.get(dataId);texData.texture=null,texData.texShape=null,texData.isPacked=!1,texData.slice=null}getTexture(dataId){return this.uploadToGPU(dataId),this.texData.get(dataId).texture}getDataInfo(dataId){return this.texData.get(dataId)}getCPUBackend(){return env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=engine().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(inputs,sizeThreshold=CPU_HANDOFF_SIZE_THRESHOLD){let cpuBackend=this.getCPUBackend();return!env().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&cpuBackend==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),cpuBackend!=null&&inputs.every(input2=>this.texData.get(input2.dataId).texture==null&&util_exports.sizeFromShape(input2.shape)<sizeThreshold)}getGPGPUContext(){return this.gpgpu}where(condition){backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let condVals=condition.dataSync();return whereImpl3(condition.shape,condVals)}packedUnaryOp(x,op2,dtype){let program=new UnaryOpPackedProgram(x.shape,op2);return this.compileAndRun(program,[x],dtype)}abs(x){if(this.shouldExecuteOnCPU([x])&&x.dtype!=="complex64"){let outValues=simpleAbsImplCPU(this.texData.get(x.dataId).values);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ABS,x.dtype);let program=new UnaryOpProgram(x.shape,ABS);return this.compileAndRun(program,[x])}makeTensorInfo(shape,dtype,values){let dataId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));dataId=this.write(encodedValues,shape,dtype)}else dataId=this.write(values,shape,dtype);return this.texData.get(dataId).usage=null,{dataId,shape,dtype}}makeOutput(shape,dtype,values){let{dataId}=this.makeTensorInfo(shape,dtype,values);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}unpackTensor(input2){let program=new UnpackProgram(input2.shape);return this.runWebGLProgram(program,[input2],input2.dtype)}packTensor(input2){let program=new PackProgram(input2.shape),preventEagerUnpackingOutput=!0;return this.runWebGLProgram(program,[input2],input2.dtype,null,preventEagerUnpackingOutput)}packedReshape(input2,afterShape){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=this.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}decode(dataId){let texData=this.texData.get(dataId),{isPacked,shape,dtype}=texData,shapeAs3D=getShapeAs3D(shape),program;isPacked?program=new DecodeMatrixPackedProgram(shapeAs3D):program=new DecodeMatrixProgram(shapeAs3D);let preventEagerUnpackingOfOutput=!0,out=this.runWebGLProgram(program,[{shape:shapeAs3D,dtype,dataId}],dtype,null,preventEagerUnpackingOfOutput);return{dtype,shape,dataId:out.dataId}}runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){let output=this.makeTensorInfo(program.outputShape,outputDtype),outData=this.texData.get(output.dataId);if(program.packedOutput&&(outData.isPacked=!0),program.outPackingScheme===PackingScheme.DENSE){let texelShape=getDenseTexShape(program.outputShape);outData.texShape=texelShape.map(d=>d*2)}if(program.outTexUsage!=null&&(outData.usage=program.outTexUsage),util_exports.sizeFromShape(output.shape)===0)return outData.values=util_exports.getTypedArrayFromDType(output.dtype,0),output;let dataToDispose=[],inputsData=inputs.map(input2=>{if(input2.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let texData=this.texData.get(input2.dataId);if(texData.texture==null){if(!program.packedInputs&&util_exports.sizeFromShape(input2.shape)<=env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:input2.shape,texData:null,isUniform:!0,uniformValues:texData.values};program.packedInputs&&(texData.isPacked=!0,texData.shape=input2.shape)}else if(!!texData.isPacked!=!!program.packedInputs)input2=texData.isPacked?this.unpackTensor(input2):this.packTensor(input2),dataToDispose.push(input2),texData=this.texData.get(input2.dataId);else if(texData.isPacked&&!isReshapeFree(texData.shape,input2.shape)){let savedInput=input2,targetShape=input2.shape;input2.shape=texData.shape,input2=this.packedReshape(input2,targetShape),dataToDispose.push(input2),texData=this.texData.get(input2.dataId),savedInput.shape=targetShape}return this.uploadToGPU(input2.dataId),{shape:input2.shape,texData,isUniform:!1}});this.uploadToGPU(output.dataId);let outputData={shape:output.shape,texData:outData,isUniform:!1},key=makeShaderKey(program,inputsData,outputData),binary=this.getAndSaveBinary(key,()=>compileProgram(this.gpgpu,program,inputsData,outputData)),shouldTimeProgram=this.activeTimers!=null,query;if(shouldTimeProgram&&(query=this.startTimer()),runProgram(this.gpgpu,binary,inputsData,outputData,customSetup),dataToDispose.forEach(info=>this.disposeIntermediateTensorInfo(info)),shouldTimeProgram&&(query=this.endTimer(query),this.activeTimers.push({name:program.constructor.name,query:this.getQueryTime(query)})),!env().getBool("WEBGL_LAZILY_UNPACK")&&outData.isPacked&&preventEagerUnpackingOfOutput===!1){let unpacked=this.unpackTensor(output);return this.disposeIntermediateTensorInfo(output),unpacked}return output}compileAndRun(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){outputDtype=outputDtype||inputs[0].dtype;let outInfo=this.runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput);return engine().makeTensorFromDataId(outInfo.dataId,outInfo.shape,outInfo.dtype)}getAndSaveBinary(key,getBinary){return key in this.binaryCache||(this.binaryCache[key]=getBinary()),this.binaryCache[key]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(env().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(key=>{this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram),delete this.binaryCache[key]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=tidy(()=>{if(!env().get("WEBGL_RENDER_FLOAT32_ENABLED")){let debugFlag=env().getBool("DEBUG");env().set("DEBUG",!1);let underflowCheckValue=this.abs(scalar(1e-8)).dataSync()[0];if(env().set("DEBUG",debugFlag),underflowCheckValue>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT322:EPSILON_FLOAT162}uploadToGPU(dataId){let texData=this.texData.get(dataId),{shape,dtype,values,texture,usage,isPacked}=texData;if(texture!=null)return;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let texShape=texData.texShape;if(texShape==null&&(texShape=getTextureShapeFromLogicalShape(shape,isPacked),texData.texShape=texShape),values!=null){let shapeAs3D=getShapeAs3D(shape),program,width=texShape[1],height=texShape[0],isByteArray=values instanceof Uint8Array;isPacked?([width,height]=getPackedMatrixTextureShapeWidthHeight(texShape[0],texShape[1]),program=new EncodeMatrixPackedProgram(shapeAs3D,[height,width],isByteArray)):program=new EncodeMatrixProgram(shapeAs3D,[height,width],isByteArray);let tempDenseInputHandle=this.makeTensorInfo([height,width],dtype);isByteArray?this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.PIXELS:this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId),width,height,values);let preventEagerUnpacking=!0,encodedOutputTarget=this.runWebGLProgram(program,[tempDenseInputHandle],dtype,null,preventEagerUnpacking),outputTexData=this.texData.get(encodedOutputTarget.dataId);texData.texture=outputTexData.texture,texData.texShape=outputTexData.texShape,texData.isPacked=outputTexData.isPacked,texData.usage=outputTexData.usage,this.disposeIntermediateTensorInfo(tempDenseInputHandle),this.texData.delete(encodedOutputTarget.dataId),texData.values=null,shouldTimeProgram&&(this.uploadWaitMs+=util_exports.now()-start)}else{let newTexture=this.acquireTexture(texShape,usage,dtype,isPacked);texData.texture=newTexture}}convertAndCacheOnCPU(dataId,float32Values){let texData=this.texData.get(dataId),{dtype}=texData;return this.releaseGPUData(dataId),float32Values!=null&&(texData.values=float32ToTypedArray(float32Values,dtype)),texData.values}acquireTexture(texShape,texType,dtype,isPacked){if(this.numBytesInGPU+=this.computeBytes(texShape,dtype),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let mb=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(texShape,texType,isPacked)}computeBytes(shape,dtype){return shape[0]*shape[1]*util_exports.bytesPerElement(dtype)}};function float32ToTypedArray(a,dtype){if(dtype==="float32"||dtype==="complex64")return a;if(dtype==="int32"||dtype==="bool"){let result=dtype==="int32"?new Int32Array(a.length):new Uint8Array(a.length);for(let i=0;i<result.length;++i)result[i]=Math.round(a[i]);return result}else throw new Error(`Unknown dtype ${dtype}`)}var version6="2.8.3";device_util_exports.isBrowser()&&registerBackend("webgl",()=>new MathBackendWebGL,2);var CHECK_NAN_SNIPPET2=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,BinaryOpProgram=class{constructor(op2,aShape,bShape){this.variableNames=["A","B"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
@@ -1814,7 +1814,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         ${lowerRow}
         setOutput(result);
       }
-    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null)||gpgpu.gl.uniform1iv(this.startLoc,start)}}};function shallowSlice(x,begin,size,backend2){let xTexData=backend2.texData.get(x.dataId),t=backend2.makeTensorInfo(size,x.dtype),newTexData=backend2.texData.get(t.dataId);Object.assign(newTexData,xTexData),newTexData.shape=size,newTexData.dtype=x.dtype;let flatOffset=slice_util_exports.computeFlatOffset(begin,util_exports.computeStrides(x.shape));xTexData.slice&&(flatOffset+=xTexData.slice.flatOffset),newTexData.slice={flatOffset,origDataId:xTexData.slice&&xTexData.slice.origDataId||x.dataId};let refCount=backend2.dataRefCount.get(newTexData.slice.origDataId)||1;return backend2.dataRefCount.set(newTexData.slice.origDataId,refCount+1),t}function slice3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs,[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);if(slice_util_exports.assertParamsValid(x,$begin,$size),util_exports.sizeFromShape($size)===0)return backend2.makeTensorInfo($size,x.dtype,[]);if(backend2.shouldExecuteOnCPU([x])||x.dtype==="string"){let xTexData=backend2.texData.get(x.dataId),outValues=sliceImplCPU(xTexData.values,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outValues)}let{isPacked}=backend2.texData.get(x.dataId),isContinous=slice_util_exports.isSliceContinous(x.shape,$begin,$size);if(isPacked||!isContinous){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram($size):new SliceProgram($size),customSetup=program.getCustomSetupFunc($begin);return backend2.runWebGLProgram(program,[x],x.dtype,customSetup)}return backend2.uploadToGPU(x.dataId),shallowSlice(x,$begin,$size,backend2)}var sliceConfig2={kernelName:Slice,backendName:"webgl",kernelFunc:slice3},batchToSpaceND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;util_exports.assert(x.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),toDispose=[],reshapedIntermediate=reshape4({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),transposedIntermediate=transpose3({inputs:{x:reshapedIntermediate},backend:backend2,attrs:{perm:permuted}}),reshapedIntermediate2=reshape4({inputs:{x:transposedIntermediate},backend:backend2,attrs:{shape:reshapedPermuted}}),sliced=slice3({inputs:{x:reshapedIntermediate2},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return toDispose.push(reshapedIntermediate),toDispose.push(transposedIntermediate),toDispose.push(reshapedIntermediate2),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),sliced},batchToSpaceNDConfig2={kernelName:BatchToSpaceND,backendName:"webgl",kernelFunc:batchToSpaceND3};function bincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.texData.get(x.dataId).values,weightsVals=backend2.texData.get(weights.dataId).values,outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig2={kernelName:Bincount,backendName:"webgl",kernelFunc:bincount3},NOT_EQUAL="return float(a != b);",notEqual3=binaryKernelFunc2({opSnippet:NOT_EQUAL,dtype:"bool"}),notEqualConfig2={kernelName:NotEqual,backendName:"webgl",kernelFunc:notEqual3};function real3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.real},backend:backend2})}var realConfig2={kernelName:Real,backendName:"webgl",kernelFunc:real3},TO_INT="return float(int(x));";function int(input2,backend2){let program=new UnaryOpProgram(input2.shape,TO_INT),output=backend2.runWebGLProgram(program,[input2],"int32");return{dataId:output.dataId,shape:output.shape,dtype:output.dtype}}function cast4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity3({inputs:{x},backend:backend2});let zerosTensor=zeros(x.shape),floatX=cast4({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex3({inputs:{real:floatX,imag:zerosTensor},backend:backend2});return zerosTensor.dispose(),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real3({inputs:{input:x},backend:backend2}),result=cast4({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity3({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32")return int(x,backend2);if(dtype==="bool"){let zerosTensorInfo=backend2.makeTensorInfo([],"bool",util_exports.getTypedArrayFromDType("bool",1)),result=notEqual3({inputs:{a:x,b:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),result}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig2={kernelName:Cast,backendName:"webgl",kernelFunc:cast4},CEIL="return ceil(x);",ceil3=unaryKernelFunc2({opSnippet:CEIL,packedOpSnippet:CEIL,cpuKernelImpl:ceilImplCPU}),ceilConfig2={kernelName:Ceil,backendName:"webgl",kernelFunc:ceil3},ClipProgram=class{constructor(aShape){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
+    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null)||gpgpu.gl.uniform1iv(this.startLoc,start)}}};function shallowSlice(x,begin,size,backend2){let xTexData=backend2.texData.get(x.dataId),t=backend2.makeTensorInfo(size,x.dtype),newTexData=backend2.texData.get(t.dataId);Object.assign(newTexData,xTexData),newTexData.shape=size,newTexData.dtype=x.dtype;let flatOffset=slice_util_exports.computeFlatOffset(begin,util_exports.computeStrides(x.shape));xTexData.slice&&(flatOffset+=xTexData.slice.flatOffset),newTexData.slice={flatOffset,origDataId:xTexData.slice&&xTexData.slice.origDataId||x.dataId};let refCount=backend2.dataRefCount.get(newTexData.slice.origDataId)||1;return backend2.dataRefCount.set(newTexData.slice.origDataId,refCount+1),t}function slice3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs,[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);if(slice_util_exports.assertParamsValid(x,$begin,$size),util_exports.sizeFromShape($size)===0)return backend2.makeTensorInfo($size,x.dtype,[]);if(backend2.shouldExecuteOnCPU([x])||x.dtype==="string"){let xTexData=backend2.texData.get(x.dataId),outValues=sliceImplCPU(xTexData.values,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outValues)}let{isPacked}=backend2.texData.get(x.dataId),isContinous=slice_util_exports.isSliceContinous(x.shape,$begin,$size);if(isPacked||!isContinous){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram($size):new SliceProgram($size),customSetup=program.getCustomSetupFunc($begin);return backend2.runWebGLProgram(program,[x],x.dtype,customSetup)}return backend2.uploadToGPU(x.dataId),shallowSlice(x,$begin,$size,backend2)}var sliceConfig2={kernelName:Slice,backendName:"webgl",kernelFunc:slice3},batchToSpaceND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;util_exports.assert(x.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),toDispose=[],reshapedIntermediate=reshape4({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),transposedIntermediate=transpose3({inputs:{x:reshapedIntermediate},backend:backend2,attrs:{perm:permuted}}),reshapedIntermediate2=reshape4({inputs:{x:transposedIntermediate},backend:backend2,attrs:{shape:reshapedPermuted}}),sliced=slice3({inputs:{x:reshapedIntermediate2},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return toDispose.push(reshapedIntermediate),toDispose.push(transposedIntermediate),toDispose.push(reshapedIntermediate2),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),sliced},batchToSpaceNDConfig2={kernelName:BatchToSpaceND,backendName:"webgl",kernelFunc:batchToSpaceND3};function bincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.readSync(x.dataId),weightsVals=backend2.readSync(weights.dataId),outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig2={kernelName:Bincount,backendName:"webgl",kernelFunc:bincount3},NOT_EQUAL="return float(a != b);",notEqual3=binaryKernelFunc2({opSnippet:NOT_EQUAL,dtype:"bool"}),notEqualConfig2={kernelName:NotEqual,backendName:"webgl",kernelFunc:notEqual3};function real3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.real},backend:backend2})}var realConfig2={kernelName:Real,backendName:"webgl",kernelFunc:real3},TO_INT="return float(int(x));";function int(input2,backend2){let program=new UnaryOpProgram(input2.shape,TO_INT),output=backend2.runWebGLProgram(program,[input2],"int32");return{dataId:output.dataId,shape:output.shape,dtype:output.dtype}}function cast4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity3({inputs:{x},backend:backend2});let zerosTensor=zeros(x.shape),floatX=cast4({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex3({inputs:{real:floatX,imag:zerosTensor},backend:backend2});return zerosTensor.dispose(),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real3({inputs:{input:x},backend:backend2}),result=cast4({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity3({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32")return int(x,backend2);if(dtype==="bool"){let zerosTensorInfo=backend2.makeTensorInfo([],"bool",util_exports.getTypedArrayFromDType("bool",1)),result=notEqual3({inputs:{a:x,b:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),result}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig2={kernelName:Cast,backendName:"webgl",kernelFunc:cast4},CEIL="return ceil(x);",ceil3=unaryKernelFunc2({opSnippet:CEIL,packedOpSnippet:CEIL,cpuKernelImpl:ceilImplCPU}),ceilConfig2={kernelName:Ceil,backendName:"webgl",kernelFunc:ceil3},ClipProgram=class{constructor(aShape){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1900,7 +1900,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         }
         setOutput(result);
       }
-    `}};function shiftedChannels(channels,channel,shift){let channelIdx=channels.indexOf(channel);return channels.map((c,idx)=>idx===channelIdx?`${c} - ${shift}`:c).join()}function imag3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.imag},backend:backend2})}var imagConfig2={kernelName:Imag,backendName:"webgl",kernelFunc:imag3};function concatImpl2(inputs,axis,backend2){let dtype=inputs[0].dtype;if(dtype==="complex64"){let reals=inputs.map(t=>real3({inputs:{input:t},backend:backend2})),imags=inputs.map(t=>imag3({inputs:{input:t},backend:backend2})),realConcated=concatImpl2(reals,axis,backend2),imagConcated=concatImpl2(imags,axis,backend2),result2=complex3({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result2}if(dtype==="string"){let{tensors2D:tensors2D2,outShape:outShape2}=computeTensors2D(inputs,axis,backend2),inputsValShapes=tensors2D2.map(t=>({vals:backend2.texData.get(t.dataId).values,shape:t.shape})),simplyConcat=tensors2D2[0].shape[0]===1,outVals=concatImplCPU(inputsValShapes,outShape2,dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),outInfo=backend2.makeTensorInfo(finalOutShape,dtype,outVals);return tensors2D2.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}if(inputs.length>env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(inputs.length/2),leftSide=concatImpl2(inputs.slice(0,midIndex),axis,backend2),rightSide=concatImpl2(inputs.slice(midIndex),axis,backend2),result2=concatImpl2([leftSide,rightSide],axis,backend2);return backend2.disposeIntermediateTensorInfo(leftSide),backend2.disposeIntermediateTensorInfo(rightSide),result2}if(env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&inputs[0].shape.length>1){let program2=new ConcatPackedProgram(inputs.map(t=>t.shape),axis);return backend2.runWebGLProgram(program2,inputs,dtype)}let{tensors2D,outShape}=computeTensors2D(inputs,axis,backend2),program=new ConcatProgram(tensors2D.map(t=>t.shape)),result=backend2.runWebGLProgram(program,tensors2D,dtype);tensors2D.forEach(r=>backend2.disposeIntermediateTensorInfo(r));let reshapedResult=reshape4({inputs:{x:result},attrs:{shape:outShape},backend:backend2});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}function computeTensors2D(inputs,axis,backend2){let outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis);return{tensors2D:inputs.map(x=>reshape4({inputs:{x},attrs:{shape:[-1,util_exports.sizeFromShape(x.shape.slice(axis))]},backend:backend2})),outShape}}function concat3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);return backend_util_exports.assertParamsConsistent(shapes,$axis),concatImpl2($inputs,$axis,backend2)}var concatConfig2={kernelName:Concat,backendName:"webgl",kernelFunc:concat3},Conv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivationWeights=!1,hasLeakyreluAlpha=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4,isChannelsLast=convInfo.dataFormat==="channelsLast",rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivationWeights?activationSnippet=`float activation(float a) {
+    `}};function shiftedChannels(channels,channel,shift){let channelIdx=channels.indexOf(channel);return channels.map((c,idx)=>idx===channelIdx?`${c} - ${shift}`:c).join()}function imag3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.imag},backend:backend2})}var imagConfig2={kernelName:Imag,backendName:"webgl",kernelFunc:imag3};function concatImpl2(inputs,axis,backend2){let dtype=inputs[0].dtype;if(dtype==="complex64"){let reals=inputs.map(t=>real3({inputs:{input:t},backend:backend2})),imags=inputs.map(t=>imag3({inputs:{input:t},backend:backend2})),realConcated=concatImpl2(reals,axis,backend2),imagConcated=concatImpl2(imags,axis,backend2),result2=complex3({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result2}if(dtype==="string"){let{tensors2D:tensors2D2,outShape:outShape2}=computeTensors2D(inputs,axis,backend2),inputsValShapes=tensors2D2.map(t=>({vals:backend2.readSync(t.dataId),shape:t.shape})),simplyConcat=tensors2D2[0].shape[0]===1,outVals=concatImplCPU(inputsValShapes,outShape2,dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),outInfo=backend2.makeTensorInfo(finalOutShape,dtype,outVals);return tensors2D2.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}if(inputs.length>env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(inputs.length/2),leftSide=concatImpl2(inputs.slice(0,midIndex),axis,backend2),rightSide=concatImpl2(inputs.slice(midIndex),axis,backend2),result2=concatImpl2([leftSide,rightSide],axis,backend2);return backend2.disposeIntermediateTensorInfo(leftSide),backend2.disposeIntermediateTensorInfo(rightSide),result2}if(env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&inputs[0].shape.length>1){let program2=new ConcatPackedProgram(inputs.map(t=>t.shape),axis);return backend2.runWebGLProgram(program2,inputs,dtype)}let{tensors2D,outShape}=computeTensors2D(inputs,axis,backend2),program=new ConcatProgram(tensors2D.map(t=>t.shape)),result=backend2.runWebGLProgram(program,tensors2D,dtype);tensors2D.forEach(r=>backend2.disposeIntermediateTensorInfo(r));let reshapedResult=reshape4({inputs:{x:result},attrs:{shape:outShape},backend:backend2});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}function computeTensors2D(inputs,axis,backend2){let outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis);return{tensors2D:inputs.map(x=>reshape4({inputs:{x},attrs:{shape:[-1,util_exports.sizeFromShape(x.shape.slice(axis))]},backend:backend2})),outShape}}function concat3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);return backend_util_exports.assertParamsConsistent(shapes,$axis),concatImpl2($inputs,$axis,backend2)}var concatConfig2={kernelName:Concat,backendName:"webgl",kernelFunc:concat3},Conv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivationWeights=!1,hasLeakyreluAlpha=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4,isChannelsLast=convInfo.dataFormat==="channelsLast",rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivationWeights?activationSnippet=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${activation2}
         }`:hasLeakyreluAlpha?activationSnippet=`float activation(float a) {
@@ -2166,7 +2166,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
 
         ${glsl.output} = result;
       }
-    `}};function conv2dByMatMul({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let xShape=x.shape,xTexData=backend2.texData.get(x.dataId),sharedMatMulDim=convInfo.inChannels,outerShapeX=xShape[0]*xShape[1]*xShape[2],outerShapeFilter=convInfo.outChannels,isChannelsLast=convInfo.dataFormat==="channelsLast",transposeA=!1,transposeB=!1,out,intermediates=[],batchMatMulWillBeUnpacked=(outerShapeX===1||outerShapeFilter===1)&&sharedMatMulDim>MATMUL_SHARED_DIM_THRESHOLD,reshapeWillBeExpensive=xShape[2]%2!==0&&!!xTexData.isPacked;if(batchMatMulWillBeUnpacked||!env().getBool("WEBGL_LAZILY_UNPACK")||!env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!reshapeWillBeExpensive){let targetShape=isChannelsLast?xShape[0]*xShape[1]*xShape[2]:xShape[0]*xShape[2]*xShape[3],xReshaped=reshape4({inputs:{x},backend:backend2,attrs:{shape:[1,targetShape,convInfo.inChannels]}}),filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}}),result=batchMatMulImpl({a:xReshaped,b:filterReshaped,transposeA,transposeB,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});out=reshape4({inputs:{x:result},backend:backend2,attrs:{shape:convInfo.outShape}}),intermediates.push(xReshaped),intermediates.push(filterReshaped),intermediates.push(result)}else{let targetShape=isChannelsLast?xShape[0]*xShape[1]*(xShape[2]+1):xShape[0]*xShape[2]*(xShape[3]+1),xReshaped={dataId:x.dataId,shape:[1,targetShape,convInfo.inChannels],dtype:x.dtype},originalXTexDataShape=xTexData.shape;xTexData.shape=xTexData.shape.slice(),xTexData.shape[xTexData.shape.length-2]++,util_exports.assert(isReshapeFree(xTexData.shape,xReshaped.shape),()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);let filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}});intermediates.push(filterReshaped);let pointwiseConv=batchMatMulImpl({a:xReshaped,b:filterReshaped,backend:backend2,transposeA,transposeB,bias,activation:activation2,preluActivationWeights,leakyreluAlpha}),pointwiseConvTexData=backend2.texData.get(pointwiseConv.dataId);util_exports.assert(pointwiseConvTexData.isPacked,()=>"batchMatMul result is expected to be packed"),xTexData.shape=originalXTexDataShape,pointwiseConvTexData.shape=convInfo.outShape,out=identity3({inputs:{x:pointwiseConv},backend:backend2}),out.shape=convInfo.outShape,intermediates.push(pointwiseConv)}for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let{filterWidth,filterHeight,inChannels,outWidth,outHeight,dataFormat}=convInfo,isChannelsLast=dataFormat==="channelsLast",sharedDim=filterWidth*filterHeight*inChannels,numCols=outHeight*outWidth,x2ColShape=[sharedDim,numCols],transposeA=!0,transposeB=!1,intermediates=[],xSqueezed=reshape4({inputs:{x},backend:backend2,attrs:{shape:x.shape.slice(1)}}),w2Row=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,sharedDim,util_exports.sizeFromShape(filter.shape)/sharedDim]}});intermediates.push(xSqueezed),intermediates.push(w2Row);let im2ColProgram=new Im2ColPackedProgram(x2ColShape,xSqueezed.shape,convInfo),im2Col=backend2.runWebGLProgram(im2ColProgram,[xSqueezed],"float32"),im2ColReshaped=reshape4({inputs:{x:im2Col},backend:backend2,attrs:{shape:[1,x2ColShape[0],x2ColShape[1]]}});intermediates.push(im2Col),intermediates.push(im2ColReshaped);let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,matmulProgram=new MatMulPackedProgram(im2ColReshaped.shape,w2Row.shape,[1,numCols,convInfo.outChannels],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs=[im2ColReshaped,w2Row];if(bias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let product=backend2.runWebGLProgram(matmulProgram,inputs,"float32"),outShape=isChannelsLast?[1,outHeight,outWidth,convInfo.outChannels]:[1,convInfo.outChannels,outHeight,outWidth],out=reshape4({inputs:{x:product},backend:backend2,attrs:{shape:outShape}});intermediates.push(product);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2d4(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out;if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2});else{let program=new Conv2DProgram(convInfo);out=backend2.runWebGLProgram(program,[x,filter],"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return backend2.disposeIntermediateTensorInfo(out),outReshaped}var conv2DConfig2={kernelName:Conv2D,backendName:"webgl",kernelFunc:conv2d4},Conv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast";this.userCode=`
+    `}};function conv2dByMatMul({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let xShape=x.shape,xTexData=backend2.texData.get(x.dataId),sharedMatMulDim=convInfo.inChannels,outerShapeX=xShape[0]*xShape[1]*xShape[2],outerShapeFilter=convInfo.outChannels,isChannelsLast=convInfo.dataFormat==="channelsLast",transposeA=!1,transposeB=!1,out,intermediates=[],batchMatMulWillBeUnpacked=(outerShapeX===1||outerShapeFilter===1)&&sharedMatMulDim>MATMUL_SHARED_DIM_THRESHOLD,reshapeWillBeExpensive=xShape[2]%2!=0&&!!xTexData.isPacked;if(batchMatMulWillBeUnpacked||!env().getBool("WEBGL_LAZILY_UNPACK")||!env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!reshapeWillBeExpensive){let targetShape=isChannelsLast?xShape[0]*xShape[1]*xShape[2]:xShape[0]*xShape[2]*xShape[3],xReshaped=reshape4({inputs:{x},backend:backend2,attrs:{shape:[1,targetShape,convInfo.inChannels]}}),filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}}),result=batchMatMulImpl({a:xReshaped,b:filterReshaped,transposeA,transposeB,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});out=reshape4({inputs:{x:result},backend:backend2,attrs:{shape:convInfo.outShape}}),intermediates.push(xReshaped),intermediates.push(filterReshaped),intermediates.push(result)}else{let targetShape=isChannelsLast?xShape[0]*xShape[1]*(xShape[2]+1):xShape[0]*xShape[2]*(xShape[3]+1),xReshaped={dataId:x.dataId,shape:[1,targetShape,convInfo.inChannels],dtype:x.dtype},originalXTexDataShape=xTexData.shape;xTexData.shape=xTexData.shape.slice(),xTexData.shape[xTexData.shape.length-2]++,util_exports.assert(isReshapeFree(xTexData.shape,xReshaped.shape),()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);let filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}});intermediates.push(filterReshaped);let pointwiseConv=batchMatMulImpl({a:xReshaped,b:filterReshaped,backend:backend2,transposeA,transposeB,bias,activation:activation2,preluActivationWeights,leakyreluAlpha}),pointwiseConvTexData=backend2.texData.get(pointwiseConv.dataId);util_exports.assert(pointwiseConvTexData.isPacked,()=>"batchMatMul result is expected to be packed"),xTexData.shape=originalXTexDataShape,pointwiseConvTexData.shape=convInfo.outShape,out=identity3({inputs:{x:pointwiseConv},backend:backend2}),out.shape=convInfo.outShape,intermediates.push(pointwiseConv)}for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let{filterWidth,filterHeight,inChannels,outWidth,outHeight,dataFormat}=convInfo,isChannelsLast=dataFormat==="channelsLast",sharedDim=filterWidth*filterHeight*inChannels,numCols=outHeight*outWidth,x2ColShape=[sharedDim,numCols],transposeA=!0,transposeB=!1,intermediates=[],xSqueezed=reshape4({inputs:{x},backend:backend2,attrs:{shape:x.shape.slice(1)}}),w2Row=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,sharedDim,util_exports.sizeFromShape(filter.shape)/sharedDim]}});intermediates.push(xSqueezed),intermediates.push(w2Row);let im2ColProgram=new Im2ColPackedProgram(x2ColShape,xSqueezed.shape,convInfo),im2Col=backend2.runWebGLProgram(im2ColProgram,[xSqueezed],"float32"),im2ColReshaped=reshape4({inputs:{x:im2Col},backend:backend2,attrs:{shape:[1,x2ColShape[0],x2ColShape[1]]}});intermediates.push(im2Col),intermediates.push(im2ColReshaped);let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,matmulProgram=new MatMulPackedProgram(im2ColReshaped.shape,w2Row.shape,[1,numCols,convInfo.outChannels],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs=[im2ColReshaped,w2Row];if(bias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let product=backend2.runWebGLProgram(matmulProgram,inputs,"float32"),outShape=isChannelsLast?[1,outHeight,outWidth,convInfo.outChannels]:[1,convInfo.outChannels,outHeight,outWidth],out=reshape4({inputs:{x:product},backend:backend2,attrs:{shape:outShape}});intermediates.push(product);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2d4(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out;if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2});else{let program=new Conv2DProgram(convInfo);out=backend2.runWebGLProgram(program,[x,filter],"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return backend2.disposeIntermediateTensorInfo(out),outReshaped}var conv2DConfig2={kernelName:Conv2D,backendName:"webgl",kernelFunc:conv2d4},Conv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2440,7 +2440,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(index){return(gpgpu,webGLProgram)=>{this.index==null&&(this.index=gpgpu.getUniformLocation(webGLProgram,"index")),gpgpu.gl.uniform1f(this.index,index)}}};function getCoords2(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.x, ${name}.y`;if(rank===3)return`${name}.x, ${name}.y, ${name}.z`;if(rank===4)return`${name}.x, ${name}.y, ${name}.z, ${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function getFinalCoord(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.y`;if(rank===3)return`${name}.z`;if(rank===4)return`${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function cumsum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];if(permutedAxis!==xRank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.shape.length-1} but got axis=${axis}`);let size=x.shape[permutedAxis],result=identity3({inputs:{x:permutedX},backend:backend2});for(let i=0;i<=Math.ceil(Math.log2(size))-1;i++){let program=new CumSumProgram(permutedX.shape,!1,reverse5),customSetup=program.getCustomSetupFunc(i),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype,customSetup),backend2.disposeIntermediateTensorInfo(prevResult)}if(exclusive){let program=new CumSumProgram(permutedX.shape,exclusive,reverse5),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype),backend2.disposeIntermediateTensorInfo(prevResult)}if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose3({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo(permutedX),reverseTransposedResult}return result}var cumsumConfig2={kernelName:Cumsum,backendName:"webgl",kernelFunc:cumsum3};function denseBincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.texData.get(x.dataId).values,weightsVals=backend2.texData.get(weights.dataId).values,outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImplCPU(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig2={kernelName:DenseBincount,backendName:"webgl",kernelFunc:denseBincount3},DepthToSpaceProgram=class{constructor(outputShape,blockSize,dataFormat){this.variableNames=["x"],this.outputShape=[],this.outputShape=outputShape,this.blockSize=blockSize,this.dataFormat=dataFormat,this.userCode=`
+    `}getCustomSetupFunc(index){return(gpgpu,webGLProgram)=>{this.index==null&&(this.index=gpgpu.getUniformLocation(webGLProgram,"index")),gpgpu.gl.uniform1f(this.index,index)}}};function getCoords2(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.x, ${name}.y`;if(rank===3)return`${name}.x, ${name}.y, ${name}.z`;if(rank===4)return`${name}.x, ${name}.y, ${name}.z, ${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function getFinalCoord(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.y`;if(rank===3)return`${name}.z`;if(rank===4)return`${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function cumsum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];if(permutedAxis!==xRank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.shape.length-1} but got axis=${axis}`);let size=x.shape[permutedAxis],result=identity3({inputs:{x:permutedX},backend:backend2});for(let i=0;i<=Math.ceil(Math.log2(size))-1;i++){let program=new CumSumProgram(permutedX.shape,!1,reverse5),customSetup=program.getCustomSetupFunc(i),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype,customSetup),backend2.disposeIntermediateTensorInfo(prevResult)}if(exclusive){let program=new CumSumProgram(permutedX.shape,exclusive,reverse5),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype),backend2.disposeIntermediateTensorInfo(prevResult)}if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose3({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo(permutedX),reverseTransposedResult}return result}var cumsumConfig2={kernelName:Cumsum,backendName:"webgl",kernelFunc:cumsum3};function denseBincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.readSync(x.dataId),weightsVals=backend2.readSync(weights.dataId),outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImplCPU(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig2={kernelName:DenseBincount,backendName:"webgl",kernelFunc:denseBincount3},DepthToSpaceProgram=class{constructor(outputShape,blockSize,dataFormat){this.variableNames=["x"],this.outputShape=[],this.outputShape=outputShape,this.blockSize=blockSize,this.dataFormat=dataFormat,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2521,7 +2521,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
           vec4 xR${r}C${c} = vec4(0.);`;for(let r=0;r<filterHeight;r++)for(let texelC=0;texelC<texelsAcross;texelC++){let c=texelC*2;if(mainLoop+=`
           xR = xRCorner + ${r*dilationHeight};
           xC = xCCorner + ${c*dilationWidth};
-        `,strideWidth===1){if(c<filterWidth&&(padLeft%2===1?mainLoop+=`
+        `,strideWidth===1){if(c<filterWidth&&(padLeft%2==1?mainLoop+=`
                 xCOffset = xC + 1;
                 if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
                   xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
@@ -2557,7 +2557,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
                 }
 
                 xR${r}C${c} = xTexelR${r}C${c};
-              `,c+1<filterWidth)){let nextTexelOffset=padLeft%2===0?util_exports.nearestLargerEven(dilationWidth):dilationWidth;dilationWidth%2===0&&padLeft%2===1||dilationWidth%2!==0&&padLeft%2!==1?(mainLoop+=`
+              `,c+1<filterWidth)){let nextTexelOffset=padLeft%2==0?util_exports.nearestLargerEven(dilationWidth):dilationWidth;dilationWidth%2==0&&padLeft%2==1||dilationWidth%2!=0&&padLeft%2!=1?(mainLoop+=`
                   xCOffset = xC + ${padLeft%2} + ${nextTexelOffset};
 
                   if(xR >= 0 && xR < ${xNumRows} &&
@@ -2586,7 +2586,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
                   xR${r}C${c+1} = xTexelR${r}C${c+2};
                 `}}else c<filterWidth&&(mainLoop+=`
               if(xR >= 0 && xR < ${xNumRows}) {
-            `,padLeft%2===1?(mainLoop+=`
+            `,padLeft%2==1?(mainLoop+=`
                 xCOffset = xC + 1 - ${strideWidth};
                 if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                   xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
@@ -2668,7 +2668,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         ${applyActivationSnippet}
         setOutput(result);
       }
-    `}};function depthwiseConv2dNative2(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs,$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),program;return env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1?program=new DepthwiseConvPacked2DProgram(convInfo):program=new DepthwiseConv2DProgram(convInfo),backend2.runWebGLProgram(program,[x,filter],"float32")}var depthwiseConv2dNativeConfig2={kernelName:DepthwiseConv2dNative,backendName:"webgl",kernelFunc:depthwiseConv2dNative2},DepthwiseConv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
+    `}};function depthwiseConv2dNative2(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs,$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),program;return env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels==1?program=new DepthwiseConvPacked2DProgram(convInfo):program=new DepthwiseConv2DProgram(convInfo),backend2.runWebGLProgram(program,[x,filter],"float32")}var depthwiseConv2dNativeConfig2={kernelName:DepthwiseConv2dNative,backendName:"webgl",kernelFunc:depthwiseConv2dNative2},DepthwiseConv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2961,7 +2961,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
 
         ${glsl.output} = result;
       }
-    `}},fromPixelsConfig={kernelName:FromPixels,backendName:"webgl",kernelFunc:fromPixels2},fromPixels2DContext2;function fromPixels2(args){let{inputs,backend:backend2,attrs}=args,{pixels}=inputs,{numChannels}=attrs,isVideo=typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement,isImage=typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement,[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],texShape=[height,width],outShape=[height,width,numChannels];(isImage||isVideo)&&(fromPixels2DContext2==null&&(fromPixels2DContext2=document.createElement("canvas").getContext("2d")),fromPixels2DContext2.canvas.width=width,fromPixels2DContext2.canvas.height=height,fromPixels2DContext2.drawImage(pixels,0,0,width,height),pixels=fromPixels2DContext2.canvas);let tempPixelHandle=backend2.makeTensorInfo(texShape,"int32");backend2.texData.get(tempPixelHandle.dataId).usage=TextureUsage.PIXELS,backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId),pixels);let program=env().getBool("WEBGL_PACK")?new FromPixelsPackedProgram(outShape):new FromPixelsProgram(outShape),res=backend2.runWebGLProgram(program,[tempPixelHandle],"int32");return backend2.disposeData(tempPixelHandle.dataId),res}function fusedConv2d(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out,intermediates=[];if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else{let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!1):null,program=new Conv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs2=[x,filter];if(bias&&inputs2.push(bias),preluActivationWeights&&inputs2.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs2.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}out=backend2.runWebGLProgram(program,inputs2,"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return intermediates.push(out),intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outReshaped}var fusedConv2DConfig2={kernelName:FusedConv2D,backendName:"webgl",kernelFunc:fusedConv2d};function fusedDepthwiseConv2D2(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,intermediates=[],$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),shouldPackDepthwiseConv=env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1,fusedActivation=activation2?mapActivationToShaderProgram(activation2,shouldPackDepthwiseConv):null,programInputs=[x,filter],hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu";if(hasBias&&programInputs.push(bias),hasPreluActivationWeights&&programInputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));programInputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let program;shouldPackDepthwiseConv?program=new DepthwiseConvPacked2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha):program=new DepthwiseConv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha);let result=backend2.runWebGLProgram(program,programInputs,"float32");return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var fusedDepthwiseConv2DConfig2={kernelName:FusedDepthwiseConv2D,backendName:"webgl",kernelFunc:fusedDepthwiseConv2D2},GatherNDProgram=class{constructor(sliceDim,strides,shape){this.sliceDim=sliceDim,this.strides=strides,this.variableNames=["x","indices"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),strideString=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},fromPixelsConfig={kernelName:FromPixels,backendName:"webgl",kernelFunc:fromPixels2},fromPixels2DContext2;function fromPixels2(args){let{inputs,backend:backend2,attrs}=args,{pixels}=inputs,{numChannels}=attrs,isVideo=typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement,isImage=typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement,isImageBitmap=typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap,[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],texShape=[height,width],outShape=[height,width,numChannels];(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext2==null&&(fromPixels2DContext2=document.createElement("canvas").getContext("2d")),fromPixels2DContext2.canvas.width=width,fromPixels2DContext2.canvas.height=height,fromPixels2DContext2.drawImage(pixels,0,0,width,height),pixels=fromPixels2DContext2.canvas);let tempPixelHandle=backend2.makeTensorInfo(texShape,"int32");backend2.texData.get(tempPixelHandle.dataId).usage=TextureUsage.PIXELS,backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId),pixels);let program=env().getBool("WEBGL_PACK")?new FromPixelsPackedProgram(outShape):new FromPixelsProgram(outShape),res=backend2.runWebGLProgram(program,[tempPixelHandle],"int32");return backend2.disposeData(tempPixelHandle.dataId),res}function fusedConv2d(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out,intermediates=[];if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else{let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!1):null,program=new Conv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs2=[x,filter];if(bias&&inputs2.push(bias),preluActivationWeights&&inputs2.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs2.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}out=backend2.runWebGLProgram(program,inputs2,"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return intermediates.push(out),intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outReshaped}var fusedConv2DConfig2={kernelName:FusedConv2D,backendName:"webgl",kernelFunc:fusedConv2d};function fusedDepthwiseConv2D2(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,intermediates=[],$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),shouldPackDepthwiseConv=env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels==1,fusedActivation=activation2?mapActivationToShaderProgram(activation2,shouldPackDepthwiseConv):null,programInputs=[x,filter],hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu";if(hasBias&&programInputs.push(bias),hasPreluActivationWeights&&programInputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));programInputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let program;shouldPackDepthwiseConv?program=new DepthwiseConvPacked2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha):program=new DepthwiseConv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha);let result=backend2.runWebGLProgram(program,programInputs,"float32");return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var fusedDepthwiseConv2DConfig2={kernelName:FusedDepthwiseConv2D,backendName:"webgl",kernelFunc:fusedDepthwiseConv2D2},GatherNDProgram=class{constructor(sliceDim,strides,shape){this.sliceDim=sliceDim,this.strides=strides,this.variableNames=["x","indices"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),strideString=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${stridesType} strides = ${stridesType}(${this.strides});
          void main() {
           ${dtype} coords = getOutputCoords();
@@ -3921,7 +3921,7 @@ return a / b;`,DIV_PACKED=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,softplus3=unaryKernelFunc2({opSnippet:SOFTPLUS}),softplusConfig2={kernelName:Softplus,backendName:"webgl",kernelFunc:softplus3},spaceToBatchND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;util_exports.assert(x.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let toDispose=[],paddedX=padV22({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),reshapedPaddedX=reshape4({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose3({inputs:{x:reshapedPaddedX},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape4({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return toDispose.push(paddedX),toDispose.push(reshapedPaddedX),toDispose.push(paddedXT),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result},spaceToBatchNDConfig2={kernelName:SpaceToBatchND,backendName:"webgl",kernelFunc:spaceToBatchND3};function sparseToDense3(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,program=new ScatterProgram(numUpdates,sliceRank,sparseIndices.shape.length,sparseValues.shape.length,strides,[outputSize,1],sumDupeIndices),res=backend2.runWebGLProgram(program,[sparseValues,sparseIndices,defaultValue],sparseValues.dtype),reshaped=reshape4({inputs:{x:res},backend:backend2,attrs:{shape:outputShape}});return backend2.disposeIntermediateTensorInfo(res),reshaped}var sparseToDenseConfig2={kernelName:SparseToDense,backendName:"webgl",kernelFunc:sparseToDense3};function splitV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),xRank=x.shape.length,begin=new Array(xRank).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[axis]=s;let sliceT=slice3({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[axis]+=s,sliceT})}var splitVConfig2={kernelName:SplitV,backendName:"webgl",kernelFunc:splitV2},SQRT="return sqrt(x);",sqrt3=unaryKernelFunc2({opSnippet:SQRT}),sqrtConfig2={kernelName:Sqrt,backendName:"webgl",kernelFunc:sqrt3},SQUARE="return x * x;",square3=unaryKernelFunc2({opSnippet:SQUARE}),squareConfig2={kernelName:Square,backendName:"webgl",kernelFunc:square3},SQUARED_DIFFERENCE="return (a - b) * (a - b);",squaredDifference3=binaryKernelFunc2({opSnippet:SQUARED_DIFFERENCE,packedOpSnippet:SQUARED_DIFFERENCE}),squaredDifferenceConfig2={kernelName:SquaredDifference,backendName:"webgl",kernelFunc:squaredDifference3};function step3({inputs,attrs,backend:backend2}){let{x}=inputs,opSnippet=CHECK_NAN_SNIPPET+`
+`,softplus3=unaryKernelFunc2({opSnippet:SOFTPLUS}),softplusConfig2={kernelName:Softplus,backendName:"webgl",kernelFunc:softplus3},spaceToBatchND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;util_exports.assert(x.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let toDispose=[],paddedX=padV22({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),reshapedPaddedX=reshape4({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose3({inputs:{x:reshapedPaddedX},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape4({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return toDispose.push(paddedX),toDispose.push(reshapedPaddedX),toDispose.push(paddedXT),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result},spaceToBatchNDConfig2={kernelName:SpaceToBatchND,backendName:"webgl",kernelFunc:spaceToBatchND3};function sparseToDense3(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,program=new ScatterProgram(numUpdates,sliceRank,sparseIndices.shape.length,sparseValues.shape.length,strides,[outputSize,1],sumDupeIndices),res=backend2.runWebGLProgram(program,[sparseValues,sparseIndices,defaultValue],sparseValues.dtype),reshaped=reshape4({inputs:{x:res},backend:backend2,attrs:{shape:outputShape}});return backend2.disposeIntermediateTensorInfo(res),reshaped}var sparseToDenseConfig2={kernelName:SparseToDense,backendName:"webgl",kernelFunc:sparseToDense3};function splitV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),xRank=x.shape.length,begin=new Array(xRank).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[$axis]=s;let sliceT=slice3({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[$axis]+=s,sliceT})}var splitVConfig2={kernelName:SplitV,backendName:"webgl",kernelFunc:splitV2},SQRT="return sqrt(x);",sqrt3=unaryKernelFunc2({opSnippet:SQRT}),sqrtConfig2={kernelName:Sqrt,backendName:"webgl",kernelFunc:sqrt3},SQUARE="return x * x;",square3=unaryKernelFunc2({opSnippet:SQUARE}),squareConfig2={kernelName:Square,backendName:"webgl",kernelFunc:square3},SQUARED_DIFFERENCE="return (a - b) * (a - b);",squaredDifference3=binaryKernelFunc2({opSnippet:SQUARED_DIFFERENCE,packedOpSnippet:SQUARED_DIFFERENCE}),squaredDifferenceConfig2={kernelName:SquaredDifference,backendName:"webgl",kernelFunc:squaredDifference3};function step3({inputs,attrs,backend:backend2}){let{x}=inputs,opSnippet=CHECK_NAN_SNIPPET+`
     return x > 0.0 ? 1.0 : float(${attrs.alpha});
   `,program=new UnaryOpProgram(x.shape,opSnippet);return backend2.runWebGLProgram(program,[x],x.dtype)}var stepConfig2={kernelName:Step,backendName:"webgl",kernelFunc:step3},StridedSliceProgram=class{constructor(begin,strides,size){this.variableNames=["x"],this.outputShape=size;let rank=size.length,inputDtype=getCoordsDataType(size.length),dtype=getCoordsDataType(size.length),newCoords="";if(rank===1)newCoords="coords * strides + begin";else{let outputAxis=0;newCoords=size.map((_,i)=>(outputAxis++,size.length===1?`coords * strides[${i}] + begin[${i}]`:`coords[${outputAxis-1}] * strides[${i}] + begin[${i}]`)).join(",")}this.userCode=`
       ${inputDtype} begin = ${inputDtype}(${begin});
@@ -3939,7 +3939,7 @@ return a / b;`,DIV_PACKED=`
         ${dtype} resRC = getOutputCoords();
         setOutput(getA(${sourceCoords}));
       }
-    `}};function getSourceCoords3(aShape){let rank=aShape.length;if(rank>5)throw Error(`Tile for rank ${rank} is not yet supported`);if(rank===1)return`imod(resRC, ${aShape[0]})`;let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],sourceCoords=[];for(let i=0;i<aShape.length;i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);return sourceCoords.join()}function tile4(params){let{inputs,backend:backend2,attrs}=params,{x}=inputs,{reps}=attrs;if(x.dtype==="string"){let decodedData=backend2.texData.get(x.dataId).values.map(d=>util_exports.decodeString(d)),buf=buffer(x.shape,x.dtype,decodedData),outBuf=tileImplCPU(buf,reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let program=new TileProgram(x.shape,reps);return backend2.runWebGLProgram(program,[x],x.dtype)}var tileConfig2={kernelName:Tile,backendName:"webgl",kernelFunc:tile4};function topK2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs,xVals=backend2.texData.get(x.dataId).values,[allTopKVals,allTopKIndices]=topKImplCPU(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig2={kernelName:TopK,backendName:"webgl",kernelFunc:topK2};function unique4(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex2(x,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let values=backend2.readSync(x.dataId),{outputValues,outputShape,indices}=uniqueImplCPU(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig2={kernelName:Unique,backendName:"webgl",kernelFunc:unique4};function unpack2(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let x=value,xRank=x.shape.length,num=value.shape[axis],outShape=new Array(xRank-1),outIndex=0;for(let i=0;i<xRank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let toDispose=[],begin=new Array(xRank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let sliced=slice3({inputs:{x},backend:backend2,attrs:{begin,size}}),reshaped=reshape4({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}});res[i]=reshaped,toDispose.push(sliced)}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),res}var unpackConfig2={kernelName:Unpack,backendName:"webgl",kernelFunc:unpack2},SegmentOpProgram=class{constructor(segOpInfo,segOpType){this.variableNames=["x","segmentIds"];let windowSize=segOpInfo.windowSize,batchSize=segOpInfo.batchSize,inSize=segOpInfo.inSize,numSegments=segOpInfo.numSegments,outSize=numSegments*Math.ceil(inSize/windowSize);this.outputShape=[batchSize,outSize];let initializationValue="0.0",returnValue="sumValue",windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
+    `}};function getSourceCoords3(aShape){let rank=aShape.length;if(rank>5)throw Error(`Tile for rank ${rank} is not yet supported`);if(rank===1)return`imod(resRC, ${aShape[0]})`;let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],sourceCoords=[];for(let i=0;i<aShape.length;i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);return sourceCoords.join()}function tile4(params){let{inputs,backend:backend2,attrs}=params,{x}=inputs,{reps}=attrs;if(x.dtype==="string"){let decodedData=backend2.readSync(x.dataId).map(d=>util_exports.decodeString(d)),buf=buffer(x.shape,x.dtype,decodedData),outBuf=tileImplCPU(buf,reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let program=new TileProgram(x.shape,reps);return backend2.runWebGLProgram(program,[x],x.dtype)}var tileConfig2={kernelName:Tile,backendName:"webgl",kernelFunc:tile4};function topK2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs,xVals=backend2.readSync(x.dataId),[allTopKVals,allTopKIndices]=topKImplCPU(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig2={kernelName:TopK,backendName:"webgl",kernelFunc:topK2};function unique4(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex2(x,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let values=backend2.readSync(x.dataId),{outputValues,outputShape,indices}=uniqueImplCPU(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig2={kernelName:Unique,backendName:"webgl",kernelFunc:unique4};function unpack2(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let x=value,xRank=x.shape.length,num=value.shape[axis],outShape=new Array(xRank-1),outIndex=0;for(let i=0;i<xRank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let toDispose=[],begin=new Array(xRank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let sliced=slice3({inputs:{x},backend:backend2,attrs:{begin,size}}),reshaped=reshape4({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}});res[i]=reshaped,toDispose.push(sliced)}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),res}var unpackConfig2={kernelName:Unpack,backendName:"webgl",kernelFunc:unpack2},SegmentOpProgram=class{constructor(segOpInfo,segOpType){this.variableNames=["x","segmentIds"];let windowSize=segOpInfo.windowSize,batchSize=segOpInfo.batchSize,inSize=segOpInfo.inSize,numSegments=segOpInfo.numSegments,outSize=numSegments*Math.ceil(inSize/windowSize);this.outputShape=[batchSize,outSize];let initializationValue="0.0",returnValue="sumValue",windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
         sumValue += dot(values, segFilter);
     `,checkValueOutOfBounds="";inSize%windowSize>0&&(checkValueOutOfBounds=`
         if (inIdx < 0 || inIdx >= ${inSize}) {
@@ -4045,7 +4045,7 @@ return a / b;`,DIV_PACKED=`
         }
         setOutput(${returnValue});
       }
-    `}};function unsortedSegmentSum3(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape4({inputs:{x:permutedX},backend:backend2,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend2.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range4({backend:backend2,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile4({inputs:{x:rangeInfo},backend:backend2,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape4({inputs:{x:segOpResult},backend:backend2,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend2,attrs:{perm}})}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3},kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var version7="2.8.2",version8={"tfjs-core":version,"tfjs-backend-cpu":version5,"tfjs-backend-webgl":version6,"tfjs-data":version4,"tfjs-layers":version2,"tfjs-converter":version3,tfjs:version7},CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend2){wasmFusedMatMul=backend2.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend2.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend2,inputs}=args,{a,b}=inputs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend2.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend2.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend2){wasmFunc=backend2.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend2}=args,out=backend2.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend2.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity4},wasmTranspose;function setup2(backend2){wasmTranspose=backend2.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend2,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i<perm.length;i++)perm[i]!==i&&(permIsNoOp=!1);let outShape=computeOutShape4(inputs.x.shape,attrs.perm),x={dataId:inputs.x.dataId,shape:reducedShape,dtype:inputs.x.dtype};if(permIsNoOp){let cloned=identity4({inputs,backend:backend2});return cloned.shape=outShape,cloned}let out=backend2.makeOutput(outShape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,permBytes=new Uint8Array(new Int32Array(perm).buffer),xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmTranspose(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],outId,permBytes,perm.length),out}function computeOutShape4(inShape,perm){let outShape=new Array(inShape.length);for(let i=0;i<outShape.length;i++)outShape[i]=inShape[perm[i]];return outShape}function removeOneSizeDims(shape,perm){let newShape=[],newPerm=[];for(let i=0;i<shape.length;++i)shape[i]!==1&&newShape.push(shape[i]),shape[perm[i]]!==1&&newPerm.push(perm[i]);for(let i=0;i<newPerm.length;++i){let minValIdx=-1;for(let j=0;j<newPerm.length;++j)newPerm[j]>=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend2){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xTransposed=transpose4({inputs:{x},attrs:{perm:permutedAxes},backend:backend2});let xId=backend2.dataIdMap.get(x.dataId).id;backend2.dataIdMap.get(xTransposed.dataId).id!==xId&&(inputWasTransposed=!0)}return{transposed:xTransposed,originalAxes,axes,inputWasTransposed}}var wasmFunc2;function setup3(backend2){wasmFunc2=backend2.wasm.cwrap(ArgMax,null,["number","number","number","number","number"])}function argmax(args){let{backend:backend2,inputs,attrs}=args,{axis}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let outShape=input2.shape.slice(0,-1),out=backend2.makeOutput(outShape,"int32"),outId=backend2.dataIdMap.get(out.dataId).id,outerSize=util_exports.sizeFromShape(out.shape),innerSize=input2.shape[axes[0]];return wasmFunc2(inputId,CppDType[input2.dtype],outerSize,innerSize,outId),inputWasTransposed&&backend2.disposeData(transposed.dataId),out}var argMaxConfig3={kernelName:ArgMax,backendName:"wasm",kernelFunc:argmax,setupFunc:setup3},wasmAvgPool;function setup4(backend2){wasmAvgPool=backend2.wasm.cwrap(AvgPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function avgPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,channels=convInfo.inChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);if(convInfo.dilationWidth!==1||convInfo.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmAvgPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,strideHeight,strideWidth,channels,outId),out}var avgPoolConfig3={kernelName:AvgPool,backendName:"wasm",setupFunc:setup4,kernelFunc:avgPool4};function reshape5(args){let{inputs,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize);return util_exports.assert(xSize===util_exports.sizeFromShape($shape),()=>`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape5},wasmBatchMatMul;function setup5(backend2){wasmBatchMatMul=backend2.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape5({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape5({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),a3dId=backend2.dataIdMap.get(a3d.dataId).id,b3dId=backend2.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend2.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast5(args){let{inputs:{x},attrs:{dtype},backend:backend2}=args,out=backend2.makeOutput(x.shape,dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast5},wasmClip;function setup6(backend2){wasmClip=backend2.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend2}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity4({inputs:{x:$inputs[0]},backend:backend2});let out=backend2.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape5({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend2.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend2.typedArrayFromHeap(input2)),outVals=backend2.typedArrayFromHeap(out);for(let b=0;b<batchDim;b++){let outOffset=b*sumInnerDims;for(let i=0;i<inVals.length;i++){let innerDim=innerDims[i],inOffset=b*innerDim,vals=inVals[i].subarray(inOffset,inOffset+innerDim);outVals.set(vals,outOffset),outOffset+=innerDim}}return out}var concatConfig3={kernelName:Concat,backendName:"wasm",kernelFunc:concat4},wasmConv2d;function setup7(backend2){wasmConv2d=backend2.wasm.cwrap(Conv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode,dataFormat}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var conv2DConfig3={kernelName:Conv2D,backendName:"wasm",setupFunc:setup7,kernelFunc:conv2d5},wasmConv2DBackpropInput;function setup8(backend2){wasmConv2DBackpropInput=backend2.wasm.cwrap(Conv2DBackpropInput,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2DBackpropInput4(args){let{backend:backend2,inputs,attrs}=args,{dy,filter}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,inputShape}=attrs,dilations=1,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast",dxStrides=util_exports.computeStrides(convInfo.inShape),dyStrides=util_exports.computeStrides(dy.shape),[fltS0,fltS1,fltS2]=util_exports.computeStrides(filter.shape),xBatchStride=dxStrides[0],xRowStride=isChannelsLast?dxStrides[1]:dxStrides[2],xColStride=isChannelsLast?dxStrides[2]:1,xChannelStride=isChannelsLast?1:dxStrides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1],out=backend2.makeOutput(convInfo.inShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,dyId=backend2.dataIdMap.get(dy.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id;return wasmConv2DBackpropInput(dyId,filterId,batchSize,filterHeight,filterWidth,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,strideHeight,strideWidth,topPad,leftPad,fltS0,fltS1,fltS2,xBatchStride,xRowStride,xColStride,xChannelStride,yBatchStride,yRowStride,yColStride,yChannelStride,outId),out}var conv2DBackpropInputConfig3={kernelName:Conv2DBackpropInput,backendName:"wasm",setupFunc:setup8,kernelFunc:conv2DBackpropInput4},cosConfig3=createUnaryKernelConfig(Cos),InterpolationMethod;(function(InterpolationMethod2){InterpolationMethod2[InterpolationMethod2.bilinear=0]="bilinear",InterpolationMethod2[InterpolationMethod2.nearest=1]="nearest"})(InterpolationMethod||(InterpolationMethod={}));var wasmCropAndResize;function setup9(backend2){wasmCropAndResize=backend2.wasm.cwrap(CropAndResize,null,["number","number","number","number","array","number","number","number","number","number"])}function cropAndResize4(args){let{backend:backend2,inputs,attrs}=args,{method,extrapolationValue,cropSize}=attrs,{image:image3,boxes,boxInd}=inputs,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,outShape=[numBoxes,cropHeight,cropWidth,image3.shape[3]],imagesData=backend2.dataIdMap.get(image3.dataId),castedData;image3.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:image3},attrs:{dtype:"float32"}}),imagesData=backend2.dataIdMap.get(castedData.dataId));let imagesId=imagesData.id,boxesId=backend2.dataIdMap.get(boxes.dataId).id,boxIndId=backend2.dataIdMap.get(boxInd.dataId).id,out=backend2.makeOutput(outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,imagesShapeBytes=new Uint8Array(new Int32Array(image3.shape).buffer);return wasmCropAndResize(imagesId,boxesId,boxIndId,numBoxes,imagesShapeBytes,cropHeight,cropWidth,InterpolationMethod[method],extrapolationValue,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var cropAndResizeConfig3={kernelName:CropAndResize,backendName:"wasm",setupFunc:setup9,kernelFunc:cropAndResize4},wasmCumsum;function setup10(backend2){wasmCumsum=backend2.wasm.cwrap(Cumsum,null,["number","number","number","number","number","number"])}function cumsum4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length;util_exports.assert(x.dtype==="float32"||x.dtype==="int32",()=>`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend2}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend2.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend2.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend2.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend2}),backend2.disposeData(permutedX.dataId),backend2.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4},wasmDepthToSpace;function setup11(backend2){wasmDepthToSpace=backend2.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend2.makeOutput(outputShape,"float32"),xId=backend2.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4},wasmDepthwiseConv2d;function setup12(backend2){wasmDepthwiseConv2d=backend2.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d5},supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool"),expConfig3=createUnaryKernelConfig(Exp);function expandDims5(args){let{inputs,attrs,backend:backend2}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape5({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims5};function fill4(args){let{attrs:{shape,value,dtype},backend:backend2}=args,out=backend2.makeOutput(shape,dtype);return backend2.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4},wasmFlipLeftRight;function setup13(backend2){wasmFlipLeftRight=backend2.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend2}=args,{image:image3}=inputs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},floorConfig3=createUnaryKernelConfig(Floor),supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3),wasmBatchNorm;function setup14(backend2){wasmBatchNorm=backend2.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend2,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale22}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,meanId=backend2.dataIdMap.get(mean4.dataId).id,varianceId=backend2.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend2.dataIdMap.get(offset.dataId).id:0,scaleId=scale22!=null?backend2.dataIdMap.get(scale22.dataId).id:0,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend2){wasmFusedConv2d=backend2.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2},wasmFusedDepthwiseConv2d;function setup16(backend2){wasmFusedDepthwiseConv2d=backend2.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend2){wasmGatherNd=backend2.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend2,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend2.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend2.dataIdMap.get(params.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3},wasmGather;function setup18(backend2){wasmGather=backend2.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend2,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape5({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend2}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape5({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend2}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend2.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend2.dataIdMap.get(flattenX.dataId).id,indicesId=backend2.dataIdMap.get(flattenIndex.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23},supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool"),supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool"),wasmFunc3;function setupFunc2(backend2){wasmFunc3=backend2.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend2}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4},supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool"),logConfig3=createUnaryKernelConfig(Log),supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool"),wasmMax;function setup19(backend2){wasmMax=backend2.wasm.cwrap(Max,null,["number, number, number"])}function max5(args){let{backend:backend2,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend2.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max5},supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9),wasmMaxPool;function setup20(backend2){wasmMaxPool=backend2.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMean;function setup21(backend2){wasmMean=backend2.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast5({backend:backend2,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend2.dataIdMap.get(castedInput.dataId).id);let out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend2.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3},wasmMin;function setup22(backend2){wasmMin=backend2.wasm.cwrap(Min,null,["number, number, number"])}function min5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min5},supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10),supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11),negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend2,resOffset){let result=new Int32Array(backend2.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend2.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend2){wasmFunc4=backend2.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);return backend2.wasm._free(pSelectedScores),backend2.wasm._free(pValidOutputs),backend2.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc},wasmFunc5;function setup24(backend2){wasmFunc5=backend2.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pSelectedScores);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend2.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43},wasmFunc6;function setup25(backend2){wasmFunc6=backend2.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pValidOutputs);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend2.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2},supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool"),wasmOneHot;function setup26(backend2){wasmOneHot=backend2.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend2.makeOutput([...indices.shape,depth],"int32"),outId=backend2.dataIdMap.get(out.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims5({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims5({inputs:{input:t},backend:backend2,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend2,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3},wasmPadV2;function setup27(backend2){wasmPadV2=backend2.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend2,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(outShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27},supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13),wasmPrelu;function setup28(backend2){wasmPrelu=backend2.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu5(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,weightsId=backend2.dataIdMap.get(alpha.dataId).id,out=backend2.makeOutput(x.shape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu5},wasmProd;function setup29(backend2){wasmProd=backend2.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4},range5=args=>{let{backend:backend2,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend2.makeOutput([values.length],dtype);return backend2.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range5},supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14),reluConfig3=createUnaryKernelConfig(Relu),relu6Config3=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup30(backend2){wasmResizeBilinear=backend2.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend2,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend2.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend2.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4},wasmReverse;function setup31(backend2){wasmReverse=backend2.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity4({inputs:{x},backend:backend2});let out=backend2.makeOutput(x.shape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape5({inputs:{x:out},attrs:{shape:x.shape},backend:backend2})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31},wasmRotate;function setup32(backend2){wasmRotate=backend2.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend2,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32},rsqrtConfig3=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup33(backend2){wasmScatterNd=backend2.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend2,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend2.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend2.dataIdMap.get(indices.dataId).id,updatesId=backend2.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3},wasmSelect;function setup34(backend2){wasmSelect=backend2.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs,conditionId=backend2.dataIdMap.get(condition.dataId).id,tId=backend2.dataIdMap.get(t.dataId).id,eId=backend2.dataIdMap.get(e.dataId).id,out=backend2.makeOutput(t.shape,t.dtype),outId=backend2.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34},wasmFunc7;function setup35(backend2){wasmFunc7=backend2.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4},sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend2}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend2.readSync(x.dataId),out=backend2.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend2.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend2.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend2.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i<endI;i++){let xOffset=i*xStride+beginJ;outVals.set(xVals.subarray(xOffset,xOffset+size[1]),outOffset),outOffset+=size[1]}}function slice3d2(xVals,xStride1,xStride2,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++){let xOffset=i*xStride1+j*xStride2+beginK;outVals.set(xVals.subarray(xOffset,xOffset+size[2]),outOffset),outOffset+=size[2]}}function slice4d2(xVals,xStride1,xStride2,xStride3,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1],endK=beginK+size[2],beginL=begin[3];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++)for(let k=beginK;k<endK;k++){let xOffset=i*xStride1+j*xStride2+k*xStride3+beginL;outVals.set(xVals.subarray(xOffset,xOffset+size[3]),outOffset),outOffset+=size[3]}}var sliceConfig3={kernelName:Slice,backendName:"wasm",kernelFunc:slice4},wasmFunc8;function setup36(backend2){wasmFunc8=backend2.wasm.cwrap(Softmax,null,["number","number","number","number"])}function softmax5(args){let{backend:backend2,inputs:{logits},attrs:{dim}}=args,xId=backend2.dataIdMap.get(logits.dataId).id,out=backend2.makeOutput(logits.shape,logits.dtype),outId=backend2.dataIdMap.get(out.dataId).id,channels=logits.shape[dim],batch=util_exports.sizeFromShape(logits.shape)/channels;return util_exports.sizeFromShape(out.shape)===0||wasmFunc8(xId,outId,channels,batch),out}var softmaxConfig3={kernelName:Softmax,backendName:"wasm",setupFunc:setup36,kernelFunc:softmax5};function splitV3(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend2});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3},sqrtConfig3=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup37(backend2){wasmStridedSlice=backend2.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape5({inputs:{x},attrs:{shape:newShape},backend:backend2}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend2});return reshape5({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend2})}let out=backend2.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend2.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape5({inputs:{x:out},attrs:{shape:outShape},backend:backend2})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup38(backend2){wasmSum=backend2.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5},tanhConfig3=createUnaryKernelConfig(Tanh),wasmTile;function setup39(backend2){wasmTile=backend2.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile5(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[i]*reps[i];let xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),newShapeBytes=new Uint8Array(new Int32Array(newShape).buffer),out=backend2.makeOutput(newShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmTile(xId,xShapeBytes,x.shape.length,newShapeBytes,newShape.length,CppDType[out.dtype],outId),out}var tileConfig3={kernelName:Tile,backendName:"wasm",setupFunc:setup39,kernelFunc:tile5},wasmTopK;function setup40(backend2){wasmTopK=backend2.wasm.cwrap(TopK,null,["number","array","number","number","number","bool","number","number"])}var topk2=({inputs,backend:backend2,attrs})=>{let{x}=inputs,{k,sorted}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend2.makeOutput(outputShape,x.dtype),outValuesId=backend2.dataIdMap.get(outValues.dataId).id,outIndices=backend2.makeOutput(outputShape,"int32"),outIndicesId=backend2.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i<rank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let outs=new Array(numOutputs),begin=new Array(rank).fill(0),size=value.shape.slice();size[axis]=1;for(let i=0;i<outs.length;i++)begin[axis]=i,outs[i]=slice4({inputs:{x:value},attrs:{begin,size},backend:backend2});return outs.map(({dataId,dtype})=>({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4},kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data=this.dataIdMap.get(dataId);this.wasm._free(data.memoryOffset),this.wasm.tfjs.disposeData(data.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.2",WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);var draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven2,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range6,round:()=>round5});var Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!==0}function isEven2(num){return num%2===0}function round5(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale3=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale3),Math.round(height*scale3))}function getCenterPoint(pts){return pts.reduce((sum6,pt)=>sum6.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range6(num,start,step4){return Array(num).fill(0).map((_,i)=>start+i*step4)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min6,x)=>x<min6?x:min6,Infinity),minY=ys.reduce((min6,y)=>y<min6?y:min6,Infinity),maxX=xs.reduce((max6,x)=>max6<x?x:max6,0),maxY=ys.reduce((max6,y)=>max6<y?y:max6,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression2(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}function normalize(x,meanRgb){return tidy(()=>{let[r,g,b]=meanRgb,avg_r=fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=concat([avg_r,avg_g,avg_b],3);return sub(x,avg_rgb)})}function padToSquare(imgTensor,isCenterImage=!1){return tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>cast(t,"float32"));return concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array2=inputArray.slice();for(let i=array2.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array2[i];array2[i]=array2[j],array2[j]=x}return array2}function sigmoid5(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round5(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json18){let descriptors=json18.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json18.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch3=window.fetch;if(!fetch3)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:fetch3,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer2)=>err?reject(err):resolve(buffer2))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch3=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch:fetch3,...fileSystem}}function isBrowser2(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env3){environment=env3}function initialize(){return isBrowser2()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env3){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env3;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env3.createCanvasElement||(()=>new Canvas),environment.createImageElement=env3.createImageElement||(()=>new Image),environment.ImageData=env3.ImageData||environment.ImageData,environment.Video=env3.Video||environment.Video,environment.fetch=env3.fetch||environment.fetch,environment.readFile=env3.readFile||environment.readFile}var env2={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser:isBrowser2,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env2.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env2.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round5(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}function isMediaLoaded(media){let{Image,Video}=env2.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env2.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env2.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input2){let{Image,Video}=env2.getEnv();return input2 instanceof Image?new Dimensions(input2.naturalWidth,input2.naturalHeight):input2 instanceof Video?new Dimensions(input2.videoWidth,input2.videoHeight):new Dimensions(input2.width,input2.height)}function createCanvas2({width,height}){let{createCanvasElement}=env2.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env2.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas2({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env2.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await browser_exports.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input2){let{Image,Canvas,Video}=env2.getEnv();return input2 instanceof Image||input2 instanceof Canvas||input2 instanceof Video}function imageToSquare(input2,inputSize,centerImage=!1){let{Image,Canvas}=env2.getEnv();if(!(input2 instanceof Image||input2 instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input2),scale3=inputSize/Math.max(dims.height,dims.width),width=scale3*dims.width,height=scale3*dims.height,targetCanvas=createCanvas2({width:inputSize,height:inputSize}),inputCanvas=input2 instanceof Canvas?input2:createCanvasFromMedia(input2),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input2,idx)=>{if(isTensor3D(input2)){this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape;return}if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape.slice(1);return}let canvas=input2 instanceof env2.getEnv().Canvas?input2:createCanvasFromMedia(input2);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range6(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tidy(()=>{let inputTensors=range6(this.batchSize,0,1).map(batchIdx=>{let input2=this.getInput(batchIdx);if(input2 instanceof Tensor){let imgTensor=isTensor4D(input2)?input2:input2.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input2 instanceof env2.getEnv().Canvas)return browser_exports.fromPixels(imageToSquare(input2,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input2}`)});return stack(inputTensors.map(t=>cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input2,i)=>{if(!isMediaElement(input2)&&!isTensor3D(input2)&&!isTensor4D(input2))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input2=>isMediaElement(input2)&&awaitMediaLoaded(input2))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input2,detections){let{Canvas}=env2.getEnv(),canvas=input2;if(!(input2 instanceof Canvas)){let netInput=await toNetInput(input2);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas2({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init2){let{fetch:fetch3}=env2.getEnv(),res=await fetch3(url,init2);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return io_exports.loadWeights(manifest,modelBaseUri)}function matchDimensions(input2,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input2.width=width,input2.height=height,{width,height}}var NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable2})=>{let tensor2=tensor(variable2.dataSync());variable2.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env2.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights2=io_exports.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights2(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}};function depthwiseSeparableConv(x,params,stride){return tidy(()=>{let out=separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=add2(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return relu(add2(out1,add2(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=relu(add2(out1,add2(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return relu(add2(out1,add2(out2,add2(out3,out4))))})}function convLayer(x,params,padding="same",withRelu=!1){return tidy(()=>{let out=add2(conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}};function fullyConnectedLayer(x,params){return tidy(()=>add2(matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}},FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input2){return tidy(()=>softmax(this.runNet(input2)))}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictExpressions(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round5(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version10="0.10.1";function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range6(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range6(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return add2(conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=maxPool(out,[3,3],[2,2],"same"),out=add2(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv2,[1,1]),out=add2(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(256)),out=relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range6(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2,pooled=avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input2){return tidy(()=>{let{age,gender}=this.runNet(input2);return{age,gender:softmax(gender)}})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictAgeAndGender(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),ages=unstack(out.age),genders=unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale3=inputSize/Math.max(height,width);return{width:width*scale3,height:height*scale3}}),batchSize=inputDimensions.length;return tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>stack([fill([68],fillX,"float32"),fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding2=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding2(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding2(batchIdx,(w,h)=>h<w);return output.mul(fill([batchSize,136],inputSize,"float32")).sub(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input2){return tidy(()=>{let out=this.runNet(input2);return this.postProcess(out,input2.inputSize,input2.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async detectLandmarks(input2){let netInput=await toNetInput(input2),landmarkTensors=tidy(()=>unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven2(i)),yCoords=landmarksArray.filter((_,i)=>!isEven2(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};function scale2(x,params){return add2(mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=conv2d(x,filters,strides,padding);return out=add2(out,bias),out=scale2(out,params.scale),withRelu?relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tidy(()=>transpose(tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tensor1d(extractWeights(numWeights)),biases=tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale3=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale3}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tidy(()=>transpose(tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale3=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale3}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=add2(out,x),out=relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=avgPool(x,2,2,"valid"),zeros4=zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=zeros(padShapeX);out=concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=zeros(padShapeY);out=concat([out,zerosH],2)}return pooled=isPad?concat([pooled,zeros4],3):pooled,out=add2(pooled,out),out=relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(256)),out=convDown(normalized,params.conv32_down);out=maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return matMul(globalAvg,params.fc)})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async computeFaceDescriptor(input2){let netInput=await toNetInput(input2),faceDescriptorTensors=tidy(()=>unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tensor1d(extractWeights(numChannels)),batch_norm_offset=tensor1d(extractWeights(numChannels)),batch_norm_mean=tensor1d(extractWeights(numChannels)),batch_norm_variance=tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function pointwiseConvLayer(x,params,strides){return tidy(()=>{let out=conv2d(x,params.filters,strides,"same");return out=add2(out,params.batch_norm_offset),clipByValue(out,0,6)})}var epsilon3=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tidy(()=>{let out=depthwiseConv2d(x,params.filters,strides,"same");return out=batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon3),clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression3(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(!(selected.length>=outputSize)){let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}}),selected}function getCenterCoordinatesAndSizesLayer(x){let vec=unstack(transpose(x,[1,0])),sizes=[sub(vec[2],vec[0]),sub(vec[3],vec[1])],centers=[add2(vec[0],div(sizes[0],scalar(2))),add2(vec[1],div(sizes[1],scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=unstack(transpose(x1,[1,0])),div0_out=div(mul(exp(div(vec[2],scalar(5))),sizes[0]),scalar(2)),add0_out=add2(mul(div(vec[0],scalar(10)),sizes[0]),centers[0]),div1_out=div(mul(exp(div(vec[3],scalar(5))),sizes[1]),scalar(2)),add1_out=add2(mul(div(vec[1],scalar(10)),sizes[1]),centers[1]);return transpose(stack([sub(add0_out,div0_out),sub(add1_out,div1_out),add2(add0_out,div0_out),add2(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(reshape(tile(params.extra_dim,[batchSize,1,1]),[-1,4]),reshape(boxPredictions,[-1,4]));boxes=reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=sigmoid(slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=unstack(boxes),scoresByBatch=unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}function boxPredictionLayer(x,params){return tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input2){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(512,!1),"float32"),x=sub(mul(batchTensor,scalar(.007843137718737125)),scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async locateFaces(input2,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input2),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression3(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME2="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",isNumber2=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber2(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber2(a.x)&&isNumber2(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber2)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}function leaky(x){return tidy(()=>{let min6=mul(x,scalar(.10000000149011612));return add2(relu(sub(x,min6)),min6)})}function convWithBatchNorm(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=conv2d(out,params.conv.filters,[1,1],"valid"),out=sub(out,params.bn.sub),out=mul(out,params.bn.truediv),out=add2(out,params.conv.bias),leaky(out)})}function depthwiseSeparableConv2(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=add2(out,params.bias),leaky(out)})}function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub4=tensor1d(extractWeights(size)),truediv=tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub4,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub4=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub4,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}},TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(x,params){let out=convWithBatchNorm(x,params.conv0);return out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input2,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input2,inputSize){return this.forwardInput(await toNetInput(input2),inputSize)}async detect(input2,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input2),out=await this.forwardInput(netInput,inputSize),out0=tidy(()=>unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression2(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid5(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid5(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid5(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max6,curr)=>max6.classScore>curr.classScore?max6:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){return(await this.detect(input2,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME2}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function extractAllFacesAndComputeResults(parentResults,input2,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input2 instanceof Tensor?await extractFaceTensors(input2,faceBoxes):await extractFaces(input2,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input2,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input2,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){return(await this.detect(input2,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}},nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input2,options)=>nets.ssdMobilenetv1.locateFaces(input2,options),tinyFaceDetector=(input2,options)=>nets.tinyFaceDetector.locateFaces(input2,options),tinyYolov2=(input2,options)=>nets.tinyYolov2.locateFaces(input2,options),detectFaceLandmarks=input2=>nets.faceLandmark68Net.detectLandmarks(input2),detectFaceLandmarksTiny=input2=>nets.faceLandmark68TinyNet.detectLandmarks(input2),computeFaceDescriptor=input2=>nets.faceRecognitionNet.computeFaceDescriptor(input2),recognizeFaceExpressions=input2=>nets.faceExpressionNet.predictExpressions(input2),predictAgeAndGender=input2=>nets.ageGenderNet.predictAgeAndGender(input2),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input2){super();this.parentTask=parentTask;this.input=input2}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input2,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input2;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{detection}=parentResult,faces=this.input instanceof Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input2,options=new SsdMobilenetv1Options){super();this.input=input2;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input:input2,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input3=>nets.tinyFaceDetector.locateFaces(input3,options):options instanceof SsdMobilenetv1Options?input3=>nets.ssdMobilenetv1.locateFaces(input3,options):options instanceof TinyYolov2Options?input3=>nets.tinyYolov2.locateFaces(input3,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input2)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input2,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input2,options)}function detectAllFaces(input2,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input2,options)}async function allFacesSsdMobilenetv1(input2,minConfidence){return detectAllFaces(input2,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input2,forwardParams={}){return detectAllFaces(input2,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count2=1,createUniqueLabel=()=>`person ${count2++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json18){let labeledDescriptors=json18.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json18.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version11={faceapi:version10,node,browser};export{AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas2 as createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw_exports as draw,env2 as env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression2 as nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid5 as sigmoid,ssdMobilenetv1,tfjs_esm_exports as tf,tinyFaceDetector,tinyYolov2,toNetInput,utils_exports as utils,validateConfig,version11 as version};
+    `}};function unsortedSegmentSum3(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape4({inputs:{x:permutedX},backend:backend2,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend2.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range4({backend:backend2,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile4({inputs:{x:rangeInfo},backend:backend2,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape4({inputs:{x:segOpResult},backend:backend2,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend2,attrs:{perm}})}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3},kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var version7="2.8.3",version8={"tfjs-core":version,"tfjs-backend-cpu":version5,"tfjs-backend-webgl":version6,"tfjs-data":version4,"tfjs-layers":version2,"tfjs-converter":version3,tfjs:version7},CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend2){wasmFusedMatMul=backend2.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend2.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend2,inputs}=args,{a,b}=inputs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend2.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend2.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend2){wasmFunc=backend2.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend2}=args,out=backend2.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend2.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity4},wasmTranspose;function setup2(backend2){wasmTranspose=backend2.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend2,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i<perm.length;i++)perm[i]!==i&&(permIsNoOp=!1);let outShape=computeOutShape4(inputs.x.shape,attrs.perm),x={dataId:inputs.x.dataId,shape:reducedShape,dtype:inputs.x.dtype};if(permIsNoOp){let cloned=identity4({inputs,backend:backend2});return cloned.shape=outShape,cloned}let out=backend2.makeOutput(outShape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,permBytes=new Uint8Array(new Int32Array(perm).buffer),xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmTranspose(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],outId,permBytes,perm.length),out}function computeOutShape4(inShape,perm){let outShape=new Array(inShape.length);for(let i=0;i<outShape.length;i++)outShape[i]=inShape[perm[i]];return outShape}function removeOneSizeDims(shape,perm){let newShape=[],newPerm=[];for(let i=0;i<shape.length;++i)shape[i]!==1&&newShape.push(shape[i]),shape[perm[i]]!==1&&newPerm.push(perm[i]);for(let i=0;i<newPerm.length;++i){let minValIdx=-1;for(let j=0;j<newPerm.length;++j)newPerm[j]>=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend2){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xTransposed=transpose4({inputs:{x},attrs:{perm:permutedAxes},backend:backend2});let xId=backend2.dataIdMap.get(x.dataId).id;backend2.dataIdMap.get(xTransposed.dataId).id!==xId&&(inputWasTransposed=!0)}return{transposed:xTransposed,originalAxes,axes,inputWasTransposed}}var wasmFunc2;function setup3(backend2){wasmFunc2=backend2.wasm.cwrap(ArgMax,null,["number","number","number","number","number"])}function argmax(args){let{backend:backend2,inputs,attrs}=args,{axis}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let outShape=input2.shape.slice(0,-1),out=backend2.makeOutput(outShape,"int32"),outId=backend2.dataIdMap.get(out.dataId).id,outerSize=util_exports.sizeFromShape(out.shape),innerSize=input2.shape[axes[0]];return wasmFunc2(inputId,CppDType[input2.dtype],outerSize,innerSize,outId),inputWasTransposed&&backend2.disposeData(transposed.dataId),out}var argMaxConfig3={kernelName:ArgMax,backendName:"wasm",kernelFunc:argmax,setupFunc:setup3},wasmAvgPool;function setup4(backend2){wasmAvgPool=backend2.wasm.cwrap(AvgPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function avgPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,channels=convInfo.inChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);if(convInfo.dilationWidth!==1||convInfo.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmAvgPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,strideHeight,strideWidth,channels,outId),out}var avgPoolConfig3={kernelName:AvgPool,backendName:"wasm",setupFunc:setup4,kernelFunc:avgPool4};function reshape5(args){let{inputs,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize);return util_exports.assert(xSize===util_exports.sizeFromShape($shape),()=>`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape5},wasmBatchMatMul;function setup5(backend2){wasmBatchMatMul=backend2.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape5({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape5({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),a3dId=backend2.dataIdMap.get(a3d.dataId).id,b3dId=backend2.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend2.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast5(args){let{inputs:{x},attrs:{dtype},backend:backend2}=args,out=backend2.makeOutput(x.shape,dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast5},wasmClip;function setup6(backend2){wasmClip=backend2.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend2}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity4({inputs:{x:$inputs[0]},backend:backend2});let out=backend2.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape5({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend2.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend2.typedArrayFromHeap(input2)),outVals=backend2.typedArrayFromHeap(out);for(let b=0;b<batchDim;b++){let outOffset=b*sumInnerDims;for(let i=0;i<inVals.length;i++){let innerDim=innerDims[i],inOffset=b*innerDim,vals=inVals[i].subarray(inOffset,inOffset+innerDim);outVals.set(vals,outOffset),outOffset+=innerDim}}return out}var concatConfig3={kernelName:Concat,backendName:"wasm",kernelFunc:concat4},wasmConv2d;function setup7(backend2){wasmConv2d=backend2.wasm.cwrap(Conv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode,dataFormat}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var conv2DConfig3={kernelName:Conv2D,backendName:"wasm",setupFunc:setup7,kernelFunc:conv2d5},wasmConv2DBackpropInput;function setup8(backend2){wasmConv2DBackpropInput=backend2.wasm.cwrap(Conv2DBackpropInput,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2DBackpropInput4(args){let{backend:backend2,inputs,attrs}=args,{dy,filter}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,inputShape}=attrs,dilations=1,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast",dxStrides=util_exports.computeStrides(convInfo.inShape),dyStrides=util_exports.computeStrides(dy.shape),[fltS0,fltS1,fltS2]=util_exports.computeStrides(filter.shape),xBatchStride=dxStrides[0],xRowStride=isChannelsLast?dxStrides[1]:dxStrides[2],xColStride=isChannelsLast?dxStrides[2]:1,xChannelStride=isChannelsLast?1:dxStrides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1],out=backend2.makeOutput(convInfo.inShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,dyId=backend2.dataIdMap.get(dy.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id;return wasmConv2DBackpropInput(dyId,filterId,batchSize,filterHeight,filterWidth,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,strideHeight,strideWidth,topPad,leftPad,fltS0,fltS1,fltS2,xBatchStride,xRowStride,xColStride,xChannelStride,yBatchStride,yRowStride,yColStride,yChannelStride,outId),out}var conv2DBackpropInputConfig3={kernelName:Conv2DBackpropInput,backendName:"wasm",setupFunc:setup8,kernelFunc:conv2DBackpropInput4},cosConfig3=createUnaryKernelConfig(Cos),InterpolationMethod;(function(InterpolationMethod2){InterpolationMethod2[InterpolationMethod2.bilinear=0]="bilinear",InterpolationMethod2[InterpolationMethod2.nearest=1]="nearest"})(InterpolationMethod||(InterpolationMethod={}));var wasmCropAndResize;function setup9(backend2){wasmCropAndResize=backend2.wasm.cwrap(CropAndResize,null,["number","number","number","number","array","number","number","number","number","number"])}function cropAndResize4(args){let{backend:backend2,inputs,attrs}=args,{method,extrapolationValue,cropSize}=attrs,{image:image3,boxes,boxInd}=inputs,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,outShape=[numBoxes,cropHeight,cropWidth,image3.shape[3]],imagesData=backend2.dataIdMap.get(image3.dataId),castedData;image3.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:image3},attrs:{dtype:"float32"}}),imagesData=backend2.dataIdMap.get(castedData.dataId));let imagesId=imagesData.id,boxesId=backend2.dataIdMap.get(boxes.dataId).id,boxIndId=backend2.dataIdMap.get(boxInd.dataId).id,out=backend2.makeOutput(outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,imagesShapeBytes=new Uint8Array(new Int32Array(image3.shape).buffer);return wasmCropAndResize(imagesId,boxesId,boxIndId,numBoxes,imagesShapeBytes,cropHeight,cropWidth,InterpolationMethod[method],extrapolationValue,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var cropAndResizeConfig3={kernelName:CropAndResize,backendName:"wasm",setupFunc:setup9,kernelFunc:cropAndResize4},wasmCumsum;function setup10(backend2){wasmCumsum=backend2.wasm.cwrap(Cumsum,null,["number","number","number","number","number","number"])}function cumsum4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length;util_exports.assert(x.dtype==="float32"||x.dtype==="int32",()=>`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend2}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend2.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend2.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend2.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend2}),backend2.disposeData(permutedX.dataId),backend2.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4},wasmDepthToSpace;function setup11(backend2){wasmDepthToSpace=backend2.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend2.makeOutput(outputShape,"float32"),xId=backend2.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4},wasmDepthwiseConv2d;function setup12(backend2){wasmDepthwiseConv2d=backend2.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d5},supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool"),expConfig3=createUnaryKernelConfig(Exp);function expandDims5(args){let{inputs,attrs,backend:backend2}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape5({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims5};function fill4(args){let{attrs:{shape,value,dtype},backend:backend2}=args,out=backend2.makeOutput(shape,dtype);return backend2.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4},wasmFlipLeftRight;function setup13(backend2){wasmFlipLeftRight=backend2.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend2}=args,{image:image3}=inputs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},floorConfig3=createUnaryKernelConfig(Floor),supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3),wasmBatchNorm;function setup14(backend2){wasmBatchNorm=backend2.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend2,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale22}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,meanId=backend2.dataIdMap.get(mean4.dataId).id,varianceId=backend2.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend2.dataIdMap.get(offset.dataId).id:0,scaleId=scale22!=null?backend2.dataIdMap.get(scale22.dataId).id:0,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend2){wasmFusedConv2d=backend2.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2},wasmFusedDepthwiseConv2d;function setup16(backend2){wasmFusedDepthwiseConv2d=backend2.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend2){wasmGatherNd=backend2.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend2,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend2.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend2.dataIdMap.get(params.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3},wasmGather;function setup18(backend2){wasmGather=backend2.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend2,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape5({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend2}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape5({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend2}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend2.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend2.dataIdMap.get(flattenX.dataId).id,indicesId=backend2.dataIdMap.get(flattenIndex.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23},supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool"),supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool"),wasmFunc3;function setupFunc2(backend2){wasmFunc3=backend2.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend2}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4},supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool"),logConfig3=createUnaryKernelConfig(Log),supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool"),wasmMax;function setup19(backend2){wasmMax=backend2.wasm.cwrap(Max,null,["number, number, number"])}function max5(args){let{backend:backend2,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend2.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max5},supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9),wasmMaxPool;function setup20(backend2){wasmMaxPool=backend2.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMean;function setup21(backend2){wasmMean=backend2.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast5({backend:backend2,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend2.dataIdMap.get(castedInput.dataId).id);let out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend2.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3},wasmMin;function setup22(backend2){wasmMin=backend2.wasm.cwrap(Min,null,["number, number, number"])}function min5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min5},supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10),supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11),negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend2,resOffset){let result=new Int32Array(backend2.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend2.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend2){wasmFunc4=backend2.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);return backend2.wasm._free(pSelectedScores),backend2.wasm._free(pValidOutputs),backend2.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc},wasmFunc5;function setup24(backend2){wasmFunc5=backend2.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pSelectedScores);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend2.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43},wasmFunc6;function setup25(backend2){wasmFunc6=backend2.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pValidOutputs);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend2.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2},supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool"),wasmOneHot;function setup26(backend2){wasmOneHot=backend2.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend2.makeOutput([...indices.shape,depth],"int32"),outId=backend2.dataIdMap.get(out.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims5({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims5({inputs:{input:t},backend:backend2,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend2,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3},wasmPadV2;function setup27(backend2){wasmPadV2=backend2.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend2,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(outShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27},supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13),wasmPrelu;function setup28(backend2){wasmPrelu=backend2.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu5(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,weightsId=backend2.dataIdMap.get(alpha.dataId).id,out=backend2.makeOutput(x.shape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu5},wasmProd;function setup29(backend2){wasmProd=backend2.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4},range5=args=>{let{backend:backend2,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend2.makeOutput([values.length],dtype);return backend2.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range5},supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14),reluConfig3=createUnaryKernelConfig(Relu),relu6Config3=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup30(backend2){wasmResizeBilinear=backend2.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend2,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend2.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend2.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4},wasmReverse;function setup31(backend2){wasmReverse=backend2.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity4({inputs:{x},backend:backend2});let out=backend2.makeOutput(x.shape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape5({inputs:{x:out},attrs:{shape:x.shape},backend:backend2})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31},wasmRotate;function setup32(backend2){wasmRotate=backend2.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend2,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32},roundConfig3=createUnaryKernelConfig(Round),rsqrtConfig3=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup33(backend2){wasmScatterNd=backend2.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend2,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend2.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend2.dataIdMap.get(indices.dataId).id,updatesId=backend2.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3},wasmSelect;function setup34(backend2){wasmSelect=backend2.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs,conditionId=backend2.dataIdMap.get(condition.dataId).id,tId=backend2.dataIdMap.get(t.dataId).id,eId=backend2.dataIdMap.get(e.dataId).id,out=backend2.makeOutput(t.shape,t.dtype),outId=backend2.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34},wasmFunc7;function setup35(backend2){wasmFunc7=backend2.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4},sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend2}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend2.readSync(x.dataId),out=backend2.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend2.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend2.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend2.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i<endI;i++){let xOffset=i*xStride+beginJ;outVals.set(xVals.subarray(xOffset,xOffset+size[1]),outOffset),outOffset+=size[1]}}function slice3d2(xVals,xStride1,xStride2,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++){let xOffset=i*xStride1+j*xStride2+beginK;outVals.set(xVals.subarray(xOffset,xOffset+size[2]),outOffset),outOffset+=size[2]}}function slice4d2(xVals,xStride1,xStride2,xStride3,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1],endK=beginK+size[2],beginL=begin[3];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++)for(let k=beginK;k<endK;k++){let xOffset=i*xStride1+j*xStride2+k*xStride3+beginL;outVals.set(xVals.subarray(xOffset,xOffset+size[3]),outOffset),outOffset+=size[3]}}var sliceConfig3={kernelName:Slice,backendName:"wasm",kernelFunc:slice4},wasmFunc8;function setup36(backend2){wasmFunc8=backend2.wasm.cwrap(Softmax,null,["number","number","number","number"])}function softmax5(args){let{backend:backend2,inputs:{logits},attrs:{dim}}=args,xId=backend2.dataIdMap.get(logits.dataId).id,out=backend2.makeOutput(logits.shape,logits.dtype),outId=backend2.dataIdMap.get(out.dataId).id,channels=logits.shape[dim],batch=util_exports.sizeFromShape(logits.shape)/channels;return util_exports.sizeFromShape(out.shape)===0||wasmFunc8(xId,outId,channels,batch),out}var softmaxConfig3={kernelName:Softmax,backendName:"wasm",setupFunc:setup36,kernelFunc:softmax5};function splitV3(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend2});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3},sqrtConfig3=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup37(backend2){wasmStridedSlice=backend2.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape5({inputs:{x},attrs:{shape:newShape},backend:backend2}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend2});return reshape5({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend2})}let out=backend2.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend2.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape5({inputs:{x:out},attrs:{shape:outShape},backend:backend2})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup38(backend2){wasmSum=backend2.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5},tanhConfig3=createUnaryKernelConfig(Tanh),wasmTile;function setup39(backend2){wasmTile=backend2.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile5(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[i]*reps[i];let xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),newShapeBytes=new Uint8Array(new Int32Array(newShape).buffer),out=backend2.makeOutput(newShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmTile(xId,xShapeBytes,x.shape.length,newShapeBytes,newShape.length,CppDType[out.dtype],outId),out}var tileConfig3={kernelName:Tile,backendName:"wasm",setupFunc:setup39,kernelFunc:tile5},wasmTopK;function setup40(backend2){wasmTopK=backend2.wasm.cwrap(TopK,null,["number","array","number","number","number","bool","number","number"])}var topk2=({inputs,backend:backend2,attrs})=>{let{x}=inputs,{k,sorted}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend2.makeOutput(outputShape,x.dtype),outValuesId=backend2.dataIdMap.get(outValues.dataId).id,outIndices=backend2.makeOutput(outputShape,"int32"),outIndicesId=backend2.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i<rank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let outs=new Array(numOutputs),begin=new Array(rank).fill(0),size=value.shape.slice();size[axis]=1;for(let i=0;i<outs.length;i++)begin[axis]=i,outs[i]=slice4({inputs:{x:value},attrs:{begin,size},backend:backend2});return outs.map(({dataId,dtype})=>({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4},kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,roundConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data=this.dataIdMap.get(dataId);this.wasm._free(data.memoryOffset),this.wasm.tfjs.disposeData(data.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.3",WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);var draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven2,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range6,round:()=>round5});var Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!=0}function isEven2(num){return num%2==0}function round5(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale3=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale3),Math.round(height*scale3))}function getCenterPoint(pts){return pts.reduce((sum6,pt)=>sum6.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range6(num,start,step4){return Array(num).fill(0).map((_,i)=>start+i*step4)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}};var Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}};var BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}};var FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min6,x)=>x<min6?x:min6,Infinity),minY=ys.reduce((min6,y)=>y<min6?y:min6,Infinity),maxX=xs.reduce((max6,x)=>max6<x?x:max6,0),maxY=ys.reduce((max6,y)=>max6<y?y:max6,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression2(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}function normalize(x,meanRgb){return tidy(()=>{let[r,g,b]=meanRgb,avg_r=fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=concat([avg_r,avg_g,avg_b],3);return sub(x,avg_rgb)})}function padToSquare(imgTensor,isCenterImage=!1){return tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>cast(t,"float32"));return concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array2=inputArray.slice();for(let i=array2.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array2[i];array2[i]=array2[j],array2[j]=x}return array2}function sigmoid5(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}};var relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round5(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json18){let descriptors=json18.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json18.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch3=window.fetch;if(!fetch3)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:fetch3,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer2)=>err?reject(err):resolve(buffer2))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch3=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch:fetch3,...fileSystem}}function isBrowser2(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env3){environment=env3}function initialize(){return isBrowser2()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env3){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env3;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env3.createCanvasElement||(()=>new Canvas),environment.createImageElement=env3.createImageElement||(()=>new Image),environment.ImageData=env3.ImageData||environment.ImageData,environment.Video=env3.Video||environment.Video,environment.fetch=env3.fetch||environment.fetch,environment.readFile=env3.readFile||environment.readFile}var env2={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser:isBrowser2,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env2.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env2.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}};var DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round5(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}function isMediaLoaded(media){let{Image,Video}=env2.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env2.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env2.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input2){let{Image,Video}=env2.getEnv();return input2 instanceof Image?new Dimensions(input2.naturalWidth,input2.naturalHeight):input2 instanceof Video?new Dimensions(input2.videoWidth,input2.videoHeight):new Dimensions(input2.width,input2.height)}function createCanvas2({width,height}){let{createCanvasElement}=env2.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env2.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas2({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env2.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await browser_exports.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input2){let{Image,Canvas,Video}=env2.getEnv();return input2 instanceof Image||input2 instanceof Canvas||input2 instanceof Video}function imageToSquare(input2,inputSize,centerImage=!1){let{Image,Canvas}=env2.getEnv();if(!(input2 instanceof Image||input2 instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input2),scale3=inputSize/Math.max(dims.height,dims.width),width=scale3*dims.width,height=scale3*dims.height,targetCanvas=createCanvas2({width:inputSize,height:inputSize}),inputCanvas=input2 instanceof Canvas?input2:createCanvasFromMedia(input2),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input2,idx)=>{if(isTensor3D(input2)){this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape;return}if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape.slice(1);return}let canvas=input2 instanceof env2.getEnv().Canvas?input2:createCanvasFromMedia(input2);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range6(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tidy(()=>{let inputTensors=range6(this.batchSize,0,1).map(batchIdx=>{let input2=this.getInput(batchIdx);if(input2 instanceof Tensor){let imgTensor=isTensor4D(input2)?input2:input2.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input2 instanceof env2.getEnv().Canvas)return browser_exports.fromPixels(imageToSquare(input2,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input2}`)});return stack(inputTensors.map(t=>cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input2,i)=>{if(!isMediaElement(input2)&&!isTensor3D(input2)&&!isTensor4D(input2))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input2=>isMediaElement(input2)&&awaitMediaLoaded(input2))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input2,detections){let{Canvas}=env2.getEnv(),canvas=input2;if(!(input2 instanceof Canvas)){let netInput=await toNetInput(input2);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas2({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init2){let{fetch:fetch3}=env2.getEnv(),res=await fetch3(url,init2);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return io_exports.loadWeights(manifest,modelBaseUri)}function matchDimensions(input2,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input2.width=width,input2.height=height,{width,height}}var NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable2})=>{let tensor2=tensor(variable2.dataSync());variable2.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env2.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights2=io_exports.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights2(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}};function depthwiseSeparableConv(x,params,stride){return tidy(()=>{let out=separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=add2(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return relu(add2(out1,add2(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=relu(add2(out1,add2(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return relu(add2(out1,add2(out2,add2(out3,out4))))})}function convLayer(x,params,padding="same",withRelu=!1){return tidy(()=>{let out=add2(conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}};function fullyConnectedLayer(x,params){return tidy(()=>add2(matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}};var FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input2){return tidy(()=>softmax(this.runNet(input2)))}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictExpressions(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round5(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version10="0.10.2";function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range6(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range6(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return add2(conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=maxPool(out,[3,3],[2,2],"same"),out=add2(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv2,[1,1]),out=add2(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(256)),out=relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range6(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2,pooled=avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input2){return tidy(()=>{let{age,gender}=this.runNet(input2);return{age,gender:softmax(gender)}})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictAgeAndGender(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),ages=unstack(out.age),genders=unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale3=inputSize/Math.max(height,width);return{width:width*scale3,height:height*scale3}}),batchSize=inputDimensions.length;return tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>stack([fill([68],fillX,"float32"),fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding2=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding2(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding2(batchIdx,(w,h)=>h<w);return output.mul(fill([batchSize,136],inputSize,"float32")).sub(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input2){return tidy(()=>{let out=this.runNet(input2);return this.postProcess(out,input2.inputSize,input2.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async detectLandmarks(input2){let netInput=await toNetInput(input2),landmarkTensors=tidy(()=>unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven2(i)),yCoords=landmarksArray.filter((_,i)=>!isEven2(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}};var FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}};var FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var FaceLandmarkNet=class extends FaceLandmark68Net{};function scale2(x,params){return add2(mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=conv2d(x,filters,strides,padding);return out=add2(out,bias),out=scale2(out,params.scale),withRelu?relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tidy(()=>transpose(tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tensor1d(extractWeights(numWeights)),biases=tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale3=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale3}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tidy(()=>transpose(tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale3=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale3}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=add2(out,x),out=relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=avgPool(x,2,2,"valid"),zeros4=zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=zeros(padShapeX);out=concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=zeros(padShapeY);out=concat([out,zerosH],2)}return pooled=isPad?concat([pooled,zeros4],3):pooled,out=add2(pooled,out),out=relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(256)),out=convDown(normalized,params.conv32_down);out=maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return matMul(globalAvg,params.fc)})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async computeFaceDescriptor(input2){let netInput=await toNetInput(input2),faceDescriptorTensors=tidy(()=>unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tensor1d(extractWeights(numChannels)),batch_norm_offset=tensor1d(extractWeights(numChannels)),batch_norm_mean=tensor1d(extractWeights(numChannels)),batch_norm_variance=tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function pointwiseConvLayer(x,params,strides){return tidy(()=>{let out=conv2d(x,params.filters,strides,"same");return out=add2(out,params.batch_norm_offset),clipByValue(out,0,6)})}var epsilon3=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tidy(()=>{let out=depthwiseConv2d(x,params.filters,strides,"same");return out=batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon3),clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression3(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(selected.length>=outputSize)return;let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}),selected}function getCenterCoordinatesAndSizesLayer(x){let vec=unstack(transpose(x,[1,0])),sizes=[sub(vec[2],vec[0]),sub(vec[3],vec[1])],centers=[add2(vec[0],div(sizes[0],scalar(2))),add2(vec[1],div(sizes[1],scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=unstack(transpose(x1,[1,0])),div0_out=div(mul(exp(div(vec[2],scalar(5))),sizes[0]),scalar(2)),add0_out=add2(mul(div(vec[0],scalar(10)),sizes[0]),centers[0]),div1_out=div(mul(exp(div(vec[3],scalar(5))),sizes[1]),scalar(2)),add1_out=add2(mul(div(vec[1],scalar(10)),sizes[1]),centers[1]);return transpose(stack([sub(add0_out,div0_out),sub(add1_out,div1_out),add2(add0_out,div0_out),add2(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(reshape(tile(params.extra_dim,[batchSize,1,1]),[-1,4]),reshape(boxPredictions,[-1,4]));boxes=reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=sigmoid(slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=unstack(boxes),scoresByBatch=unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}function boxPredictionLayer(x,params){return tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input2){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(512,!1),"float32"),x=sub(mul(batchTensor,scalar(.007843137718737125)),scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async locateFaces(input2,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input2),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression3(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{};var IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME2="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model";var isNumber2=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber2(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber2(a.x)&&isNumber2(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber2)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}function leaky(x){return tidy(()=>{let min6=mul(x,scalar(.10000000149011612));return add2(relu(sub(x,min6)),min6)})}function convWithBatchNorm(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=conv2d(out,params.conv.filters,[1,1],"valid"),out=sub(out,params.bn.sub),out=mul(out,params.bn.truediv),out=add2(out,params.conv.bias),leaky(out)})}function depthwiseSeparableConv2(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=add2(out,params.bias),leaky(out)})}function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub4=tensor1d(extractWeights(size)),truediv=tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub4,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub4=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub4,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(x,params){let out=convWithBatchNorm(x,params.conv0);return out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input2,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input2,inputSize){return this.forwardInput(await toNetInput(input2),inputSize)}async detect(input2,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input2),out=await this.forwardInput(netInput,inputSize),out0=tidy(()=>unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression2(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid5(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid5(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid5(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max6,curr)=>max6.classScore>curr.classScore?max6:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){return(await this.detect(input2,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME2}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function extractAllFacesAndComputeResults(parentResults,input2,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input2 instanceof Tensor?await extractFaceTensors(input2,faceBoxes):await extractFaces(input2,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input2,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input2,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404];var TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){return(await this.detect(input2,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};var nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input2,options)=>nets.ssdMobilenetv1.locateFaces(input2,options),tinyFaceDetector=(input2,options)=>nets.tinyFaceDetector.locateFaces(input2,options),tinyYolov2=(input2,options)=>nets.tinyYolov2.locateFaces(input2,options),detectFaceLandmarks=input2=>nets.faceLandmark68Net.detectLandmarks(input2),detectFaceLandmarksTiny=input2=>nets.faceLandmark68TinyNet.detectLandmarks(input2),computeFaceDescriptor=input2=>nets.faceRecognitionNet.computeFaceDescriptor(input2),recognizeFaceExpressions=input2=>nets.faceExpressionNet.predictExpressions(input2),predictAgeAndGender=input2=>nets.ageGenderNet.predictAgeAndGender(input2),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks;var PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input2){super();this.parentTask=parentTask;this.input=input2}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}};var DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input2,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input2;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{detection}=parentResult,faces=this.input instanceof Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var DetectFacesTaskBase=class extends ComposableTask{constructor(input2,options=new SsdMobilenetv1Options){super();this.input=input2;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input:input2,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input3=>nets.tinyFaceDetector.locateFaces(input3,options):options instanceof SsdMobilenetv1Options?input3=>nets.ssdMobilenetv1.locateFaces(input3,options):options instanceof TinyYolov2Options?input3=>nets.tinyYolov2.locateFaces(input3,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input2)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input2,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input2,options)}function detectAllFaces(input2,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input2,options)}async function allFacesSsdMobilenetv1(input2,minConfidence){return detectAllFaces(input2,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input2,forwardParams={}){return detectAllFaces(input2,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count2=1,createUniqueLabel=()=>`person ${count2++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json18){let labeledDescriptors=json18.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json18.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version11={faceapi:version10,node,browser};export{AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas2 as createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw_exports as draw,env2 as env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression2 as nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid5 as sigmoid,ssdMobilenetv1,tfjs_esm_exports as tf,tinyFaceDetector,tinyYolov2,toNetInput,utils_exports as utils,validateConfig,version11 as version};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -4203,5 +4203,21 @@ return a / b;`,DIV_PACKED=`
  * limitations under the License.
  * =============================================================================
  */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
 /** @license See the LICENSE file. */
 //# sourceMappingURL=face-api.esm.js.map
diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map
index 660be01..8a2167c 100644
--- a/dist/face-api.esm.js.map
+++ b/dist/face-api.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["../src/env/isNodejs.ts", "empty:/home/vlado/dev/face-api/node_modules/node-fetch/browser.js", "../node_modules/seedrandom/lib/alea.js", "../node_modules/seedrandom/lib/xor128.js", "../node_modules/seedrandom/lib/xorwow.js", "../node_modules/seedrandom/lib/xorshift7.js", "../node_modules/seedrandom/lib/xor4096.js", "../node_modules/seedrandom/lib/tychei.js", "empty:crypto", "../node_modules/seedrandom/seedrandom.js", "../node_modules/seedrandom/index.js", "empty:/home/vlado/dev/face-api/node_modules/string_decoder/lib/string_decoder.js", "empty:path", "empty:worker_threads", "empty:perf_hooks", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js", "../node_modules/@tensorflow/tfjs-core/src/backends/backend.ts", "../node_modules/@tensorflow/tfjs-core/src/util_base.ts", "../node_modules/@tensorflow/tfjs-core/src/environment.ts", "../node_modules/@tensorflow/tfjs-core/src/global_util.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_names.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/util.ts", "../node_modules/@tensorflow/tfjs-core/src/profiler.ts", "../node_modules/@tensorflow/tfjs-core/src/tape.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_format.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util.ts", "../node_modules/@tensorflow/tfjs-core/src/types.ts", "../node_modules/@tensorflow/tfjs-core/src/engine.ts", "../node_modules/@tensorflow/tfjs-core/src/device_util.ts", "../node_modules/@tensorflow/tfjs-core/src/flags.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util_env.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/operation.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/complex.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor_ops_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/io/types.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io_utils.ts", "../node_modules/@tensorflow/tfjs-core/src/io/router_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/io/indexed_db.ts", "../node_modules/@tensorflow/tfjs-core/src/io/local_storage.ts", "../node_modules/@tensorflow/tfjs-core/src/io/model_management.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_browser.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_node.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/buffer.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cast.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clone.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/print.ts", "../node_modules/@tensorflow/tfjs-core/src/base_side_effects.ts", 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"../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Pack.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/PadV2.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Pow.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Prelu.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Prod.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Range.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/RealDiv.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Relu.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Relu6.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ResizeBilinear.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Reverse.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/RotateWithOffset.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Rsqrt.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ScatterNd.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Select.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sigmoid.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sin.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Slice.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Softmax.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/SplitV.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sqrt.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Square.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/SquaredDifference.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/StridedSlice.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sub.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sum.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Tanh.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Tile.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/TopK.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Unpack.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ZerosLike.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/register_all_kernels.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/flags_wasm.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/backend_wasm.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js", "../node_modules/@tensorflow/tfjs-backend-wasm/src/version.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/base.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
-  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var _scriptDir=undefined;var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":167,\"maximum\":167+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var 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assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Floor\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"LeakyRelu\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"Mean\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"Neg\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Prod\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"RealDiv\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"TopK\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  // eslint-disable-next-line no-mixed-operators\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n\n    const { readFile } = env.getEnv();\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)),\n    );\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParamsFromWeigthMap(weightMap);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParams(weights);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input);\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      (t) => t.data(),\n    )) as Float32Array[];\n\n    faceDescriptorTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) {\n      return;\n    }\n    const originalScore = c.score;\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n\n    return {\n      boxPredictionEncoding,\n      classPrediction,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    // TODO batches\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data());\n\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n    // return tf.maximum(x, min)\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024,\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config';\nexport * from './types';\n\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\n// eslint-disable-next-line no-undef\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n"],
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"../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
+  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":169,\"maximum\":169+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"Ba\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Ca\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var 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_free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"_a\"]).apply(null,arguments)};var _emscripten_get_global_libc=Module[\"_emscripten_get_global_libc\"]=function(){return(_emscripten_get_global_libc=Module[\"_emscripten_get_global_libc\"]=Module[\"asm\"][\"$a\"]).apply(null,arguments)};var ___errno_location=Module[\"___errno_location\"]=function(){return(___errno_location=Module[\"___errno_location\"]=Module[\"asm\"][\"ab\"]).apply(null,arguments)};var ___em_js__initPthreadsJS=Module[\"___em_js__initPthreadsJS\"]=function(){return(___em_js__initPthreadsJS=Module[\"___em_js__initPthreadsJS\"]=Module[\"asm\"][\"bb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"cb\"]).apply(null,arguments)};var 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_emscripten_sync_run_in_main_thread_5=Module[\"_emscripten_sync_run_in_main_thread_5\"]=function(){return(_emscripten_sync_run_in_main_thread_5=Module[\"_emscripten_sync_run_in_main_thread_5\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_6=Module[\"_emscripten_sync_run_in_main_thread_6\"]=function(){return(_emscripten_sync_run_in_main_thread_6=Module[\"_emscripten_sync_run_in_main_thread_6\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_7=Module[\"_emscripten_sync_run_in_main_thread_7\"]=function(){return(_emscripten_sync_run_in_main_thread_7=Module[\"_emscripten_sync_run_in_main_thread_7\"]=Module[\"asm\"][\"sb\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"tb\"]).apply(null,arguments)};var _emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=function(){return(_emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=Module[\"asm\"][\"ub\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"vb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"wb\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"xb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"yb\"]).apply(null,arguments)};var dynCall_vi=Module[\"dynCall_vi\"]=function(){return(dynCall_vi=Module[\"dynCall_vi\"]=Module[\"asm\"][\"zb\"]).apply(null,arguments)};var dynCall_v=Module[\"dynCall_v\"]=function(){return(dynCall_v=Module[\"dynCall_v\"]=Module[\"asm\"][\"Ab\"]).apply(null,arguments)};var dynCall_ii=Module[\"dynCall_ii\"]=function(){return(dynCall_ii=Module[\"dynCall_ii\"]=Module[\"asm\"][\"Bb\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"_pthread_self\"]=_pthread_self;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(!ENVIRONMENT_IS_PTHREAD)noExitRuntime=true;if(!ENVIRONMENT_IS_PTHREAD)run();\n\n\n  return WasmBackendModuleThreadedSimd\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModuleThreadedSimd;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModuleThreadedSimd; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n    ", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new 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assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function 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callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  // eslint-disable-next-line no-mixed-operators\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n\n    const { readFile } = env.getEnv();\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)),\n    );\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParamsFromWeigthMap(weightMap);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParams(weights);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input);\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      (t) => t.data(),\n    )) as Float32Array[];\n\n    faceDescriptorTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) {\n      return;\n    }\n    const originalScore = c.score;\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n\n    return {\n      boxPredictionEncoding,\n      classPrediction,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    // TODO batches\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data());\n\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n    // return tf.maximum(x, min)\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024,\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config';\nexport * from './types';\n\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\n// eslint-disable-next-line no-undef\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n"],
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diff --git a/dist/face-api.esm.json b/dist/face-api.esm.json
index 528e325..c037868 100644
--- a/dist/face-api.esm.json
+++ b/dist/face-api.esm.json
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diff --git a/dist/face-api.js b/dist/face-api.js
index 8b247f5..6a6c3a2 100644
--- a/dist/face-api.js
+++ b/dist/face-api.js
@@ -5,43 +5,43 @@
   author: <https://github.com/vladmandic>'
   */
 
-var faceapi=(()=>{var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module),require_isNodejs=__commonJS((exports,module)=>{__export(exports,{isNodejs:()=>isNodejs2});function isNodejs2(){return typeof global=="object"&&!0&&typeof module!="undefined"&&typeof process!="undefined"&&!!process.version}}),src_exports={};__export(src_exports,{AgeGenderNet:()=>AgeGenderNet,BoundingBox:()=>BoundingBox,Box:()=>Box,ComposableTask:()=>ComposableTask,ComputeAllFaceDescriptorsTask:()=>ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase:()=>ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask:()=>ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask:()=>DetectAllFaceLandmarksTask,DetectAllFacesTask:()=>DetectAllFacesTask,DetectFaceLandmarksTaskBase:()=>DetectFaceLandmarksTaskBase,DetectFacesTaskBase:()=>DetectFacesTaskBase,DetectSingleFaceLandmarksTask:()=>DetectSingleFaceLandmarksTask,DetectSingleFaceTask:()=>DetectSingleFaceTask,Dimensions:()=>Dimensions,FACE_EXPRESSION_LABELS:()=>FACE_EXPRESSION_LABELS,FaceDetection:()=>FaceDetection,FaceDetectionNet:()=>FaceDetectionNet,FaceExpressionNet:()=>FaceExpressionNet,FaceExpressions:()=>FaceExpressions,FaceLandmark68Net:()=>FaceLandmark68Net,FaceLandmark68TinyNet:()=>FaceLandmark68TinyNet,FaceLandmarkNet:()=>FaceLandmarkNet,FaceLandmarks:()=>FaceLandmarks,FaceLandmarks5:()=>FaceLandmarks5,FaceLandmarks68:()=>FaceLandmarks68,FaceMatch:()=>FaceMatch,FaceMatcher:()=>FaceMatcher,FaceRecognitionNet:()=>FaceRecognitionNet,Gender:()=>Gender,LabeledBox:()=>LabeledBox,LabeledFaceDescriptors:()=>LabeledFaceDescriptors,NetInput:()=>NetInput,NeuralNetwork:()=>NeuralNetwork,ObjectDetection:()=>ObjectDetection,Point:()=>Point,PredictedBox:()=>PredictedBox,Rect:()=>Rect,SsdMobilenetv1:()=>SsdMobilenetv1,SsdMobilenetv1Options:()=>SsdMobilenetv1Options,TinyFaceDetector:()=>TinyFaceDetector,TinyFaceDetectorOptions:()=>TinyFaceDetectorOptions,TinyYolov2:()=>TinyYolov2,TinyYolov2Options:()=>TinyYolov2Options,allFaces:()=>allFaces,allFacesSsdMobilenetv1:()=>allFacesSsdMobilenetv1,allFacesTinyYolov2:()=>allFacesTinyYolov2,awaitMediaLoaded:()=>awaitMediaLoaded,bufferToImage:()=>bufferToImage,computeFaceDescriptor:()=>computeFaceDescriptor,createCanvas:()=>createCanvas2,createCanvasFromMedia:()=>createCanvasFromMedia,createFaceDetectionNet:()=>createFaceDetectionNet,createFaceRecognitionNet:()=>createFaceRecognitionNet,createSsdMobilenetv1:()=>createSsdMobilenetv1,createTinyFaceDetector:()=>createTinyFaceDetector,createTinyYolov2:()=>createTinyYolov2,detectAllFaces:()=>detectAllFaces,detectFaceLandmarks:()=>detectFaceLandmarks,detectFaceLandmarksTiny:()=>detectFaceLandmarksTiny,detectLandmarks:()=>detectLandmarks,detectSingleFace:()=>detectSingleFace,draw:()=>draw_exports,env:()=>env2,euclideanDistance:()=>euclideanDistance,extendWithAge:()=>extendWithAge,extendWithFaceDescriptor:()=>extendWithFaceDescriptor,extendWithFaceDetection:()=>extendWithFaceDetection,extendWithFaceExpressions:()=>extendWithFaceExpressions,extendWithFaceLandmarks:()=>extendWithFaceLandmarks,extendWithGender:()=>extendWithGender,extractFaceTensors:()=>extractFaceTensors,extractFaces:()=>extractFaces,fetchImage:()=>fetchImage,fetchJson:()=>fetchJson,fetchNetWeights:()=>fetchNetWeights,fetchOrThrow:()=>fetchOrThrow,getContext2dOrThrow:()=>getContext2dOrThrow,getMediaDimensions:()=>getMediaDimensions,imageTensorToCanvas:()=>imageTensorToCanvas,imageToSquare:()=>imageToSquare,inverseSigmoid:()=>inverseSigmoid,iou:()=>iou,isMediaElement:()=>isMediaElement,isMediaLoaded:()=>isMediaLoaded,isWithAge:()=>isWithAge,isWithFaceDetection:()=>isWithFaceDetection,isWithFaceExpressions:()=>isWithFaceExpressions,isWithFaceLandmarks:()=>isWithFaceLandmarks,isWithGender:()=>isWithGender,loadAgeGenderModel:()=>loadAgeGenderModel,loadFaceDetectionModel:()=>loadFaceDetectionModel,loadFaceExpressionModel:()=>loadFaceExpressionModel,loadFaceLandmarkModel:()=>loadFaceLandmarkModel,loadFaceLandmarkTinyModel:()=>loadFaceLandmarkTinyModel,loadFaceRecognitionModel:()=>loadFaceRecognitionModel,loadSsdMobilenetv1Model:()=>loadSsdMobilenetv1Model,loadTinyFaceDetectorModel:()=>loadTinyFaceDetectorModel,loadTinyYolov2Model:()=>loadTinyYolov2Model,loadWeightMap:()=>loadWeightMap,locateFaces:()=>locateFaces,matchDimensions:()=>matchDimensions,minBbox:()=>minBbox,nets:()=>nets,nonMaxSuppression:()=>nonMaxSuppression2,normalize:()=>normalize,padToSquare:()=>padToSquare,predictAgeAndGender:()=>predictAgeAndGender,recognizeFaceExpressions:()=>recognizeFaceExpressions,resizeResults:()=>resizeResults,resolveInput:()=>resolveInput,shuffleArray:()=>shuffleArray,sigmoid:()=>sigmoid5,ssdMobilenetv1:()=>ssdMobilenetv1,tf:()=>tfjs_esm_exports,tinyFaceDetector:()=>tinyFaceDetector,tinyYolov2:()=>tinyYolov2,toNetInput:()=>toNetInput,utils:()=>utils_exports,validateConfig:()=>validateConfig,version:()=>version11});var 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k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,k==strseed.length&&(me.d=me.x<<10^me.x>>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift7=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j<seed2.length;++j)X[j&7]=X[j&7]<<15^seed2.charCodeAt(j)+X[j+1&7]<<13;for(;X.length<8;)X.push(0);for(j=0;j<8&&X[j]===0;++j);for(j==8?w=X[7]=-1:w=X[j],me2.x=X,me2.i=0,j=256;j>0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor4096=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j<limit;++j)seed2&&(v^=seed2.charCodeAt((j+32)%seed2.length)),j===0&&(w=v),v^=v<<10,v^=v>>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&&copy(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten4(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten4(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser2=global2.navigator,plugins=browser2&&browser2.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool3)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool3),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)}),require_seedrandom2=__commonJS2((exports,module)=>{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_string_decoder=__commonJS2(()=>{}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data=read(f,"binary"),assert3(typeof data=="object"),data)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:167,maximum:167+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack2===0&&(stack2=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack2!==0&&stackRestore(stack2),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,str="";!(idx>=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i<str.length;++i){var u=str.charCodeAt(i);u>=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i<pthreadPoolSize;++i)PThread.allocateUnusedWorker();PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i],pthread=worker.pthread;PThread.freeThreadData(pthread),worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(pthread){if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data){worker.onmessage({data})}),worker.on("error",function(data){worker.onerror(data)}),worker.on("exit",function(data){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()<t;);}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop,STACK_MAX=stackMax,stackRestore(stackTop)}Module.establishStackSpace=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module.getNoExitRuntime=getNoExitRuntime;function ___assert_fail(condition,filename,line,func2){abort("Assertion failed: "+UTF8ToString(condition)+", at: "+[filename?UTF8ToString(filename):"unknown filename",line,func2?UTF8ToString(func2):"unknown function"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;ENVIRONMENT_IS_NODE?_emscripten_get_now=function(){var t=process.hrtime();return t[0]*1e3+t[1]/1e6}:ENVIRONMENT_IS_PTHREAD?_emscripten_get_now=function(){return performance.now()-Module.__performance_now_clock_drift}:typeof dateNow!="undefined"?_emscripten_get_now=dateNow:_emscripten_get_now=function(){return performance.now()};function setErrNo(value){return GROWABLE_HEAP_I32()[___errno_location()>>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i<numCallArgs;i++)GROWABLE_HEAP_F64()[b+i]=arguments[2+i];var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);return stackRestore(stack2),ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgs.array||(readAsmConstArgs.array=[]);var args=readAsmConstArgs.array;args.length=0;for(var ch;ch=GROWABLE_HEAP_U8()[sigPtr++];)ch===100||ch===102?(buf=buf+7&~7,args.push(GROWABLE_HEAP_F64()[buf>>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i<numCallArgs;i++)_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i];var isEmAsmConst=index<0,func2=isEmAsmConst?ASM_CONSTS[-index-1]:proxiedFunctionTable[index];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1],varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2],constArgs=readAsmConstArgs(sigPtr,varargPtr);return func2.apply(null,constArgs)}return func2.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{return wasmMemory.grow(size-buffer2.byteLength+65535>>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i)JSEvents.deferredCalls[i].targetFunction==targetFunction&&(JSEvents.deferredCalls.splice(i,1),--i)},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(JSEvents.canPerformEventHandlerRequests())for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1),--i,call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)&&JSEvents._removeHandler(i--)},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture),JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function(event){++JSEvents.inEventHandler,JSEvents.currentEventHandler=eventHandler,JSEvents.runDeferredCalls(),eventHandler.handlerFunc(event),JSEvents.runDeferredCalls(),--JSEvents.inEventHandler};if(eventHandler.callbackfunc)eventHandler.eventListenerFunc=jsEventHandler,eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture),JSEvents.eventHandlers.push(eventHandler),JSEvents.registerRemoveEventListeners();else for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString&&JSEvents._removeHandler(i--)},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave(),varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1);for(var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)},recordError:function(errorCode){GL.lastError||(GL.lastError=errorCode)},getNewId:function(table){for(var ret=GL.counter++,i=table.length;i<ret;i++)table[i]=null;return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count2,string,length){for(var source="",i=0;i<count2;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p2,i),name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1),name.slice(-1)=="]"&&(name=name.slice(0,name.lastIndexOf("[")));var loc=GLctx.getUniformLocation(p2,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id],GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+"["+j+"]";loc=GLctx.getUniformLocation(p2,n),id=GL.getNewId(GL.uniforms),GL.uniforms[id]=loc}}}}},__emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={},a=attributes>>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i<iovcnt;i++){for(var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j]);num+=len}return GROWABLE_HEAP_I32()[pnum>>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 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cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,endPtr=idx;heap[endPtr]&&!(endPtr>=endIdx);)++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder)return UTF8Decoder.decode(heap.subarray(idx,endPtr));for(var str="";idx<endPtr;){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array2,buffer3){HEAP8.set(array2,buffer3)}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATPOSTRUN__=[],runtimeInitialized=!1,runtimeExited=!1;function preRun(){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=!0}function postRun(){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). 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fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){for(var num=0,i=0;i<iovcnt;i++){for(var ptr=HEAP32[iov+i*8>>2],len=HEAP32[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,HEAPU8[ptr+j]);num+=len}return HEAP32[pnum>>2]=num,0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}var asmLibraryArg={emscripten_notify_memory_growth:_emscripten_notify_memory_growth,fd_close:_fd_close,fd_seek:_fd_seek,fd_write:_fd_write,proc_exit:_proc_exit,roundf:_roundf},asm=createWasm();Module.asm=asm;var 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Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config]=value,[backend2]=key.split("_");backend2===backendName&&result.push(config)}return result}function registerKernel(config){let{kernelName,backendName}=config,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config)}function registerGradient(config){let{kernelName}=config;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now(){return env().platform.now()}function fetch2(path,requestInits){return env().platform.fetch(path,requestInits)}function encodeString(s,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.encode(s,encoding)}function decodeString(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.decode(bytes,encoding)}var Profiler=class{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad(name,25),rank=result.rank,size=result.size,shape=rightPad(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape.length;i++){let node2=tape[i],nodeInputs=node2.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node2.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node2.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node2=tape[i],nodeInputs=node2.inputs;for(let j=0;j<node2.outputs.length;j++)if(tensorsLeadToY[node2.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node2.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape.length;i++){let node2=tape[i];if(nodesFromX[node2.id]&&nodesToY[node2.id]){let prunedInputs={};for(let inputName in node2.inputs){let nodeInput=node2.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node2);prunedNode.inputs=prunedInputs,prunedNode.outputs=node2.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients(tensorAccumulatedGradientMap,filteredTape,tidy2,add5){for(let i=filteredTape.length-1;i>=0;i--){let node2=filteredTape[i],dys=[];if(node2.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node2.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);let inputGradients=node2.gradient(dys);for(let inputName in node2.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node2.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
+var faceapi=(()=>{var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);var require_isNodejs=__commonJS((exports,module)=>{__export(exports,{isNodejs:()=>isNodejs2});function isNodejs2(){return typeof global=="object"&&!0&&typeof module!="undefined"&&typeof process!="undefined"&&!!process.version}});var src_exports={};__export(src_exports,{AgeGenderNet:()=>AgeGenderNet,BoundingBox:()=>BoundingBox,Box:()=>Box,ComposableTask:()=>ComposableTask,ComputeAllFaceDescriptorsTask:()=>ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase:()=>ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask:()=>ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask:()=>DetectAllFaceLandmarksTask,DetectAllFacesTask:()=>DetectAllFacesTask,DetectFaceLandmarksTaskBase:()=>DetectFaceLandmarksTaskBase,DetectFacesTaskBase:()=>DetectFacesTaskBase,DetectSingleFaceLandmarksTask:()=>DetectSingleFaceLandmarksTask,DetectSingleFaceTask:()=>DetectSingleFaceTask,Dimensions:()=>Dimensions,FACE_EXPRESSION_LABELS:()=>FACE_EXPRESSION_LABELS,FaceDetection:()=>FaceDetection,FaceDetectionNet:()=>FaceDetectionNet,FaceExpressionNet:()=>FaceExpressionNet,FaceExpressions:()=>FaceExpressions,FaceLandmark68Net:()=>FaceLandmark68Net,FaceLandmark68TinyNet:()=>FaceLandmark68TinyNet,FaceLandmarkNet:()=>FaceLandmarkNet,FaceLandmarks:()=>FaceLandmarks,FaceLandmarks5:()=>FaceLandmarks5,FaceLandmarks68:()=>FaceLandmarks68,FaceMatch:()=>FaceMatch,FaceMatcher:()=>FaceMatcher,FaceRecognitionNet:()=>FaceRecognitionNet,Gender:()=>Gender,LabeledBox:()=>LabeledBox,LabeledFaceDescriptors:()=>LabeledFaceDescriptors,NetInput:()=>NetInput,NeuralNetwork:()=>NeuralNetwork,ObjectDetection:()=>ObjectDetection,Point:()=>Point,PredictedBox:()=>PredictedBox,Rect:()=>Rect,SsdMobilenetv1:()=>SsdMobilenetv1,SsdMobilenetv1Options:()=>SsdMobilenetv1Options,TinyFaceDetector:()=>TinyFaceDetector,TinyFaceDetectorOptions:()=>TinyFaceDetectorOptions,TinyYolov2:()=>TinyYolov2,TinyYolov2Options:()=>TinyYolov2Options,allFaces:()=>allFaces,allFacesSsdMobilenetv1:()=>allFacesSsdMobilenetv1,allFacesTinyYolov2:()=>allFacesTinyYolov2,awaitMediaLoaded:()=>awaitMediaLoaded,bufferToImage:()=>bufferToImage,computeFaceDescriptor:()=>computeFaceDescriptor,createCanvas:()=>createCanvas2,createCanvasFromMedia:()=>createCanvasFromMedia,createFaceDetectionNet:()=>createFaceDetectionNet,createFaceRecognitionNet:()=>createFaceRecognitionNet,createSsdMobilenetv1:()=>createSsdMobilenetv1,createTinyFaceDetector:()=>createTinyFaceDetector,createTinyYolov2:()=>createTinyYolov2,detectAllFaces:()=>detectAllFaces,detectFaceLandmarks:()=>detectFaceLandmarks,detectFaceLandmarksTiny:()=>detectFaceLandmarksTiny,detectLandmarks:()=>detectLandmarks,detectSingleFace:()=>detectSingleFace,draw:()=>draw_exports,env:()=>env2,euclideanDistance:()=>euclideanDistance,extendWithAge:()=>extendWithAge,extendWithFaceDescriptor:()=>extendWithFaceDescriptor,extendWithFaceDetection:()=>extendWithFaceDetection,extendWithFaceExpressions:()=>extendWithFaceExpressions,extendWithFaceLandmarks:()=>extendWithFaceLandmarks,extendWithGender:()=>extendWithGender,extractFaceTensors:()=>extractFaceTensors,extractFaces:()=>extractFaces,fetchImage:()=>fetchImage,fetchJson:()=>fetchJson,fetchNetWeights:()=>fetchNetWeights,fetchOrThrow:()=>fetchOrThrow,getContext2dOrThrow:()=>getContext2dOrThrow,getMediaDimensions:()=>getMediaDimensions,imageTensorToCanvas:()=>imageTensorToCanvas,imageToSquare:()=>imageToSquare,inverseSigmoid:()=>inverseSigmoid,iou:()=>iou,isMediaElement:()=>isMediaElement,isMediaLoaded:()=>isMediaLoaded,isWithAge:()=>isWithAge,isWithFaceDetection:()=>isWithFaceDetection,isWithFaceExpressions:()=>isWithFaceExpressions,isWithFaceLandmarks:()=>isWithFaceLandmarks,isWithGender:()=>isWithGender,loadAgeGenderModel:()=>loadAgeGenderModel,loadFaceDetectionModel:()=>loadFaceDetectionModel,loadFaceExpressionModel:()=>loadFaceExpressionModel,loadFaceLandmarkModel:()=>loadFaceLandmarkModel,loadFaceLandmarkTinyModel:()=>loadFaceLandmarkTinyModel,loadFaceRecognitionModel:()=>loadFaceRecognitionModel,loadSsdMobilenetv1Model:()=>loadSsdMobilenetv1Model,loadTinyFaceDetectorModel:()=>loadTinyFaceDetectorModel,loadTinyYolov2Model:()=>loadTinyYolov2Model,loadWeightMap:()=>loadWeightMap,locateFaces:()=>locateFaces,matchDimensions:()=>matchDimensions,minBbox:()=>minBbox,nets:()=>nets,nonMaxSuppression:()=>nonMaxSuppression2,normalize:()=>normalize,padToSquare:()=>padToSquare,predictAgeAndGender:()=>predictAgeAndGender,recognizeFaceExpressions:()=>recognizeFaceExpressions,resizeResults:()=>resizeResults,resolveInput:()=>resolveInput,shuffleArray:()=>shuffleArray,sigmoid:()=>sigmoid5,ssdMobilenetv1:()=>ssdMobilenetv1,tf:()=>tfjs_esm_exports,tinyFaceDetector:()=>tinyFaceDetector,tinyYolov2:()=>tinyYolov2,toNetInput:()=>toNetInput,utils:()=>utils_exports,validateConfig:()=>validateConfig,version:()=>version11});var 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t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j<seed2.length;++j)X[j&7]=X[j&7]<<15^seed2.charCodeAt(j)+X[j+1&7]<<13;for(;X.length<8;)X.push(0);for(j=0;j<8&&X[j]===0;++j);for(j==8?w=X[7]=-1:w=X[j],me2.x=X,me2.i=0,j=256;j>0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor4096=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j<limit;++j)seed2&&(v^=seed2.charCodeAt((j+32)%seed2.length)),j===0&&(w=v),v^=v<<10,v^=v>>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&&copy(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten4(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten4(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser2=global2.navigator,plugins=browser2&&browser2.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool3)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool3),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)}),require_seedrandom2=__commonJS2((exports,module)=>{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_string_decoder=__commonJS2(()=>{}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data=read(f,"binary"),assert3(typeof data=="object"),data)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack2===0&&(stack2=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack2!==0&&stackRestore(stack2),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,str="";!(idx>=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i<str.length;++i){var u=str.charCodeAt(i);u>=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i<pthreadPoolSize;++i)PThread.allocateUnusedWorker();PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i],pthread=worker.pthread;PThread.freeThreadData(pthread),worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(pthread){if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data){worker.onmessage({data})}),worker.on("error",function(data){worker.onerror(data)}),worker.on("exit",function(data){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()<t;);}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop,STACK_MAX=stackMax,stackRestore(stackTop)}Module.establishStackSpace=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module.getNoExitRuntime=getNoExitRuntime;function ___assert_fail(condition,filename,line,func2){abort("Assertion failed: "+UTF8ToString(condition)+", at: "+[filename?UTF8ToString(filename):"unknown filename",line,func2?UTF8ToString(func2):"unknown function"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;ENVIRONMENT_IS_NODE?_emscripten_get_now=function(){var t=process.hrtime();return t[0]*1e3+t[1]/1e6}:ENVIRONMENT_IS_PTHREAD?_emscripten_get_now=function(){return performance.now()-Module.__performance_now_clock_drift}:typeof dateNow!="undefined"?_emscripten_get_now=dateNow:_emscripten_get_now=function(){return performance.now()};function setErrNo(value){return GROWABLE_HEAP_I32()[___errno_location()>>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i<numCallArgs;i++)GROWABLE_HEAP_F64()[b+i]=arguments[2+i];var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);return stackRestore(stack2),ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgs.array||(readAsmConstArgs.array=[]);var args=readAsmConstArgs.array;args.length=0;for(var ch;ch=GROWABLE_HEAP_U8()[sigPtr++];)ch===100||ch===102?(buf=buf+7&~7,args.push(GROWABLE_HEAP_F64()[buf>>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i<numCallArgs;i++)_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i];var isEmAsmConst=index<0,func2=isEmAsmConst?ASM_CONSTS[-index-1]:proxiedFunctionTable[index];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1],varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2],constArgs=readAsmConstArgs(sigPtr,varargPtr);return func2.apply(null,constArgs)}return func2.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{return wasmMemory.grow(size-buffer2.byteLength+65535>>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i)JSEvents.deferredCalls[i].targetFunction==targetFunction&&(JSEvents.deferredCalls.splice(i,1),--i)},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(JSEvents.canPerformEventHandlerRequests())for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1),--i,call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)&&JSEvents._removeHandler(i--)},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture),JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function(event){++JSEvents.inEventHandler,JSEvents.currentEventHandler=eventHandler,JSEvents.runDeferredCalls(),eventHandler.handlerFunc(event),JSEvents.runDeferredCalls(),--JSEvents.inEventHandler};if(eventHandler.callbackfunc)eventHandler.eventListenerFunc=jsEventHandler,eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture),JSEvents.eventHandlers.push(eventHandler),JSEvents.registerRemoveEventListeners();else for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString&&JSEvents._removeHandler(i--)},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave(),varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1);for(var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)},recordError:function(errorCode){GL.lastError||(GL.lastError=errorCode)},getNewId:function(table){for(var ret=GL.counter++,i=table.length;i<ret;i++)table[i]=null;return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count2,string,length){for(var source="",i=0;i<count2;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p2,i),name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1),name.slice(-1)=="]"&&(name=name.slice(0,name.lastIndexOf("[")));var loc=GLctx.getUniformLocation(p2,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id],GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+"["+j+"]";loc=GLctx.getUniformLocation(p2,n),id=GL.getNewId(GL.uniforms),GL.uniforms[id]=loc}}}}},__emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={},a=attributes>>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i<iovcnt;i++){for(var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j]);num+=len}return GROWABLE_HEAP_I32()[pnum>>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array2,buffer3){HEAP8.set(array2,buffer3)}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATPOSTRUN__=[],runtimeInitialized=!1,runtimeExited=!1;function preRun(){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=!0}function postRun(){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={env:asmLibraryArg,wasi_snapshot_preview1:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;Module.asm=exports3,wasmMemory=exports3.memory,updateGlobalBufferAndViews(wasmMemory.buffer),removeRunDependency("wasm-instantiate")}addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){for(var num=0,i=0;i<iovcnt;i++){for(var ptr=HEAP32[iov+i*8>>2],len=HEAP32[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,HEAPU8[ptr+j]);num+=len}return HEAP32[pnum>>2]=num,0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}var asmLibraryArg={emscripten_notify_memory_growth:_emscripten_notify_memory_growth,fd_close:_fd_close,fd_seek:_fd_seek,fd_write:_fd_write,proc_exit:_proc_exit,roundf:_roundf},asm=createWasm();Module.asm=asm;var _init=Module._init=function(){return(_init=Module._init=Module.asm.init).apply(null,arguments)},_register_tensor=Module._register_tensor=function(){return(_register_tensor=Module._register_tensor=Module.asm.register_tensor).apply(null,arguments)},_dispose_data=Module._dispose_data=function(){return(_dispose_data=Module._dispose_data=Module.asm.dispose_data).apply(null,arguments)},_dispose=Module._dispose=function(){return(_dispose=Module._dispose=Module.asm.dispose).apply(null,arguments)},_Abs=Module._Abs=function(){return(_Abs=Module._Abs=Module.asm.Abs).apply(null,arguments)},_Add=Module._Add=function(){return(_Add=Module._Add=Module.asm.Add).apply(null,arguments)},_AddN=Module._AddN=function(){return(_AddN=Module._AddN=Module.asm.AddN).apply(null,arguments)},_ArgMax=Module._ArgMax=function(){return(_ArgMax=Module._ArgMax=Module.asm.ArgMax).apply(null,arguments)},_AvgPool=Module._AvgPool=function(){return(_AvgPool=Module._AvgPool=Module.asm.AvgPool).apply(null,arguments)},_BatchMatMul=Module._BatchMatMul=function(){return(_BatchMatMul=Module._BatchMatMul=Module.asm.BatchMatMul).apply(null,arguments)},_ClipByValue=Module._ClipByValue=function(){return(_ClipByValue=Module._ClipByValue=Module.asm.ClipByValue).apply(null,arguments)},_Conv2D=Module._Conv2D=function(){return(_Conv2D=Module._Conv2D=Module.asm.Conv2D).apply(null,arguments)},_Conv2DBackpropInput=Module._Conv2DBackpropInput=function(){return(_Conv2DBackpropInput=Module._Conv2DBackpropInput=Module.asm.Conv2DBackpropInput).apply(null,arguments)},_Cos=Module._Cos=function(){return(_Cos=Module._Cos=Module.asm.Cos).apply(null,arguments)},_CropAndResize=Module._CropAndResize=function(){return(_CropAndResize=Module._CropAndResize=Module.asm.CropAndResize).apply(null,arguments)},_Cumsum=Module._Cumsum=function(){return(_Cumsum=Module._Cumsum=Module.asm.Cumsum).apply(null,arguments)},_DepthToSpace=Module._DepthToSpace=function(){return(_DepthToSpace=Module._DepthToSpace=Module.asm.DepthToSpace).apply(null,arguments)},_DepthwiseConv2dNative=Module._DepthwiseConv2dNative=function(){return(_DepthwiseConv2dNative=Module._DepthwiseConv2dNative=Module.asm.DepthwiseConv2dNative).apply(null,arguments)},_Equal=Module._Equal=function(){return(_Equal=Module._Equal=Module.asm.Equal).apply(null,arguments)},_Exp=Module._Exp=function(){return(_Exp=Module._Exp=Module.asm.Exp).apply(null,arguments)},_FlipLeftRight=Module._FlipLeftRight=function(){return(_FlipLeftRight=Module._FlipLeftRight=Module.asm.FlipLeftRight).apply(null,arguments)},_Floor=Module._Floor=function(){return(_Floor=Module._Floor=Module.asm.Floor).apply(null,arguments)},_FloorDiv=Module._FloorDiv=function(){return(_FloorDiv=Module._FloorDiv=Module.asm.FloorDiv).apply(null,arguments)},_FusedBatchNorm=Module._FusedBatchNorm=function(){return(_FusedBatchNorm=Module._FusedBatchNorm=Module.asm.FusedBatchNorm).apply(null,arguments)},_FusedConv2D=Module._FusedConv2D=function(){return(_FusedConv2D=Module._FusedConv2D=Module.asm.FusedConv2D).apply(null,arguments)},_FusedDepthwiseConv2D=Module._FusedDepthwiseConv2D=function(){return(_FusedDepthwiseConv2D=Module._FusedDepthwiseConv2D=Module.asm.FusedDepthwiseConv2D).apply(null,arguments)},_Gather=Module._Gather=function(){return(_Gather=Module._Gather=Module.asm.Gather).apply(null,arguments)},_GatherNd=Module._GatherNd=function(){return(_GatherNd=Module._GatherNd=Module.asm.GatherNd).apply(null,arguments)},_Greater=Module._Greater=function(){return(_Greater=Module._Greater=Module.asm.Greater).apply(null,arguments)},_GreaterEqual=Module._GreaterEqual=function(){return(_GreaterEqual=Module._GreaterEqual=Module.asm.GreaterEqual).apply(null,arguments)},_LeakyRelu=Module._LeakyRelu=function(){return(_LeakyRelu=Module._LeakyRelu=Module.asm.LeakyRelu).apply(null,arguments)},_Less=Module._Less=function(){return(_Less=Module._Less=Module.asm.Less).apply(null,arguments)},_LessEqual=Module._LessEqual=function(){return(_LessEqual=Module._LessEqual=Module.asm.LessEqual).apply(null,arguments)},_Log=Module._Log=function(){return(_Log=Module._Log=Module.asm.Log).apply(null,arguments)},_LogicalAnd=Module._LogicalAnd=function(){return(_LogicalAnd=Module._LogicalAnd=Module.asm.LogicalAnd).apply(null,arguments)},_Max=Module._Max=function(){return(_Max=Module._Max=Module.asm.Max).apply(null,arguments)},_MaxPool=Module._MaxPool=function(){return(_MaxPool=Module._MaxPool=Module.asm.MaxPool).apply(null,arguments)},_Maximum=Module._Maximum=function(){return(_Maximum=Module._Maximum=Module.asm.Maximum).apply(null,arguments)},_Mean=Module._Mean=function(){return(_Mean=Module._Mean=Module.asm.Mean).apply(null,arguments)},_Min=Module._Min=function(){return(_Min=Module._Min=Module.asm.Min).apply(null,arguments)},_Minimum=Module._Minimum=function(){return(_Minimum=Module._Minimum=Module.asm.Minimum).apply(null,arguments)},_Multiply=Module._Multiply=function(){return(_Multiply=Module._Multiply=Module.asm.Multiply).apply(null,arguments)},_Neg=Module._Neg=function(){return(_Neg=Module._Neg=Module.asm.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calledRun;Module.then=function(func2){if(calledRun)func2(Module);else{var old=Module.onRuntimeInitialized;Module.onRuntimeInitialized=function(){old&&old(),func2(Module)}}return Module};function ExitStatus(status){this.name="ExitStatus",this.message="Program terminated with exit("+status+")",this.status=status}var calledMain=!1;dependenciesFulfilled=function runCaller(){calledRun||run(),calledRun||(dependenciesFulfilled=runCaller)};function callMain(args){var entryFunction=Module.__start;try{entryFunction();var ret=0;exit(ret,!0)}catch(e){if(e instanceof ExitStatus)return;if(e=="unwind"){noExitRuntime=!0;return}else{var toLog=e;e&&typeof e=="object"&&e.stack&&(toLog=[e,e.stack]),err("exception thrown: "+toLog),quit_(1,e)}}finally{calledMain=!0}}function run(args){if(args=args||arguments_,runDependencies>0||(preRun(),runDependencies>0))return;function doRun(){calledRun||(calledRun=!0,Module.calledRun=!0,!ABORT&&(initRuntime(),preMain(),Module.onRuntimeInitialized&&Module.onRuntimeInitialized(),shouldRunNow&&callMain(args),postRun()))}Module.setStatus?(Module.setStatus("Running..."),setTimeout(function(){setTimeout(function(){Module.setStatus("")},1),doRun()},1)):doRun()}Module.run=run;function exit(status,implicit){implicit&&noExitRuntime&&status===0||(noExitRuntime||(ABORT=!0,EXITSTATUS=status,exitRuntime(),Module.onExit&&Module.onExit(status)),quit_(status,new ExitStatus(status)))}if(Module.preInit)for(typeof Module.preInit=="function"&&(Module.preInit=[Module.preInit]);Module.preInit.length>0;)Module.preInit.pop()();var shouldRunNow=!0;return Module.noInitialRun&&(shouldRunNow=!1),noExitRuntime=!0,run(),WasmBackendModule2}}();typeof exports=="object"&&typeof module=="object"?module.exports=WasmBackendModule:typeof define=="function"&&define.amd?define([],function(){return WasmBackendModule}):typeof 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urlParams=getQueryParams(this.global.location.search);TENSORFLOWJS_FLAGS_PREFIX in urlParams&&urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",").forEach(keyValue=>{let[key,value]=keyValue.split(":");this.urlFlags[key]=parseValue(key,value)})}};function getQueryParams(queryString){let params={};return queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(s,...t)=>(decodeParam(params,t[0],t[1]),t.join("="))),params}function decodeParam(params,name,value){params[decodeURIComponent(name)]=decodeURIComponent(value||"")}function parseValue(flagName,value){if(value=value.toLowerCase(),value==="true"||value==="false")return value==="true";if(`${+value}`===value)return+value;throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`)}function env(){return ENV}var ENV=null;function setEnvironmentGlobal(environment2){ENV=environment2}var globalNameSpace;function getGlobalNamespace(){if(globalNameSpace==null){let ns;if(typeof window!="undefined")ns=window;else if(typeof global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){let ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){let globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{let singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}var Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config]=value,[backend2]=key.split("_");backend2===backendName&&result.push(config)}return result}function registerKernel(config){let{kernelName,backendName}=config,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config)}function registerGradient(config){let{kernelName}=config;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now(){return env().platform.now()}function fetch2(path,requestInits){return env().platform.fetch(path,requestInits)}function encodeString(s,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.encode(s,encoding)}function decodeString(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.decode(bytes,encoding)}var Profiler=class{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad(name,25),rank=result.rank,size=result.size,shape=rightPad(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape.length;i++){let node2=tape[i],nodeInputs=node2.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node2.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node2.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node2=tape[i],nodeInputs=node2.inputs;for(let j=0;j<node2.outputs.length;j++)if(tensorsLeadToY[node2.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node2.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape.length;i++){let node2=tape[i];if(nodesFromX[node2.id]&&nodesToY[node2.id]){let prunedInputs={};for(let inputName in node2.inputs){let nodeInput=node2.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node2);prunedNode.inputs=prunedInputs,prunedNode.outputs=node2.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients(tensorAccumulatedGradientMap,filteredTape,tidy2,add5){for(let i=filteredTape.length-1;i>=0;i--){let node2=filteredTape[i],dys=[];if(node2.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node2.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);let inputGradients=node2.gradient(dys);for(let inputName in node2.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node2.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
 `)),lines.join(`
 `)}function computeMaxSizePerColumn(vals,shape,dtype,strides){let n=sizeFromShape(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples(vals):vals;if(rank>1)for(let row=0;row<n/numCols;row++){let offset=row*numCols;for(let j=0;j<numCols;j++)padPerCol[j]=Math.max(padPerCol[j],valToString(valuesOrTuples[offset+j],0,dtype).length)}return padPerCol}function valToString(val,pad3,dtype){let valStr;return Array.isArray(val)?valStr=`${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`:isString(val)?valStr=`'${val}'`:dtype==="bool"?valStr=boolNumToString(val):valStr=parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),rightPad(valStr,pad3)}function boolNumToString(v){return v===0?"false":"true"}function subTensorToString(vals,shape,dtype,strides,padPerCol,isLast=!0){let storagePerElement=dtype==="complex64"?2:1,size=shape[0],rank=shape.length;if(rank===0){if(dtype==="complex64"){let complexTuple=createComplexTuples(vals);return[valToString(complexTuple[0],0,dtype)]}return dtype==="bool"?[boolNumToString(vals[0])]:[vals[0].toString()]}if(rank===1){if(size>FORMAT_LIMIT_NUM_VALS){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples(firstVals),lastVals=createComplexTuples(lastVals)),["["+firstVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS){for(let i=0;i<FORMAT_NUM_FIRST_LAST_VALS;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,!1))}lines.push("...");for(let i=size-FORMAT_NUM_FIRST_LAST_VALS;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}}else for(let i=0;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}let sep=rank===2?",":"";lines[0]="["+lines[0]+sep;for(let i=1;i<lines.length-1;i++)lines[i]=" "+lines[i]+sep;let newLineSep=`,
 `;for(let i=2;i<rank;i++)newLineSep+=`
-`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape(shape),values!=null){let n=values.length;assert(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn().makeTensor(this.values,this.shape,this.dtype)}},trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(fn){trackerFn=fn}function setOpHandler(handler){opHandler=handler}function setDeprecationWarningFn(fn){deprecationWarningFn=fn}var Tensor=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape(shape),this.strides=computeStrides(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray(this.shape,vals)}arraySync(){return toNestedArray(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export2(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor2,tensorList){return tensorList.some(x=>x.id===tensor2.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container,list,seen){if(container!=null){if(container instanceof Tensor){list.push(container);return}if(isIterable(container)){let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(k=>k.name)))}}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance==null){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i];if(await this.initializeBackend(backendName).success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName);if(!(asyncInit?await success:success))return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler(this.backendInstance),!0}setupRegisteredKernels(){getKernelsForBackend(this.backendName).forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){getKernelsForBackend(backendName).forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend2=registryFactoryEntry.factory();if(backend2&&!(backend2 instanceof KernelBackend)&&typeof backend2.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend2.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend2,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend2,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend2,backend2.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend2=>backend2.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3,kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(outInfo=>{if(outInfo.rank!=null)return outInfo;let{dataId,shape,dtype}=outInfo;return this.makeTensorFromDataId(dataId,shape,dtype)});if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{if(forwardFunc==null)throw new Error(`Error running ${kernelName}: Neither modular kernel nor forward func passed`);let saveFunc=tensors=>{!isTapeOn||(saved=tensors.map(tensor2=>this.keep(this.clone(tensor2))))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){return tensors.map(tensor2=>this.keep(this.clone(tensor2)))}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend2){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend2=backend2||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend2.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend2),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend2){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend2),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend2){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend2||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(this.state.tensorInfo.has(a.dataId)){this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId);info.refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor2=this.state.activeScope.track[i];!tensor2.kept&&!tensorsToTrackInParentSet.has(tensor2.id)&&tensor2.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor2=>{!tensor2.kept&&tensor2.scopeId===oldScope.id&&this.track(tensor2)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node2=>{for(let tensor2 of node2.saved)tensor2.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){return this.state.tensorInfo.get(dataId).backend.readSync(dataId)}read(dataId){return this.state.tensorInfo.get(dataId).backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment2=new Environment(ns);ns._tfengine=new Engine(environment2)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernel(Add,inputs)}var device_util_exports={};__export2(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s 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is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency(val[i],subShape,indices.concat(i))}function assertDtype(expectedDtype,actualDType,argName,functionName){if(expectedDtype!=="string_or_numeric"){if(expectedDtype==null)throw new Error("Expected dtype cannot be null.");if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}}function convertToTensor(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor)return assertDtype(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],!0);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);return arg.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName,parseAsDtype))}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real4,imag4){let $real=convertToTensor(real4,"real","complex"),$imag=convertToTensor(imag4,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let inputs={real:$real,imag:$imag};return ENGINE.runKernel(Complex,inputs)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape(shape.slice(i)):!0;assert(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor2=>tensor2.name):Object.keys(tensors);for(let i=0;i<names.length;++i){let name=names[i],t=Array.isArray(tensors)?tensors[i].tensor:tensors[name];if(t.dtype!=="float32"&&t.dtype!=="int32"&&t.dtype!=="bool"&&t.dtype!=="string"&&t.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);let spec={name,shape:t.shape,dtype:t.dtype};if(t.dtype==="string"){let utf8bytes=new Promise(async resolve=>{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2<vals.length;i2++){let val=vals[i2],bytesOfLength=new Uint8Array(new Uint32Array([val.length]).buffer);bytes.set(bytesOfLength,offset),offset+=NUM_BYTES_STRING_LENGTH,bytes.set(val,offset),offset+=val.length}resolve(bytes)});dataPromises.push(utf8bytes)}else dataPromises.push(t.data());group!=null&&(spec.group=group),specs.push(spec)}let tensorValues=await Promise.all(dataPromises);return{data:concatenateTypedArrays(tensorValues),specs}}function decodeWeights(buffer2,specs){let out={},float16Decode,offset=0;for(let spec of specs){let name=spec.name,dtype=spec.dtype,shape=spec.shape,size=sizeFromShape(shape),values;if("quantization"in spec){let quantization=spec.quantization;if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){if(!("min"in quantization&&"scale"in quantization))throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`)}else if(quantization.dtype==="float16"){if(dtype!=="float32")throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`)}else throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let quantizationSizeFactor=DTYPE_VALUE_SIZE_MAP[quantization.dtype],byteBuffer=buffer2.slice(offset,offset+size*quantizationSizeFactor),quantizedArray=quantization.dtype==="uint8"?new Uint8Array(byteBuffer):new Uint16Array(byteBuffer);if(dtype==="float32")if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){values=new Float32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=v*quantization.scale+quantization.min}}else if(quantization.dtype==="float16")float16Decode===void 0&&(float16Decode=getFloat16Decoder()),values=float16Decode(quantizedArray);else throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);else if(dtype==="int32"){if(quantization.dtype!=="uint8"&&quantization.dtype!=="uint16")throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);values=new Int32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=Math.round(v*quantization.scale+quantization.min)}}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*quantizationSizeFactor}else if(dtype==="string"){let size2=sizeFromShape(spec.shape);values=[];for(let i=0;i<size2;i++){let byteLength=new Uint32Array(buffer2.slice(offset,offset+NUM_BYTES_STRING_LENGTH))[0];offset+=NUM_BYTES_STRING_LENGTH;let bytes=new Uint8Array(buffer2.slice(offset,offset+byteLength));values.push(bytes),offset+=byteLength}}else{let dtypeFactor=DTYPE_VALUE_SIZE_MAP[dtype],byteBuffer=buffer2.slice(offset,offset+size*dtypeFactor);if(dtype==="float32")values=new Float32Array(byteBuffer);else if(dtype==="int32")values=new Int32Array(byteBuffer);else if(dtype==="bool")values=new Uint8Array(byteBuffer);else if(dtype==="complex64"){values=new Float32Array(byteBuffer);let real4=new Float32Array(values.length/2),image3=new Float32Array(values.length/2);for(let i=0;i<real4.length;i++)real4[i]=values[i*2],image3[i]=values[i*2+1];let realTensor=tensor(real4,shape,"float32"),imageTensor=tensor(image3,shape,"float32");out[name]=complex(realTensor,imageTensor),realTensor.dispose(),imageTensor.dispose()}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*dtypeFactor}dtype!=="complex64"&&(out[name]=tensor(values,shape,dtype))}return out}function concatenateTypedArrays(xs){if(xs===null)throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);let totalByteLength=0,normalizedXs=[];xs.forEach(x=>{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer2){if(useNodeBuffer)return Buffer.from(buffer2).toString("base64");let buf=new Uint8Array(buffer2),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer(str){if(useNodeBuffer){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer2=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer2.set([s.charCodeAt(i)],i);return buffer2.buffer}function concatenateArrayBuffers(buffers){if(buffers.length===1)return buffers[0];let totalByteLength=0;buffers.forEach(buffer2=>{totalByteLength+=buffer2.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer2=>{temp.set(new Uint8Array(buffer2),offset),offset+=buffer2.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)===0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer2=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer2);for(let index=0;index<quantizedArray.length;index++){let float16Bits=quantizedArray[index],float32Bits=mantisaTable[offsetTable[float16Bits>>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer2)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[];return(handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters).forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions),DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),getRequest=modelTx.objectStore(MODEL_STORE_NAME).get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let putModelRequest=modelTx.objectStore(MODEL_STORE_NAME).put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),getAllInfoRequest=tx.objectStore(INFO_STORE_NAME).getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let deleteModelRequest=modelTx.objectStore(MODEL_STORE_NAME).delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}},PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData));let result={format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy};return modelArtifacts.signature!=null&&(result.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(result.modelInitializer=modelArtifacts.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(result)),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,metadata.signature!=null&&(out.signature=metadata.signature),metadata.userDefinedMetadata!=null&&(out.userDefinedMetadata=metadata.userDefinedMetadata),metadata.modelInitializer!=null&&(out.modelInitializer=metadata.modelInitializer)}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme2(path);let keys=getModelKeys(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}},URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry.instance==null&&(ModelStoreManagerRegistry.instance=new ModelStoreManagerRegistry),ModelStoreManagerRegistry.instance}static registerManager(scheme,manager){assert(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url);return ModelStoreManagerRegistry.getManager(schemeAndPath.scheme).removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!1)}async function moveModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!0)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}now(){return performance.now()}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernel(Cast,inputs,attrs)}var cast=op({cast_});function clone_(x){let inputs={x:convertToTensor(x,"x","clone","string_or_numeric")};return ENGINE.runKernel(Identity,inputs)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export2(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer);let modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);if(perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1){let result={modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy};modelJSON.signature!=null&&(result.signature=modelJSON.signature),modelJSON.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelJSON.userDefinedMetadata),modelJSON.modelInitializer!=null&&(result.modelInitializer=modelJSON.modelInitializer),resolve(result)}},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,bufferPromises=(loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction)).map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1;return loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction)}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){return weightsLoaderFactory(fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}))(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
-Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2<numBuffers;i2++)groupBytes+=buffers[bufferIndexOffset+i2].byteLength;let groupBuffer=new ArrayBuffer(groupBytes),groupByteBuffer=new Uint8Array(groupBuffer),groupBufferOffset=0;for(let i2=0;i2<numBuffers;i2++){let buffer2=new Uint8Array(buffers[bufferIndexOffset+i2]);groupByteBuffer.set(buffer2,groupBufferOffset),groupBufferOffset+=buffer2.byteLength}groupWeightsToFetch[i].forEach(weightsEntry=>{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}return tensor3d(values,[height,width,numChannels],"int32")}async function toPixels(img,canvas){let $img=convertToTensor(img,"img","toPixels");if(!(img instanceof Tensor)){let originalImgTensor=$img;$img=cast(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;i<indicesShape.length-1;++i)nResult*=indicesShape[i];let inputShape=tensor2.shape,resultShape=indicesShape.slice();resultShape.pop();let sliceSize=1;for(let i=sliceRank;i<tensorRank;++i)sliceSize*=inputShape[i],resultShape.push(inputShape[i]);let strides=[...computeStrides(tensor2.shape).map(stride=>stride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank<batchDim)throw new Error(shapeError+` update.rank < ${batchDim}. `);if(shape.length<sliceDim+(updates.rank-batchDim))throw new Error(shapeError+` Output shape length < ${sliceDim+(updates.rank-batchDim)}`);if(updates.rank!==batchDim+shape.length-sliceDim)throw new Error(shapeError+` update.rank != ${batchDim+shape.length-sliceDim}`);for(let d=0;d<batchDim;++d)if(updates.shape[d]!==indices.shape[d])throw new Error(shapeError+` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);for(let d=0;d<updates.rank-batchDim;++d)if(updates.shape[d+batchDim]!==shape[d+sliceDim])throw new Error(shapeError+` updates.shape[${d+batchDim}] (${updates.shape[d+batchDim]}) != shape[${d+batchDim}] (${shape[d+batchDim]})`)}function validateInput(updates,indices,shape){if(indices.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(updates.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);if(indices.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);if(shape.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);if(shape.length===0){if(indices.size===0)throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);if(updates.size===0)throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`)}validateUpdateShape(shape,indices,updates)}function calculateShapes(updates,indices,shape){let indicesRank=indices.shape.length,sliceRank=indicesRank>1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;i<totalNd;++i)sliceSize*=shape[i];let safeSliceDim=sliceRank<1?1:sliceRank,numUpdates=sizeFromShape(indices.shape)/safeSliceDim,strides=[...computeStrides(shape.slice(0,sliceRank)),1],outputSize=sizeFromShape(shape);return{sliceRank,numUpdates,sliceSize,strides,outputSize}}var slice_util_exports={};__export2(slice_util_exports,{assertParamsValid:()=>assertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i<size.length;i++)if(size[i]>1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i<size.length;i++)if(begin[i]>0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i<begin.length-1;i++)flatOffset+=begin[i]*strides[i];return flatOffset}function parseSliceParams(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config){return new cls(config)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon32){return epsilon32==null&&(epsilon32=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon32))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
+`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape(shape),values!=null){let n=values.length;assert(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn().makeTensor(this.values,this.shape,this.dtype)}},trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(fn){trackerFn=fn}function setOpHandler(handler){opHandler=handler}function setDeprecationWarningFn(fn){deprecationWarningFn=fn}var Tensor=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape(shape),this.strides=computeStrides(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray(this.shape,vals)}arraySync(){return toNestedArray(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export2(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor2,tensorList){return tensorList.some(x=>x.id===tensor2.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container,list,seen){if(container==null)return;if(container instanceof Tensor){list.push(container);return}if(!isIterable(container))return;let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(k=>k.name)))}}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i];if(await this.initializeBackend(backendName).success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName);if(!(asyncInit?await success:success))return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler(this.backendInstance),!0}setupRegisteredKernels(){getKernelsForBackend(this.backendName).forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){getKernelsForBackend(backendName).forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend2=registryFactoryEntry.factory();if(backend2&&!(backend2 instanceof KernelBackend)&&typeof backend2.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend2.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend2,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend2,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend2,backend2.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend2=>backend2.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3;this.backendName==null&&this.backend;let kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(outInfo=>{if(outInfo.rank!=null)return outInfo;let{dataId,shape,dtype}=outInfo;return this.makeTensorFromDataId(dataId,shape,dtype)});if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{if(forwardFunc==null)throw new Error(`Error running ${kernelName}: Neither modular kernel nor forward func passed`);let saveFunc=tensors=>{!isTapeOn||(saved=tensors.map(tensor2=>this.keep(this.clone(tensor2))))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){return tensors.map(tensor2=>this.keep(this.clone(tensor2)))}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend2){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend2=backend2||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend2.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend2),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend2){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend2),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend2){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend2||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId);info.refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor2=this.state.activeScope.track[i];!tensor2.kept&&!tensorsToTrackInParentSet.has(tensor2.id)&&tensor2.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor2=>{!tensor2.kept&&tensor2.scopeId===oldScope.id&&this.track(tensor2)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node2=>{for(let tensor2 of node2.saved)tensor2.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){return this.state.tensorInfo.get(dataId).backend.readSync(dataId)}read(dataId){return this.state.tensorInfo.get(dataId).backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment2=new Environment(ns);ns._tfengine=new Engine(environment2)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernel(Add,inputs)}var device_util_exports={};__export2(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s 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)|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0,4))}return!1}function isBrowser(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var ENV2=env();ENV2.registerFlag("DEBUG",()=>!1,debugValue=>{debugValue&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency(val[i],subShape,indices.concat(i))}function assertDtype(expectedDtype,actualDType,argName,functionName){if(expectedDtype!=="string_or_numeric"){if(expectedDtype==null)throw new Error("Expected dtype cannot be null.");if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}}function convertToTensor(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor)return assertDtype(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],!0);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);return arg.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName,parseAsDtype))}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real4,imag4){let $real=convertToTensor(real4,"real","complex"),$imag=convertToTensor(imag4,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let inputs={real:$real,imag:$imag};return ENGINE.runKernel(Complex,inputs)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape(shape.slice(i)):!0;assert(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor2=>tensor2.name):Object.keys(tensors);for(let i=0;i<names.length;++i){let name=names[i],t=Array.isArray(tensors)?tensors[i].tensor:tensors[name];if(t.dtype!=="float32"&&t.dtype!=="int32"&&t.dtype!=="bool"&&t.dtype!=="string"&&t.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);let spec={name,shape:t.shape,dtype:t.dtype};if(t.dtype==="string"){let utf8bytes=new Promise(async resolve=>{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2<vals.length;i2++){let val=vals[i2],bytesOfLength=new Uint8Array(new Uint32Array([val.length]).buffer);bytes.set(bytesOfLength,offset),offset+=NUM_BYTES_STRING_LENGTH,bytes.set(val,offset),offset+=val.length}resolve(bytes)});dataPromises.push(utf8bytes)}else dataPromises.push(t.data());group!=null&&(spec.group=group),specs.push(spec)}let tensorValues=await Promise.all(dataPromises);return{data:concatenateTypedArrays(tensorValues),specs}}function decodeWeights(buffer2,specs){let out={},float16Decode,offset=0;for(let spec of specs){let name=spec.name,dtype=spec.dtype,shape=spec.shape,size=sizeFromShape(shape),values;if("quantization"in spec){let quantization=spec.quantization;if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){if(!("min"in quantization&&"scale"in quantization))throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`)}else if(quantization.dtype==="float16"){if(dtype!=="float32")throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`)}else throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let quantizationSizeFactor=DTYPE_VALUE_SIZE_MAP[quantization.dtype],byteBuffer=buffer2.slice(offset,offset+size*quantizationSizeFactor),quantizedArray=quantization.dtype==="uint8"?new Uint8Array(byteBuffer):new Uint16Array(byteBuffer);if(dtype==="float32")if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){values=new Float32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=v*quantization.scale+quantization.min}}else if(quantization.dtype==="float16")float16Decode===void 0&&(float16Decode=getFloat16Decoder()),values=float16Decode(quantizedArray);else throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);else if(dtype==="int32"){if(quantization.dtype!=="uint8"&&quantization.dtype!=="uint16")throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);values=new Int32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=Math.round(v*quantization.scale+quantization.min)}}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*quantizationSizeFactor}else if(dtype==="string"){let size2=sizeFromShape(spec.shape);values=[];for(let i=0;i<size2;i++){let byteLength=new Uint32Array(buffer2.slice(offset,offset+NUM_BYTES_STRING_LENGTH))[0];offset+=NUM_BYTES_STRING_LENGTH;let bytes=new Uint8Array(buffer2.slice(offset,offset+byteLength));values.push(bytes),offset+=byteLength}}else{let dtypeFactor=DTYPE_VALUE_SIZE_MAP[dtype],byteBuffer=buffer2.slice(offset,offset+size*dtypeFactor);if(dtype==="float32")values=new Float32Array(byteBuffer);else if(dtype==="int32")values=new Int32Array(byteBuffer);else if(dtype==="bool")values=new Uint8Array(byteBuffer);else if(dtype==="complex64"){values=new Float32Array(byteBuffer);let real4=new Float32Array(values.length/2),image3=new Float32Array(values.length/2);for(let i=0;i<real4.length;i++)real4[i]=values[i*2],image3[i]=values[i*2+1];let realTensor=tensor(real4,shape,"float32"),imageTensor=tensor(image3,shape,"float32");out[name]=complex(realTensor,imageTensor),realTensor.dispose(),imageTensor.dispose()}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*dtypeFactor}dtype!=="complex64"&&(out[name]=tensor(values,shape,dtype))}return out}function concatenateTypedArrays(xs){if(xs===null)throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);let totalByteLength=0,normalizedXs=[];xs.forEach(x=>{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer2){if(useNodeBuffer)return Buffer.from(buffer2).toString("base64");let buf=new Uint8Array(buffer2),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer(str){if(useNodeBuffer){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer2=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer2.set([s.charCodeAt(i)],i);return buffer2.buffer}function concatenateArrayBuffers(buffers){if(buffers.length===1)return buffers[0];let totalByteLength=0;buffers.forEach(buffer2=>{totalByteLength+=buffer2.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer2=>{temp.set(new Uint8Array(buffer2),offset),offset+=buffer2.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)==0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer2=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer2);for(let index=0;index<quantizedArray.length;index++){let float16Bits=quantizedArray[index],float32Bits=mantisaTable[offsetTable[float16Bits>>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer2)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[];return(handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters).forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions),DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),getRequest=modelTx.objectStore(MODEL_STORE_NAME).get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let putModelRequest=modelTx.objectStore(MODEL_STORE_NAME).put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),getAllInfoRequest=tx.objectStore(INFO_STORE_NAME).getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let deleteModelRequest=modelTx.objectStore(MODEL_STORE_NAME).delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}},PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData));let result={format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy};return modelArtifacts.signature!=null&&(result.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(result.modelInitializer=modelArtifacts.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(result)),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,metadata.signature!=null&&(out.signature=metadata.signature),metadata.userDefinedMetadata!=null&&(out.userDefinedMetadata=metadata.userDefinedMetadata),metadata.modelInitializer!=null&&(out.modelInitializer=metadata.modelInitializer)}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme2(path);let keys=getModelKeys(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}},URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry.instance==null&&(ModelStoreManagerRegistry.instance=new ModelStoreManagerRegistry),ModelStoreManagerRegistry.instance}static registerManager(scheme,manager){assert(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url);return ModelStoreManagerRegistry.getManager(schemeAndPath.scheme).removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!1)}async function moveModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!0)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}now(){return performance.now()}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernel(Cast,inputs,attrs)}var cast=op({cast_});function clone_(x){let inputs={x:convertToTensor(x,"x","clone","string_or_numeric")};return ENGINE.runKernel(Identity,inputs)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export2(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer);let modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);if(perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1){let result={modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy};modelJSON.signature!=null&&(result.signature=modelJSON.signature),modelJSON.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelJSON.userDefinedMetadata),modelJSON.modelInitializer!=null&&(result.modelInitializer=modelJSON.modelInitializer),resolve(result)}},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,bufferPromises=(loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction)).map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1;return loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction)}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){return weightsLoaderFactory(fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}))(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
+Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2<numBuffers;i2++)groupBytes+=buffers[bufferIndexOffset+i2].byteLength;let groupBuffer=new ArrayBuffer(groupBytes),groupByteBuffer=new Uint8Array(groupBuffer),groupBufferOffset=0;for(let i2=0;i2<numBuffers;i2++){let buffer2=new Uint8Array(buffers[bufferIndexOffset+i2]);groupByteBuffer.set(buffer2,groupBufferOffset),groupBufferOffset+=buffer2.byteLength}groupWeightsToFetch[i].forEach(weightsEntry=>{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1,isImageBitmap=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else if(typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap)isImageBitmap=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}return tensor3d(values,[height,width,numChannels],"int32")}async function toPixels(img,canvas){let $img=convertToTensor(img,"img","toPixels");if(!(img instanceof Tensor)){let originalImgTensor=$img;$img=cast(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;i<indicesShape.length-1;++i)nResult*=indicesShape[i];let inputShape=tensor2.shape,resultShape=indicesShape.slice();resultShape.pop();let sliceSize=1;for(let i=sliceRank;i<tensorRank;++i)sliceSize*=inputShape[i],resultShape.push(inputShape[i]);let strides=[...computeStrides(tensor2.shape).map(stride=>stride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank<batchDim)throw new Error(shapeError+` update.rank < ${batchDim}. `);if(shape.length<sliceDim+(updates.rank-batchDim))throw new Error(shapeError+` Output shape length < ${sliceDim+(updates.rank-batchDim)}`);if(updates.rank!==batchDim+shape.length-sliceDim)throw new Error(shapeError+` update.rank != ${batchDim+shape.length-sliceDim}`);for(let d=0;d<batchDim;++d)if(updates.shape[d]!==indices.shape[d])throw new Error(shapeError+` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);for(let d=0;d<updates.rank-batchDim;++d)if(updates.shape[d+batchDim]!==shape[d+sliceDim])throw new Error(shapeError+` updates.shape[${d+batchDim}] (${updates.shape[d+batchDim]}) != shape[${d+batchDim}] (${shape[d+batchDim]})`)}function validateInput(updates,indices,shape){if(indices.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(updates.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);if(indices.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);if(shape.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);if(shape.length===0){if(indices.size===0)throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);if(updates.size===0)throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`)}validateUpdateShape(shape,indices,updates)}function calculateShapes(updates,indices,shape){let indicesRank=indices.shape.length,sliceRank=indicesRank>1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;i<totalNd;++i)sliceSize*=shape[i];let safeSliceDim=sliceRank<1?1:sliceRank,numUpdates=sizeFromShape(indices.shape)/safeSliceDim,strides=[...computeStrides(shape.slice(0,sliceRank)),1],outputSize=sizeFromShape(shape);return{sliceRank,numUpdates,sliceSize,strides,outputSize}}var slice_util_exports={};__export2(slice_util_exports,{assertParamsValid:()=>assertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i<size.length;i++)if(size[i]>1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i<size.length;i++)if(begin[i]>0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i<begin.length-1;i++)flatOffset+=begin[i]*strides[i];return flatOffset}function parseSliceParams(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config){return new cls(config)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon32){return epsilon32==null&&(epsilon32=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon32))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
 Actual:   ${actualFlat}.
 Expected: ${expectedFlat}.`);for(let i=0;i<expectedFlat.length;++i){let a=actualFlat[i],e=expectedFlat[i];if(!predicate(a,e))throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
 Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`)}}function expectPromiseToFail(fn,done){fn().then(()=>done.fail(),()=>done())}function expectArraysEqual(actual,expected){let exp4=typeof expected=="string"||typeof expected=="number"||typeof expected=="boolean"?[expected]:expected;return isString(actual)||isString(actual[0])||isString(expected)||isString(expected[0])?expectArraysPredicate(actual,exp4,(a,b)=>a==b):expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,0))}function expectNumbersClose(a,e,epsilon32){if(epsilon32==null&&(epsilon32=testEpsilon()),!areClose(a,e,epsilon32))throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`)}function areClose(a,e,epsilon32){return!isFinite(a)&&!isFinite(e)?!0:!(isNaN(a)||isNaN(e)||Math.abs(a-e)>epsilon32)}function expectValuesInRange(actual,low,high){for(let i=0;i<actual.length;i++)if(actual[i]<low||actual[i]>high)throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`)}function expectArrayBuffersEqual(actual,expected){expect(new Float32Array(actual)).toEqual(new Float32Array(expected))}function encodeStrings(a){for(let i=0;i<a.length;i++){let val=a[i];Array.isArray(val)?encodeStrings(val):a[i]=encodeString(val)}return a}var version="2.8.2";function enableProdMode(){env().set("PROD",!0)}function enableDebugMode(){env().set("DEBUG",!0)}function disableDeprecationWarnings(){env().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function deprecationWarn(msg){env().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn(deprecationWarn);function disposeVariables(){ENGINE.disposeVariables()}function engine(){return ENGINE}function memory(){return ENGINE.memory()}function profile(f){return ENGINE.profile(f)}function tidy(nameOrFn,fn){return ENGINE.tidy(nameOrFn,fn)}function dispose(container){getTensorsInContainer(container).forEach(tensor2=>tensor2.dispose())}function keep(result){return ENGINE.keep(result)}function time(f){return ENGINE.time(f)}function setBackend(backendName){return ENGINE.setBackend(backendName)}function ready(){return ENGINE.ready()}function getBackend(){return ENGINE.backendName}function removeBackend(name){ENGINE.removeBackend(name)}function findBackend(name){return ENGINE.findBackend(name)}function findBackendFactory(name){return ENGINE.findBackendFactory(name)}function registerBackend(name,factory,priority=1){return ENGINE.registerBackend(name,factory,priority)}function backend(){return ENGINE.backend}function setPlatform(platformName,platform){env().setPlatform(platformName,platform)}function add_(a,b){let $a=convertToTensor(a,"a","add"),$b=convertToTensor(b,"b","add");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Add,inputs)}var add2=op({add_});function floorDiv_(a,b){let $a=convertToTensor(a,"a","floorDiv"),$b=convertToTensor(b,"b","floorDiv");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(FloorDiv,inputs)}var floorDiv=op({floorDiv_});function div_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");if([$a,$b]=makeTypesMatch($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv($a,$b);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(RealDiv,inputs,attrs)}var div=op({div_});function mul_(a,b){let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Multiply,inputs)}var mul=op({mul_});function abs_(x){let $x=convertToTensor(x,"x","abs");if($x.dtype==="complex64"){let inputs={x:$x};return ENGINE.runKernel(ComplexAbs,inputs)}else{let inputs={x:$x};return ENGINE.runKernel(Abs,inputs)}}var abs=op({abs_});function acos_(x){let inputs={x:convertToTensor(x,"x","acos")};return ENGINE.runKernel(Acos,inputs)}var acos=op({acos_});function acosh_(x){let inputs={x:convertToTensor(x,"x","acosh")};return ENGINE.runKernel(Acosh,inputs)}var acosh=op({acosh_});function addN_(tensors){assert(Array.isArray(tensors),()=>"The argument passed to tf.addN() must be a list of tensors"),assert(tensors.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);let $tensors=tensors.map((t,i)=>convertToTensor(t,`tensors${i}`,"addN")),firstTensor=$tensors[0];$tensors.forEach(t=>{if(t.dtype!==firstTensor.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),$tensors.forEach(t=>{if(!arraysEqual(t.shape,firstTensor.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let inputs=$tensors;return ENGINE.runKernel(AddN,inputs)}var addN=op({addN_});function all_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","all","bool")},attrs={axis,keepDims};return ENGINE.runKernel(All,inputs,attrs)}var all=op({all_});function any_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","any","bool")},attrs={axis,keepDims};return ENGINE.runKernel(Any,inputs,attrs)}var any=op({any_});function argMax_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMax")},attrs={axis};return ENGINE.runKernel(ArgMax,inputs,attrs)}var argMax=op({argMax_});function argMin_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMin")},attrs={axis};return ENGINE.runKernel(ArgMin,inputs,attrs)}var argMin=op({argMin_});function asin_(x){let inputs={x:convertToTensor(x,"x","asin")};return ENGINE.runKernel(Asin,inputs)}var asin=op({asin_});function asinh_(x){let inputs={x:convertToTensor(x,"x","asinh")};return ENGINE.runKernel(Asinh,inputs)}var asinh=op({asinh_});function atan_(x){let inputs={x:convertToTensor(x,"x","atan")};return ENGINE.runKernel(Atan,inputs)}var atan=op({atan_});function atan2_(a,b){let $a=convertToTensor(a,"a","atan2"),$b=convertToTensor(b,"b","atan2");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Atan2,inputs)}var atan2=op({atan2_});function atanh_(x){let inputs={x:convertToTensor(x,"x","atanh")};return ENGINE.runKernel(Atanh,inputs)}var atanh=op({atanh_});function computeDilation2DInfo(inputShape,filterShape,strides,pad3,dataFormat="NHWC",dilations){let inputChannels=inputShape[3],$filterShape=[...filterShape,inputChannels],$dataFormat=convertConv2DDataFormat(dataFormat);return computeConv2DInfo(inputShape,$filterShape,strides,dilations,pad3,null,null,$dataFormat)}function computePool2DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,!1,dataFormat)}function computePool3DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,!1,$dataFormat,roundingMode)}function computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam(strides),[dilationHeight,dilationWidth]=parseTupleParam(dilations),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam(dilations),effectiveFilterDepth=getEffectiveFilterSize(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputRows,outputCols]}function computeOutputShape4D(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=round((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode),outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape2D([inHeight,inWidth],filterHeight,strideHeight,pad3,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad3==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad3=="object"){let top=dataFormat==="channelsLast"?pad3[1][0]:pad3[2][0],bottom=dataFormat==="channelsLast"?pad3[1][1]:pad3[2][1],left=dataFormat==="channelsLast"?pad3[2][0]:pad3[3][0],right=dataFormat==="channelsLast"?pad3[2][1]:pad3[3][1];padInfo={top,bottom,left,right,type:top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT"},outHeight=round((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=round((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,front:pad3,back:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape4D([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad3,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad3==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outDepth,outHeight,outWidth}}function round(value,roundingMode){if(!roundingMode)return Math.trunc(value);switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne(param){let[dimA,dimB,dimC]=parseTupleParam(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne(strides,dilations){return tupleValuesAreOne(strides)||tupleValuesAreOne(dilations)}function convertConv2DDataFormat(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function reshape_(x,shape){let inputs={x:convertToTensor(x,"x","reshape","string_or_numeric")},attrs={shape};return ENGINE.runKernel(Reshape,inputs,attrs)}var reshape=op({reshape_});function avgPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","avgPool","float32"),dilations=1;assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool,inputs,attrs);return res=cast(res,$x.dtype),reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool=op({avgPool_});function avgPool3d_(x,filterSize,strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","avgPool3d","float32"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(AvgPool3D,inputs,attrs);return res=cast(res,x5D.dtype),reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3d=op({avgPool3d_});function concat_(tensors,axis=0){assert(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray(tensors,"tensors","concat","string_or_numeric");if($tensors[0].dtype==="complex64"&&$tensors.forEach(tensor2=>{if(tensor2.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
-          with dtype ${tensor2.dtype}. `)}),$tensors.length===1)return clone($tensors[0]);let inputs=$tensors,attr={axis};return ENGINE.runKernel(Concat,inputs,attr)}var concat=op({concat_});function sigmoid_(x){let inputs={x:convertToTensor(x,"x","sigmoid")};return ENGINE.runKernel(Sigmoid,inputs)}var sigmoid=op({sigmoid_});function slice_(x,begin,size){let $x=convertToTensor(x,"x","slice","string_or_numeric");if($x.rank===0)throw new Error("Slicing scalar is not possible");let inputs={x:$x},attrs={begin,size};return ENGINE.runKernel(Slice,inputs,attrs)}var slice=op({slice_});function tanh_(x){let inputs={x:convertToTensor(x,"x","tanh")};return ENGINE.runKernel(Tanh,inputs)}var tanh2=op({tanh_});function basicLSTMCell_(forgetBias,lstmKernel,lstmBias,data,c,h){let $forgetBias=convertToTensor(forgetBias,"forgetBias","basicLSTMCell"),$lstmKernel=convertToTensor(lstmKernel,"lstmKernel","basicLSTMCell"),$lstmBias=convertToTensor(lstmBias,"lstmBias","basicLSTMCell"),$data=convertToTensor(data,"data","basicLSTMCell"),$c=convertToTensor(c,"c","basicLSTMCell"),$h=convertToTensor(h,"h","basicLSTMCell"),combined=concat([$data,$h],1),weighted=matMul(combined,$lstmKernel),res=add2(weighted,$lstmBias),batchSize=res.shape[0],sliceCols=res.shape[1]/4,sliceSize=[batchSize,sliceCols],i=slice(res,[0,0],sliceSize),j=slice(res,[0,sliceCols],sliceSize),f=slice(res,[0,sliceCols*2],sliceSize),o=slice(res,[0,sliceCols*3],sliceSize),newC=add2(mul(sigmoid(i),tanh2(j)),mul($c,sigmoid(add2($forgetBias,f)))),newH=mul(tanh2(newC),sigmoid(o));return[newC,newH]}var basicLSTMCell=op({basicLSTMCell_});function batchToSpaceND_(x,blockShape,crops){let $x=convertToTensor(x,"x","batchToSpaceND"),prod5=blockShape.reduce((a,b)=>a*b);assert($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert($x.shape[0]%prod5===0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`);let inputs={x:$x},attrs={blockShape,crops};return ENGINE.runKernel(BatchToSpaceND,inputs,attrs)}var batchToSpaceND=op({batchToSpaceND_});function xAs4D(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape(x,[1,1,1,x.size]):x.rank===2?x4D=reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_(x,mean4,variance,offset,scale22,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let inputs={x:xAs4D($x),scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE.runKernel(FusedBatchNorm,inputs,attrs);return reshape(res,$x.shape)}var batchNorm=op({batchNorm_});function batchNorm2d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`),assert($mean.rank===2||$mean.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===2||$variance.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===2||$scale.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===2||$offset.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm2d=op({batchNorm2d_});function batchNorm3d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`),assert($mean.rank===3||$mean.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===3||$variance.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===3||$scale.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===3||$offset.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm3d=op({batchNorm3d_});function batchNorm4d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`),assert($mean.rank===4||$mean.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===4||$variance.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===4||$scale.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===4||$offset.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm4d=op({batchNorm4d_});function bincount_(x,weights,size){let $x=convertToTensor(x,"x","bincount"),$weights=convertToTensor(weights,"weights","bincount");assert($x.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${$x.dtype}`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size};return ENGINE.runKernel(Bincount,inputs,attrs)}var bincount=op({bincount_});function broadcastTo_(x,shape){let input2=convertToTensor(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);if(reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0).length===0)return clone(input2);let inputs={x:input2},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var broadcastTo=op({broadcastTo_});function ceil_(x){let inputs={x:convertToTensor(x,"x","ceil")};return ENGINE.runKernel(Ceil,inputs)}var ceil=op({ceil_});function clipByValue_(x,clipValueMin,clipValueMax){let $x=convertToTensor(x,"x","clipByValue");assert(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE.runKernel(ClipByValue,inputs,attrs)}var clipByValue=op({clipByValue_});function concat1d_(tensors){return concat(tensors,0)}var concat1d=op({concat1d_});function concat2d_(tensors,axis){return concat(tensors,axis)}var concat2d=op({concat2d_});function concat3d_(tensors,axis){return concat(tensors,axis)}var concat3d=op({concat3d_});function concat4d_(tensors,axis){return concat(tensors,axis)}var concat4d=op({concat4d_});function conv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(Conv2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d=op({conv2d_});function conv1d_(x,filter,stride,pad3,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor(x,"x","conv1d"),$filter=convertToTensor(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape($x,[1,$x.shape[0],$x.shape[1]])),assert(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert(eitherStridesOrDilationsAreOne(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),res=conv2d(input4D,filter4D,[1,stride],pad3,"NHWC",[1,dilation],dimRoundingMode);return reshapedTo3D?reshape(res,[res.shape[2],res.shape[3]]):reshape(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d=op({conv1d_});function conv2DBackpropInput_(xShape,dy,filter,strides,pad3,dataFormat="NHWC",dimRoundingMode){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE.runKernel(Conv2DBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput=op({conv2DBackpropInput_});function conv2dTranspose_(x,filter,outputShape,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","conv2dTranspose"),$filter=convertToTensor(filter,"filter","conv2dTranspose");return conv2DBackpropInput(outputShape,$x,$filter,strides,pad3,"NHWC",dimRoundingMode)}var conv2dTranspose=op({conv2dTranspose_});function conv3d_(x,filter,strides,pad3,dataFormat="NDHWC",dilations=[1,1,1]){let $x=convertToTensor(x,"x","conv3d"),$filter=convertToTensor(filter,"filter","conv3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`),assert($filter.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`),assert(x5D.shape[4]===$filter.shape[3],()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NDHWC",()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);let inputs={x:x5D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations},res=ENGINE.runKernel(Conv3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3d=op({conv3d_});function conv3DBackpropInput_(xShape,dy,filter,strides,pad3){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let inputs={dy:dy5D,filter},attrs={pad:pad3,strides,inputShape:xShape5D},res=ENGINE.runKernel(Conv3DBackpropInputV2,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput=op({conv3DBackpropInput_});function conv3dTranspose_(x,filter,outputShape,strides,pad3){let $x=convertToTensor(x,"x","conv3dTranspose"),$filter=convertToTensor(filter,"filter","conv3dTranspose");return conv3DBackpropInput(outputShape,$x,$filter,strides,pad3)}var conv3dTranspose=op({conv3dTranspose_});function cos_(x){let inputs={x:convertToTensor(x,"x","cos")};return ENGINE.runKernel(Cos,inputs)}var cos=op({cos_});function cosh_(x){let inputs={x:convertToTensor(x,"x","cosh")};return ENGINE.runKernel(Cosh,inputs)}var cosh=op({cosh_});function cumsum_(x,axis=0,exclusive=!1,reverse5=!1){let inputs={x:convertToTensor(x,"x","cumsum")},attrs={axis,exclusive,reverse:reverse5};return ENGINE.runKernel(Cumsum,inputs,attrs)}var cumsum=op({cumsum_});function denseBincount_(x,weights,size,binaryOutput=!1){let $x=convertToTensor(x,"x","denseBincount"),$weights=convertToTensor(weights,"weights","denseBincount");assert($x.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`),assert($x.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size,binaryOutput};return ENGINE.runKernel(DenseBincount,inputs,attrs)}var denseBincount=op({denseBincount_});function depthToSpace_(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+Expected: ${expectedFlat}.`)}}function expectPromiseToFail(fn,done){fn().then(()=>done.fail(),()=>done())}function expectArraysEqual(actual,expected){let exp4=typeof expected=="string"||typeof expected=="number"||typeof expected=="boolean"?[expected]:expected;return isString(actual)||isString(actual[0])||isString(expected)||isString(expected[0])?expectArraysPredicate(actual,exp4,(a,b)=>a==b):expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,0))}function expectNumbersClose(a,e,epsilon32){if(epsilon32==null&&(epsilon32=testEpsilon()),!areClose(a,e,epsilon32))throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`)}function areClose(a,e,epsilon32){return!isFinite(a)&&!isFinite(e)?!0:!(isNaN(a)||isNaN(e)||Math.abs(a-e)>epsilon32)}function expectValuesInRange(actual,low,high){for(let i=0;i<actual.length;i++)if(actual[i]<low||actual[i]>high)throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`)}function expectArrayBuffersEqual(actual,expected){expect(new Float32Array(actual)).toEqual(new Float32Array(expected))}function encodeStrings(a){for(let i=0;i<a.length;i++){let val=a[i];Array.isArray(val)?encodeStrings(val):a[i]=encodeString(val)}return a}var version="2.8.3";function enableProdMode(){env().set("PROD",!0)}function enableDebugMode(){env().set("DEBUG",!0)}function disableDeprecationWarnings(){env().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function deprecationWarn(msg){env().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn(deprecationWarn);function disposeVariables(){ENGINE.disposeVariables()}function engine(){return ENGINE}function memory(){return ENGINE.memory()}function profile(f){return ENGINE.profile(f)}function tidy(nameOrFn,fn){return ENGINE.tidy(nameOrFn,fn)}function dispose(container){getTensorsInContainer(container).forEach(tensor2=>tensor2.dispose())}function keep(result){return ENGINE.keep(result)}function time(f){return ENGINE.time(f)}function setBackend(backendName){return ENGINE.setBackend(backendName)}function ready(){return ENGINE.ready()}function getBackend(){return ENGINE.backendName}function removeBackend(name){ENGINE.removeBackend(name)}function findBackend(name){return ENGINE.findBackend(name)}function findBackendFactory(name){return ENGINE.findBackendFactory(name)}function registerBackend(name,factory,priority=1){return ENGINE.registerBackend(name,factory,priority)}function backend(){return ENGINE.backend}function setPlatform(platformName,platform){env().setPlatform(platformName,platform)}function add_(a,b){let $a=convertToTensor(a,"a","add"),$b=convertToTensor(b,"b","add");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Add,inputs)}var add2=op({add_});function floorDiv_(a,b){let $a=convertToTensor(a,"a","floorDiv"),$b=convertToTensor(b,"b","floorDiv");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(FloorDiv,inputs)}var floorDiv=op({floorDiv_});function div_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");if([$a,$b]=makeTypesMatch($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv($a,$b);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(RealDiv,inputs,attrs)}var div=op({div_});function mul_(a,b){let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Multiply,inputs)}var mul=op({mul_});function abs_(x){let $x=convertToTensor(x,"x","abs");if($x.dtype==="complex64"){let inputs={x:$x};return ENGINE.runKernel(ComplexAbs,inputs)}else{let inputs={x:$x};return ENGINE.runKernel(Abs,inputs)}}var abs=op({abs_});function acos_(x){let inputs={x:convertToTensor(x,"x","acos")};return ENGINE.runKernel(Acos,inputs)}var acos=op({acos_});function acosh_(x){let inputs={x:convertToTensor(x,"x","acosh")};return ENGINE.runKernel(Acosh,inputs)}var acosh=op({acosh_});function addN_(tensors){assert(Array.isArray(tensors),()=>"The argument passed to tf.addN() must be a list of tensors"),assert(tensors.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);let $tensors=tensors.map((t,i)=>convertToTensor(t,`tensors${i}`,"addN")),firstTensor=$tensors[0];$tensors.forEach(t=>{if(t.dtype!==firstTensor.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),$tensors.forEach(t=>{if(!arraysEqual(t.shape,firstTensor.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let inputs=$tensors;return ENGINE.runKernel(AddN,inputs)}var addN=op({addN_});function all_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","all","bool")},attrs={axis,keepDims};return ENGINE.runKernel(All,inputs,attrs)}var all=op({all_});function any_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","any","bool")},attrs={axis,keepDims};return ENGINE.runKernel(Any,inputs,attrs)}var any=op({any_});function argMax_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMax")},attrs={axis};return ENGINE.runKernel(ArgMax,inputs,attrs)}var argMax=op({argMax_});function argMin_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMin")},attrs={axis};return ENGINE.runKernel(ArgMin,inputs,attrs)}var argMin=op({argMin_});function asin_(x){let inputs={x:convertToTensor(x,"x","asin")};return ENGINE.runKernel(Asin,inputs)}var asin=op({asin_});function asinh_(x){let inputs={x:convertToTensor(x,"x","asinh")};return ENGINE.runKernel(Asinh,inputs)}var asinh=op({asinh_});function atan_(x){let inputs={x:convertToTensor(x,"x","atan")};return ENGINE.runKernel(Atan,inputs)}var atan=op({atan_});function atan2_(a,b){let $a=convertToTensor(a,"a","atan2"),$b=convertToTensor(b,"b","atan2");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Atan2,inputs)}var atan2=op({atan2_});function atanh_(x){let inputs={x:convertToTensor(x,"x","atanh")};return ENGINE.runKernel(Atanh,inputs)}var atanh=op({atanh_});function computeDilation2DInfo(inputShape,filterShape,strides,pad3,dataFormat="NHWC",dilations){let inputChannels=inputShape[3],$filterShape=[...filterShape,inputChannels],$dataFormat=convertConv2DDataFormat(dataFormat);return computeConv2DInfo(inputShape,$filterShape,strides,dilations,pad3,null,null,$dataFormat)}function computePool2DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,!1,dataFormat)}function computePool3DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,!1,$dataFormat,roundingMode)}function computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam(strides),[dilationHeight,dilationWidth]=parseTupleParam(dilations),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam(dilations),effectiveFilterDepth=getEffectiveFilterSize(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputRows,outputCols]}function computeOutputShape4D(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=round((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode),outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape2D([inHeight,inWidth],filterHeight,strideHeight,pad3,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad3==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad3=="object"){let top=dataFormat==="channelsLast"?pad3[1][0]:pad3[2][0],bottom=dataFormat==="channelsLast"?pad3[1][1]:pad3[2][1],left=dataFormat==="channelsLast"?pad3[2][0]:pad3[3][0],right=dataFormat==="channelsLast"?pad3[2][1]:pad3[3][1];padInfo={top,bottom,left,right,type:top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT"},outHeight=round((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=round((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,front:pad3,back:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape4D([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad3,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad3==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outDepth,outHeight,outWidth}}function round(value,roundingMode){if(!roundingMode)return Math.trunc(value);switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne(param){let[dimA,dimB,dimC]=parseTupleParam(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne(strides,dilations){return tupleValuesAreOne(strides)||tupleValuesAreOne(dilations)}function convertConv2DDataFormat(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function reshape_(x,shape){let inputs={x:convertToTensor(x,"x","reshape","string_or_numeric")},attrs={shape};return ENGINE.runKernel(Reshape,inputs,attrs)}var reshape=op({reshape_});function avgPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","avgPool","float32"),dilations=1;assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool,inputs,attrs);return res=cast(res,$x.dtype),reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool=op({avgPool_});function avgPool3d_(x,filterSize,strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","avgPool3d","float32"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(AvgPool3D,inputs,attrs);return res=cast(res,x5D.dtype),reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3d=op({avgPool3d_});function concat_(tensors,axis=0){assert(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray(tensors,"tensors","concat","string_or_numeric");if($tensors[0].dtype==="complex64"&&$tensors.forEach(tensor2=>{if(tensor2.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
+          with dtype ${tensor2.dtype}. `)}),$tensors.length===1)return clone($tensors[0]);let inputs=$tensors,attr={axis};return ENGINE.runKernel(Concat,inputs,attr)}var concat=op({concat_});function sigmoid_(x){let inputs={x:convertToTensor(x,"x","sigmoid")};return ENGINE.runKernel(Sigmoid,inputs)}var sigmoid=op({sigmoid_});function slice_(x,begin,size){let $x=convertToTensor(x,"x","slice","string_or_numeric");if($x.rank===0)throw new Error("Slicing scalar is not possible");let inputs={x:$x},attrs={begin,size};return ENGINE.runKernel(Slice,inputs,attrs)}var slice=op({slice_});function tanh_(x){let inputs={x:convertToTensor(x,"x","tanh")};return ENGINE.runKernel(Tanh,inputs)}var tanh2=op({tanh_});function basicLSTMCell_(forgetBias,lstmKernel,lstmBias,data,c,h){let $forgetBias=convertToTensor(forgetBias,"forgetBias","basicLSTMCell"),$lstmKernel=convertToTensor(lstmKernel,"lstmKernel","basicLSTMCell"),$lstmBias=convertToTensor(lstmBias,"lstmBias","basicLSTMCell"),$data=convertToTensor(data,"data","basicLSTMCell"),$c=convertToTensor(c,"c","basicLSTMCell"),$h=convertToTensor(h,"h","basicLSTMCell"),combined=concat([$data,$h],1),weighted=matMul(combined,$lstmKernel),res=add2(weighted,$lstmBias),batchSize=res.shape[0],sliceCols=res.shape[1]/4,sliceSize=[batchSize,sliceCols],i=slice(res,[0,0],sliceSize),j=slice(res,[0,sliceCols],sliceSize),f=slice(res,[0,sliceCols*2],sliceSize),o=slice(res,[0,sliceCols*3],sliceSize),newC=add2(mul(sigmoid(i),tanh2(j)),mul($c,sigmoid(add2($forgetBias,f)))),newH=mul(tanh2(newC),sigmoid(o));return[newC,newH]}var basicLSTMCell=op({basicLSTMCell_});function batchToSpaceND_(x,blockShape,crops){let $x=convertToTensor(x,"x","batchToSpaceND"),prod5=blockShape.reduce((a,b)=>a*b);assert($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert($x.shape[0]%prod5==0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`);let inputs={x:$x},attrs={blockShape,crops};return ENGINE.runKernel(BatchToSpaceND,inputs,attrs)}var batchToSpaceND=op({batchToSpaceND_});function xAs4D(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape(x,[1,1,1,x.size]):x.rank===2?x4D=reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_(x,mean4,variance,offset,scale22,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let inputs={x:xAs4D($x),scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE.runKernel(FusedBatchNorm,inputs,attrs);return reshape(res,$x.shape)}var batchNorm=op({batchNorm_});function batchNorm2d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`),assert($mean.rank===2||$mean.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===2||$variance.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===2||$scale.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===2||$offset.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm2d=op({batchNorm2d_});function batchNorm3d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`),assert($mean.rank===3||$mean.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===3||$variance.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===3||$scale.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===3||$offset.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm3d=op({batchNorm3d_});function batchNorm4d_(x,mean4,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`),assert($mean.rank===4||$mean.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===4||$variance.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===4||$scale.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===4||$offset.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm4d=op({batchNorm4d_});function bincount_(x,weights,size){let $x=convertToTensor(x,"x","bincount"),$weights=convertToTensor(weights,"weights","bincount");assert($x.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${$x.dtype}`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size};return ENGINE.runKernel(Bincount,inputs,attrs)}var bincount=op({bincount_});function broadcastTo_(x,shape){let input2=convertToTensor(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);if(reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0).length===0)return clone(input2);let inputs={x:input2},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var broadcastTo=op({broadcastTo_});function ceil_(x){let inputs={x:convertToTensor(x,"x","ceil")};return ENGINE.runKernel(Ceil,inputs)}var ceil=op({ceil_});function clipByValue_(x,clipValueMin,clipValueMax){let $x=convertToTensor(x,"x","clipByValue");assert(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE.runKernel(ClipByValue,inputs,attrs)}var clipByValue=op({clipByValue_});function concat1d_(tensors){return concat(tensors,0)}var concat1d=op({concat1d_});function concat2d_(tensors,axis){return concat(tensors,axis)}var concat2d=op({concat2d_});function concat3d_(tensors,axis){return concat(tensors,axis)}var concat3d=op({concat3d_});function concat4d_(tensors,axis){return concat(tensors,axis)}var concat4d=op({concat4d_});function conv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(Conv2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d=op({conv2d_});function conv1d_(x,filter,stride,pad3,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor(x,"x","conv1d"),$filter=convertToTensor(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape($x,[1,$x.shape[0],$x.shape[1]])),assert(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert(eitherStridesOrDilationsAreOne(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),res=conv2d(input4D,filter4D,[1,stride],pad3,"NHWC",[1,dilation],dimRoundingMode);return reshapedTo3D?reshape(res,[res.shape[2],res.shape[3]]):reshape(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d=op({conv1d_});function conv2DBackpropInput_(xShape,dy,filter,strides,pad3,dataFormat="NHWC",dimRoundingMode){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE.runKernel(Conv2DBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput=op({conv2DBackpropInput_});function conv2dTranspose_(x,filter,outputShape,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","conv2dTranspose"),$filter=convertToTensor(filter,"filter","conv2dTranspose");return conv2DBackpropInput(outputShape,$x,$filter,strides,pad3,"NHWC",dimRoundingMode)}var conv2dTranspose=op({conv2dTranspose_});function conv3d_(x,filter,strides,pad3,dataFormat="NDHWC",dilations=[1,1,1]){let $x=convertToTensor(x,"x","conv3d"),$filter=convertToTensor(filter,"filter","conv3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`),assert($filter.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`),assert(x5D.shape[4]===$filter.shape[3],()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NDHWC",()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);let inputs={x:x5D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations},res=ENGINE.runKernel(Conv3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3d=op({conv3d_});function conv3DBackpropInput_(xShape,dy,filter,strides,pad3){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let inputs={dy:dy5D,filter},attrs={pad:pad3,strides,inputShape:xShape5D},res=ENGINE.runKernel(Conv3DBackpropInputV2,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput=op({conv3DBackpropInput_});function conv3dTranspose_(x,filter,outputShape,strides,pad3){let $x=convertToTensor(x,"x","conv3dTranspose"),$filter=convertToTensor(filter,"filter","conv3dTranspose");return conv3DBackpropInput(outputShape,$x,$filter,strides,pad3)}var conv3dTranspose=op({conv3dTranspose_});function cos_(x){let inputs={x:convertToTensor(x,"x","cos")};return ENGINE.runKernel(Cos,inputs)}var cos=op({cos_});function cosh_(x){let inputs={x:convertToTensor(x,"x","cosh")};return ENGINE.runKernel(Cosh,inputs)}var cosh=op({cosh_});function cumsum_(x,axis=0,exclusive=!1,reverse5=!1){let inputs={x:convertToTensor(x,"x","cumsum")},attrs={axis,exclusive,reverse:reverse5};return ENGINE.runKernel(Cumsum,inputs,attrs)}var cumsum=op({cumsum_});function denseBincount_(x,weights,size,binaryOutput=!1){let $x=convertToTensor(x,"x","denseBincount"),$weights=convertToTensor(weights,"weights","denseBincount");assert($x.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`),assert($x.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size,binaryOutput};return ENGINE.runKernel(DenseBincount,inputs,attrs)}var denseBincount=op({denseBincount_});function depthToSpace_(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
     ${inputHeight} and ${blockSize}  for depthToSpace with input shape
     ${$x.shape}`),assert(inputWidth*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
     ${inputWidth} and ${blockSize} for depthToSpace with input shape
-        ${$x.shape}`),assert(inputDepth%(blockSize*blockSize)===0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernel(DepthToSpace,inputs,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(DepthwiseConv2dNative,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let inputs={x:convertToTensor(x,"x","diag")};return ENGINE.runKernel(Diag,inputs)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad3,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i<inRank;i++){let dim=inRank-1-i,a=inShape[dim]||1;(outShape[outShape.length-1-i]||1)>1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_(a,b){let $a=convertToTensor(a,"a","equal"),$b=convertToTensor(b,"b","equal");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Equal,inputs)}var equal=op({equal_});function where_(condition,a,b){let $a=convertToTensor(a,"a","where"),$b=convertToTensor(b,"b","where"),$condition=convertToTensor(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape($a.shape,$b.shape),$broadcastedA=broadcastTo($a,broadcastShape),$broadcastedB=broadcastTo($b,broadcastShape);$condition.rank===1&&assert($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernel(Select,inputs)}var where=op({where_});function zerosLike_(x){let inputs={x:convertToTensor(x,"x","zerosLike")};return ENGINE.runKernel(ZerosLike,inputs)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros4=zerosLike(divResult),bEqualsZero=equal($b,zeros4);return where(bEqualsZero,zeros4,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]);return matMul($t1,t22D)}}var dot=op({dot_});function elu_(x){let inputs={x:convertToTensor(x,"x","elu")};return ENGINE.runKernel(Elu,inputs)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernel(Erf,inputs)}var erf=op({erf_});function exp_(x){let inputs={x:convertToTensor(x,"x","exp")};return ENGINE.runKernel(Exp,inputs)}var exp=op({exp_});function expandDims_(x,axis=0){let $x=convertToTensor(x,"x","expandDims","string_or_numeric");assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let inputs={input:$x},attrs={dim:axis};return ENGINE.runKernel(ExpandDims,inputs,attrs)}var expandDims=op({expandDims_});function expm1_(x){let inputs={x:convertToTensor(x,"x","expm1")};return ENGINE.runKernel(Expm1,inputs)}var expm1=op({expm1_});function tile_(x,reps){let $x=convertToTensor(x,"x","tile","string_or_numeric");assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let inputs={x:$x},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile(expandDims(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile(expandDims(expandDims(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile(expandDims(expandDims(expandDims(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye=op({eye_});function fill(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE.runKernel(Fill,{},attrs)}function floor_(x){let inputs={x:convertToTensor(x,"x","floor")};return ENGINE.runKernel(Floor,inputs)}var floor=op({floor_});function gather_(x,indices,axis=0,batchDims=0){let $x=convertToTensor(x,"x","gather"),$indices=convertToTensor(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis,batchDims};return ENGINE.runKernel(GatherV2,inputs,attrs)}var gather=op({gather_});function greater_(a,b){let $a=convertToTensor(a,"a","greater"),$b=convertToTensor(b,"b","greater");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Greater,inputs)}var greater=op({greater_});function greaterEqual_(a,b){let $a=convertToTensor(a,"a","greaterEqual"),$b=convertToTensor(b,"b","greaterEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(GreaterEqual,inputs)}var greaterEqual=op({greaterEqual_});function imag_(input2){let inputs={input:convertToTensor(input2,"input","imag")};return ENGINE.runKernel(Imag,inputs)}var imag=op({imag_});function isFinite_(x){let inputs={x:convertToTensor(x,"x","isFinite")};return ENGINE.runKernel(IsFinite,inputs)}var isFinite2=op({isFinite_});function isInf_(x){let inputs={x:convertToTensor(x,"x","isInf")};return ENGINE.runKernel(IsInf,inputs)}var isInf=op({isInf_});function isNaN_(x){let inputs={x:convertToTensor(x,"x","isNaN")};return ENGINE.runKernel(IsNan,inputs)}var isNaN2=op({isNaN_});function leakyRelu_(x,alpha=.2){let inputs={x:convertToTensor(x,"x","leakyRelu")},attrs={alpha};return ENGINE.runKernel(LeakyRelu,inputs,attrs)}var leakyRelu=op({leakyRelu_});function less_(a,b){let $a=convertToTensor(a,"a","less"),$b=convertToTensor(b,"b","less");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Less,inputs)}var less=op({less_});function lessEqual_(a,b){let $a=convertToTensor(a,"a","lessEqual"),$b=convertToTensor(b,"b","lessEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LessEqual,inputs)}var lessEqual=op({lessEqual_});function linspace(start,stop,num){if(num<=0)throw new Error("The number of values should be positive.");let attrs={start,stop,num};return ENGINE.runKernel(LinSpace,{},attrs)}function localResponseNormalization_(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor(x,"x","localResponseNormalization");assert($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
+        ${$x.shape}`),assert(inputDepth%(blockSize*blockSize)==0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernel(DepthToSpace,inputs,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(DepthwiseConv2dNative,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let inputs={x:convertToTensor(x,"x","diag")};return ENGINE.runKernel(Diag,inputs)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad3,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i<inRank;i++){let dim=inRank-1-i,a=inShape[dim]||1;(outShape[outShape.length-1-i]||1)>1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_(a,b){let $a=convertToTensor(a,"a","equal"),$b=convertToTensor(b,"b","equal");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Equal,inputs)}var equal=op({equal_});function where_(condition,a,b){let $a=convertToTensor(a,"a","where"),$b=convertToTensor(b,"b","where"),$condition=convertToTensor(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape($a.shape,$b.shape),$broadcastedA=broadcastTo($a,broadcastShape),$broadcastedB=broadcastTo($b,broadcastShape);$condition.rank===1&&assert($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernel(Select,inputs)}var where=op({where_});function zerosLike_(x){let inputs={x:convertToTensor(x,"x","zerosLike")};return ENGINE.runKernel(ZerosLike,inputs)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros4=zerosLike(divResult),bEqualsZero=equal($b,zeros4);return where(bEqualsZero,zeros4,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]);return matMul($t1,t22D)}}var dot=op({dot_});function elu_(x){let inputs={x:convertToTensor(x,"x","elu")};return ENGINE.runKernel(Elu,inputs)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernel(Erf,inputs)}var erf=op({erf_});function exp_(x){let inputs={x:convertToTensor(x,"x","exp")};return ENGINE.runKernel(Exp,inputs)}var exp=op({exp_});function expandDims_(x,axis=0){let $x=convertToTensor(x,"x","expandDims","string_or_numeric");assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let inputs={input:$x},attrs={dim:axis};return ENGINE.runKernel(ExpandDims,inputs,attrs)}var expandDims=op({expandDims_});function expm1_(x){let inputs={x:convertToTensor(x,"x","expm1")};return ENGINE.runKernel(Expm1,inputs)}var expm1=op({expm1_});function tile_(x,reps){let $x=convertToTensor(x,"x","tile","string_or_numeric");assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let inputs={x:$x},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile(expandDims(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile(expandDims(expandDims(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile(expandDims(expandDims(expandDims(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye=op({eye_});function fill(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE.runKernel(Fill,{},attrs)}function floor_(x){let inputs={x:convertToTensor(x,"x","floor")};return ENGINE.runKernel(Floor,inputs)}var floor=op({floor_});function gather_(x,indices,axis=0,batchDims=0){let $x=convertToTensor(x,"x","gather"),$indices=convertToTensor(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis,batchDims};return ENGINE.runKernel(GatherV2,inputs,attrs)}var gather=op({gather_});function greater_(a,b){let $a=convertToTensor(a,"a","greater"),$b=convertToTensor(b,"b","greater");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Greater,inputs)}var greater=op({greater_});function greaterEqual_(a,b){let $a=convertToTensor(a,"a","greaterEqual"),$b=convertToTensor(b,"b","greaterEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(GreaterEqual,inputs)}var greaterEqual=op({greaterEqual_});function imag_(input2){let inputs={input:convertToTensor(input2,"input","imag")};return ENGINE.runKernel(Imag,inputs)}var imag=op({imag_});function isFinite_(x){let inputs={x:convertToTensor(x,"x","isFinite")};return ENGINE.runKernel(IsFinite,inputs)}var isFinite2=op({isFinite_});function isInf_(x){let inputs={x:convertToTensor(x,"x","isInf")};return ENGINE.runKernel(IsInf,inputs)}var isInf=op({isInf_});function isNaN_(x){let inputs={x:convertToTensor(x,"x","isNaN")};return ENGINE.runKernel(IsNan,inputs)}var isNaN2=op({isNaN_});function leakyRelu_(x,alpha=.2){let inputs={x:convertToTensor(x,"x","leakyRelu")},attrs={alpha};return ENGINE.runKernel(LeakyRelu,inputs,attrs)}var leakyRelu=op({leakyRelu_});function less_(a,b){let $a=convertToTensor(a,"a","less"),$b=convertToTensor(b,"b","less");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Less,inputs)}var less=op({less_});function lessEqual_(a,b){let $a=convertToTensor(a,"a","lessEqual"),$b=convertToTensor(b,"b","lessEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LessEqual,inputs)}var lessEqual=op({lessEqual_});function linspace(start,stop,num){if(num<=0)throw new Error("The number of values should be positive.");let attrs={start,stop,num};return ENGINE.runKernel(LinSpace,{},attrs)}function localResponseNormalization_(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor(x,"x","localResponseNormalization");assert($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
                rank ${$x.rank}.`),assert(isInt(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE.runKernel(LRN,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization=op({localResponseNormalization_});function log_(x){let inputs={x:convertToTensor(x,"x","log")};return ENGINE.runKernel(Log,inputs)}var log=op({log_});function log1p_(x){let inputs={x:convertToTensor(x,"x","log1p")};return ENGINE.runKernel(Log1p,inputs)}var log1p=op({log1p_});function grad(f){return assert(isFunction(f),()=>"The f passed in grad(f) must be a function"),(x,dy)=>{let $x=convertToTensor(x,"x","tf.grad","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grad"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f($x),[$x],$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(grads2),grads2[0]})}}function grads(f){return assert(isFunction(f),()=>"The f passed in grads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let $args=convertToTensorArray(args,"args","tf.grads","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grads"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f(...$args),$args,$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(grads2),grads2})}}function valueAndGrad(f){return assert(isFunction(f),()=>"The f passed in valueAndGrad(f) must be a function"),(x,dy)=>{assert(x instanceof Tensor,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:grads2,value}=ENGINE.gradients(()=>f(x),[x],dy);return checkGrads(grads2),{grad:grads2[0],value}}}function valueAndGrads(f){return assert(isFunction(f),()=>"The f passed in valueAndGrads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args)&&args.every(arg=>arg instanceof Tensor),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let res=ENGINE.gradients(()=>f(...args),args,dy);return dy!=null&&assertShapesMatch(res.value.shape,dy.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(res.grads),res}}function variableGrads(f,varList){assert(isFunction(f),()=>"The f passed in variableGrads(f) must be a function"),assert(varList==null||Array.isArray(varList)&&varList.every(v=>v instanceof Variable),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let specifiedVarList=varList!=null;if(!specifiedVarList){varList=[];for(let varName in ENGINE.registeredVariables)varList.push(ENGINE.registeredVariables[varName])}let specifiedNonTrainable=specifiedVarList?varList.filter(variable2=>!variable2.trainable):null,originalVarCount=varList.length;varList=varList.filter(variable2=>variable2.trainable),assert(varList.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);let allowNoGradients=!0,{value,grads:grads2}=ENGINE.gradients(f,varList,null,allowNoGradients);assert(grads2.some(g=>g!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(value.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);let namedGrads={};return varList.forEach((v,i)=>{grads2[i]!=null&&(namedGrads[v.name]=grads2[i])}),specifiedNonTrainable!=null&&specifiedNonTrainable.forEach(v=>namedGrads[v.name]=null),{value,grads:namedGrads}}function customGrad(f){return ENGINE.customGrad(f)}function checkGrads(grads2){if(grads2.filter(g=>g==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
-    the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function logSumExp_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","logSumExp"),axes=parseAxisParam(axis,$x.shape),xMax=max($x,axes,!0),a=sub($x,xMax),b=exp(a),c=sum2(b,axes),d=log(c),res=add2(reshape(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,axes);return reshape(res,newShape)}return res}var logSumExp=op({logSumExp_});function logicalAnd_(a,b){let $a=convertToTensor(a,"a","logicalAnd","bool"),$b=convertToTensor(b,"b","logicalAnd","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalAnd,inputs)}var logicalAnd=op({logicalAnd_});function logicalNot_(x){let inputs={x:convertToTensor(x,"x","logicalNot","bool")};return ENGINE.runKernel(LogicalNot,inputs)}var logicalNot=op({logicalNot_});function logicalOr_(a,b){let $a=convertToTensor(a,"a","logicalOr","bool"),$b=convertToTensor(b,"b","logicalOr","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalOr,inputs)}var logicalOr=op({logicalOr_});function logicalXor_(a,b){let $a=convertToTensor(a,"a","logicalXor","bool"),$b=convertToTensor(b,"b","logicalXor","bool");return assertAndGetBroadcastShape($a.shape,$b.shape),logicalAnd(logicalOr(a,b),logicalNot(logicalAnd(a,b)))}var logicalXor=op({logicalXor_});function maxPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data,c,h){let $data=convertToTensor(data,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i<lstmCells.length;i++){let output=lstmCells[i](input2,$c[i],$h[i]);newStates.push(output[0]),newStates.push(output[1]),input2=output[1]}let newC=[],newH=[];for(let i=0;i<newStates.length;i+=2)newC.push(newStates[i]),newH.push(newStates[i+1]);return[newC,newH]}var multiRNNCell=op({multiRNNCell_});function multinomial_(logits,numSamples,seed,normalized=!1){let $logits=convertToTensor(logits,"logits","multinomial"),numOutcomes=$logits.size,origRank=$logits.rank;if(numOutcomes<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);if(origRank>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]===0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i<size;i++)values[i]=randFunction();return ENGINE.makeTensor(values,shape,dtype)}var rand=op({rand_}),seedrandom=__toModule2(require_seedrandom2()),MPRandGauss=class{constructor(mean4,stdDeviation,dtype,truncated,seed){this.mean=mean4,this.stdDev=stdDeviation,this.dtype=dtype,this.nextVal=NaN,this.truncated=truncated,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let seedValue=seed||Math.random();this.random=seedrandom.alea(seedValue.toString())}nextValue(){if(!isNaN(this.nextVal)){let value=this.nextVal;return this.nextVal=NaN,value}let resultX,resultY,isValid=!1;for(;!isValid;){let v1,v2,s;do v1=2*this.random()-1,v2=2*this.random()-1,s=v1*v1+v2*v2;while(s>=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),u<v0||Math.log(u)<v1)break}return v=1/this.beta*this.d*v,this.alpha<1&&(v*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(v)}convertValue(value){return this.dtype==="float32"?value:Math.round(value)}},UniformRandom=class{constructor(min6=0,max6=1,dtype,seed){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=rgamma.nextValue();return res.toTensor()}var randomGamma=op({randomGamma_});function randomNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error(`Unsupported data type ${dtype}`);let randGauss=new MPRandGauss(mean4,stdDev,dtype,!1,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var randomNormal=op({randomNormal_});function randomUniform_(shape,minval=0,maxval=1,dtype="float32",seed){let res=buffer(shape,dtype),random=new UniformRandom(minval,maxval,null,seed);for(let i=0;i<res.values.length;i++)res.values[i]=random.nextValue();return res.toTensor()}var randomUniform=op({randomUniform_});function range(start,stop,step4=1,dtype="float32"){if(step4===0)throw new Error("Cannot have a step of zero");let attrs={start,stop,step:step4,dtype};return ENGINE.runKernel(Range,{},attrs)}function real_(input2){let inputs={input:convertToTensor(input2,"input","real")};return ENGINE.runKernel(Real,inputs)}var real=op({real_});function reciprocal_(x){let inputs={x:convertToTensor(x,"x","reciprocal")};return ENGINE.runKernel(Reciprocal,inputs)}var reciprocal=op({reciprocal_});function relu_(x){let inputs={x:convertToTensor(x,"x","relu")};return ENGINE.runKernel(Relu,inputs)}var relu=op({relu_});function relu6_(x){let inputs={x:convertToTensor(x,"x","relu6")};return ENGINE.runKernel(Relu6,inputs)}var relu6=op({relu6_});function reverse_(x,axis){let inputs={x:convertToTensor(x,"x","reverse")},attrs={dims:axis};return ENGINE.runKernel(Reverse,inputs,attrs)}var reverse=op({reverse_});function reverse1d_(x){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i<xVals.length;i++)ySet.has(xVals[i])||outputSize++;let buffer2=new TensorBuffer([outputSize],$x.dtype),indices=new TensorBuffer([outputSize],"int32");for(let i=0,p2=0;i<xVals.length;i++)ySet.has(xVals[i])||(buffer2.values[p2]=xVals[i],indices.values[p2]=i,p2++);return[buffer2.toTensor(),indices.toTensor()]}var setdiff1dAsync=setdiff1dAsync_;function sign_(x){let inputs={x:convertToTensor(x,"x","sign")};return ENGINE.runKernel(Sign,inputs)}var sign=op({sign_});function sin_(x){let inputs={x:convertToTensor(x,"x","sin")};return ENGINE.runKernel(Sin,inputs)}var sin=op({sin_});function sinh_(x){let inputs={x:convertToTensor(x,"x","sinh")};return ENGINE.runKernel(Sinh,inputs)}var sinh=op({sinh_});function slice1d_(x,begin,size){let $x=convertToTensor(x,"x","slice1d");return assert($x.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var truncatedNormal=op({truncatedNormal_});function unique_(x,axis=0){let $x=convertToTensor(x,"x","unique","string_or_numeric");assert($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i<condVals.length;i++)condVals[i]&&indices.push(i);let inBuffer=buffer(condShape,"int32"),out=buffer([indices.length,condShape.length],"int32");for(let i=0;i<indices.length;i++){let loc=inBuffer.indexToLoc(indices[i]),offset=i*condShape.length;out.values.set(loc,offset)}return out.toTensor()}async function whereAsync_(condition){let $condition=convertToTensor(condition,"condition","whereAsync","bool"),vals=await $condition.data(),res=whereImpl($condition.shape,vals);return condition!==$condition&&$condition.dispose(),res}var whereAsync=whereAsync_;async function booleanMaskAsync_(tensor2,mask,axis){let $tensor=convertToTensor(tensor2,"tensor","boolMask"),$mask=convertToTensor(mask,"mask","boolMask","bool"),axisFrom=axis==null?0:axis,maskDim=$mask.rank,tensorShape=$tensor.shape;assert(maskDim>0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i<axisFrom+maskDim;i++)leadingSize*=tensorShape[i];let targetTensorShape=tensorShape.slice(0,axisFrom).concat([leadingSize],tensorShape.slice(axisFrom+maskDim)),reshapedTensor=reshape($tensor,targetTensorShape),reshapedMask=reshape($mask,[-1]),positivePositions=await whereAsync(reshapedMask),indices=squeeze(positivePositions,[1]),res=gather(reshapedTensor,indices,axisFrom);return tensor2!==$tensor&&$tensor.dispose(),mask!==$mask&&$mask.dispose(),indices.dispose(),reshapedTensor.dispose(),reshapedMask.dispose(),positivePositions.dispose(),res}var booleanMaskAsync=booleanMaskAsync_;function notEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","notEqualStrict"),$b=convertToTensor(b,"b","notEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual($a,$b)}function lessStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessStrict"),$b=convertToTensor(b,"b","lessStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessStrict: "),less($a,$b)}function equalStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","equalStrict"),$b=convertToTensor(b,"b","equalStrict");return assertShapesMatch($a.shape,$b.shape,"Error in equalStrict: "),equal($a,$b)}function lessEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessEqualStrict"),$b=convertToTensor(b,"b","lessEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual($a,$b)}function greaterStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterStrict"),$b=convertToTensor(b,"b","greaterStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterStrict: "),greater($a,$b)}function greaterEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterEqualStrict"),$b=convertToTensor(b,"b","greaterEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual($a,$b)}var equalStrict=op({equalStrict_}),greaterEqualStrict=op({greaterEqualStrict_}),greaterStrict=op({greaterStrict_}),lessEqualStrict=op({lessEqualStrict_}),lessStrict=op({lessStrict_}),notEqualStrict=op({notEqualStrict_});function addStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","addStrict"),$b=convertToTensor(b,"b","addStrict");return assertShapesMatch($a.shape,$b.shape,"Error in addStrict: "),add2($a,$b)}function subStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","subStrict"),$b=convertToTensor(b,"b","subStrict");return assertShapesMatch($a.shape,$b.shape,"Error in subStrict: "),sub($a,$b)}function powStrict_(base2,exp4){return deprecationWarn("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch(base2.shape,exp4.shape,"Error in powStrict: "),pow(base2,exp4)}function mulStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");return assertShapesMatch($a.shape,$b.shape,"Error in multiplyStrict: "),mul($a,$b)}function divStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");return assertShapesMatch($a.shape,$b.shape,"Error in divideStrict: "),div($a,$b)}function modStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","modStrict"),$b=convertToTensor(b,"b","modStrict");return assertShapesMatch($a.shape,$b.shape,"Error in modStrict: "),mod($a,$b)}function minimumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","minimumStrict"),$b=convertToTensor(b,"b","minimumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in minimumStrict: "),minimum($a,$b)}function maximumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","maximumStrict"),$b=convertToTensor(b,"b","maximumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in maximumStrict: "),maximum($a,$b)}function squaredDifferenceStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","squaredDifferenceStrict"),$b=convertToTensor(b,"b","squaredDifferenceStrict");return assertShapesMatch($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference($a,$b)}var addStrict=op({addStrict_}),divStrict=op({divStrict_}),maximumStrict=op({maximumStrict_}),minimumStrict=op({minimumStrict_}),modStrict=op({modStrict_}),mulStrict=op({mulStrict_}),powStrict=op({powStrict_}),squaredDifferenceStrict=op({squaredDifferenceStrict_}),subStrict=op({subStrict_});function norm_(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor(x,"x","norm");let norm2=normImpl(x,ord,axis),keepDimsShape=norm2.shape;if(keepDims){let axes=parseAxisParam(axis,x.shape);keepDimsShape=expandShapeToKeepDim(norm2.shape,axes)}return reshape(norm2,keepDimsShape)}function normImpl(x,p2,axis=null){if(x.rank===0)return abs(x);if(x.rank!==1&&axis===null)return normImpl(reshape(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum2(abs(x),axis);if(p2===Infinity)return max(abs(x),axis);if(p2===-Infinity)return min(abs(x),axis);if(p2==="euclidean"||p2===2)return sqrt(sum2(pow(abs(x),scalar(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max(sum2(abs(x),axis[0]),axis[1]-1);if(p2===Infinity)return max(sum2(abs(x),axis[1]),axis[0]);if(p2===-Infinity)return min(sum2(abs(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt(sum2(square(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm=op({norm_});function movingAverage_(v,x,decay,step4,zeroDebias=!0){let $v=convertToTensor(v,"v","movingAverage"),$x=convertToTensor(x,"x","movingAverage"),$decay=convertToTensor(decay,"decay","movingAverage");assertTypesMatch($v,$x),assert(arraysEqual($v.shape,$x.shape),()=>"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i<x.shape.length;i++)noiseShape[i]==null&&x.shape[i]!=null?newDimension.push(x.shape[i]):newDimension.push(noiseShape[i]);return newDimension}return noiseShape}function dropout_(x,rate,noiseShape,seed){let $x=convertToTensor(x,"x","dropout");if(assert($x.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d(newValues,"float32")}async function inTopKAsync_(predictions,targets,k=1){let $predictions=convertToTensor(predictions,"predictions","inTopK"),$targets=convertToTensor(targets,"targets","inTopK");assert($predictions.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;b<batch;b++){let offset=b*size,vals=predictionsVals.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value),precision3[b]=0;for(let i=0;i<k;i++)if(valAndInd[i].index===targetsVals[b]){precision3[b]=1;break}}return predictions!==$predictions&&$predictions.dispose(),targets!==$targets&&$targets.dispose(),tensor(precision3,$targets.shape,"bool")}var inTopKAsync=inTopKAsync_,fused_ops_exports={};__export2(fused_ops_exports,{conv2d:()=>conv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal2.size;){let padLen=start+frameLength-signal2.size,pad3=concat([slice(signal2,start,frameLength-padLen),fill([padLen],padValue)]);output.push(pad3),start+=frameStep}return output.length===0?tensor2d([],[0,frameLength]):reshape(concat(output),[output.length,frameLength])}var frame=op({frame_});function stft_(signal2,frameLength,frameStep,fftLength,windowFn=hannWindow){fftLength==null&&(fftLength=enclosingPowerOfTwo(frameLength));let framedSignal=frame(signal2,frameLength,frameStep),windowedSignal=mul(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft(slice(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat(output)}var stft=op({stft_});function cropAndResize_(image3,boxes,boxInd,cropSize,method="bilinear",extrapolationValue=0){let $image=convertToTensor(image3,"image","cropAndResize"),$boxes=convertToTensor(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor(boxInd,"boxInd","cropAndResize","int32"),numBoxes=$boxes.shape[0];assert($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a<b?-1:0}function binarySearch_(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale22=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou2=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou2>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale22,iou2),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale22,iou2){let weight=Math.exp(scale22*iou2*iou2);return iou2<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1===0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1===0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul(sum2(mul(ys[j],x)),ys[j]);x=sub(x,proj)}return div(x,norm(x,"euclidean"))}));return inputIsTensor2D?stack(ys,0):ys}var gramSchmidt=op({gramSchmidt_});function qr_(x,fullMatrices=!1){if(assert(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE.tidy(()=>{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon32=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon32),l13=neg(mul($labels,log(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon32=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon32,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.rho,config.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config){return new cls(config.learningRate,config.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon,config.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config){return new cls(config.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config){return new cls(config.learningRate,config.momentum,config.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon32,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config){return new cls(config.learningRate,config.decay,config.momentum,config.epsilon,config.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon32,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon32=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon32)}static adadelta(learningRate=.001,rho=.95,epsilon32=null){return new AdadeltaOptimizer(learningRate,rho,epsilon32)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon32=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon32,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log2,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(inSize){return inSize<=PARALLELIZE_THRESHOLD?inSize:nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)))}function getImageCenter(center,imageHeight,imageWidth){let centerX=imageWidth*(typeof center=="number"?center:center[0]),centerY=imageHeight*(typeof center=="number"?center:center[1]);return[centerX,centerY]}function getReshaped(inputShape,blockShape,prod5,batchToSpace=!0){let reshaped=[];if(batchToSpace)reshaped=reshaped.concat(blockShape.slice(0)),reshaped.push(inputShape[0]/prod5),reshaped=reshaped.concat(inputShape.slice(1));else{reshaped=reshaped.concat(inputShape[0]);let spatialLength=blockShape.length;for(let i=0;i<spatialLength;++i)reshaped=reshaped.concat([inputShape[i+1]/blockShape[i],blockShape[i]]);reshaped=reshaped.concat(inputShape.slice(spatialLength+1))}return reshaped}function getPermuted(reshapedRank,blockShapeRank,batchToSpace=!0){let permuted=[];if(batchToSpace){permuted.push(blockShapeRank);for(let i=blockShapeRank+1;i<reshapedRank;++i)i<=2*blockShapeRank?(permuted.push(i),permuted.push(i-(blockShapeRank+1))):permuted.push(i)}else{let permutedBeforeBatch=[],permutedAfterBatch=[];for(let i=1;i<reshapedRank;++i)i>=blockShapeRank*2+1||i%2===1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i<inputShape.length;++i)i<=blockShape.length?batchToSpace?reshapedPermuted.push(blockShape[i-1]*inputShape[i]):reshapedPermuted.push(inputShape[i]/blockShape[i-1]):reshapedPermuted.push(inputShape[i]);return reshapedPermuted}function getSliceBeginCoords(crops,blockShape){let sliceBeginCoords=[0];for(let i=0;i<blockShape;++i)sliceBeginCoords.push(crops[i][0]);return sliceBeginCoords}function getSliceSize(uncroppedShape,crops,blockShape){let sliceSize=uncroppedShape.slice(0,1);for(let i=0;i<blockShape;++i)sliceSize.push(uncroppedShape[i+1]-crops[i][0]-crops[i][1]);return sliceSize}var SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429;function warn(...msg){env().getBool("IS_TEST")||console.warn(...msg)}function log2(...msg){env().getBool("IS_TEST")||console.log(...msg)}function mergeRealAndImagArrays(real4,imag4){if(real4.length!==imag4.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`);let result=new Float32Array(real4.length*2);for(let i=0;i<result.length;i+=2)result[i]=real4[i/2],result[i+1]=imag4[i/2];return result}function splitRealAndImagArrays(complex4){let real4=new Float32Array(complex4.length/2),imag4=new Float32Array(complex4.length/2);for(let i=0;i<complex4.length;i+=2)real4[i/2]=complex4[i],imag4[i/2]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithEvenIndex(complex4){let len=Math.ceil(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=0;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithOddIndex(complex4){let len=Math.floor(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=2;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function getComplexWithIndex(complex4,index){let real4=complex4[index*2],imag4=complex4[index*2+1];return{real:real4,imag:imag4}}function assignToTypedArray(data,real4,imag4,index){data[index*2]=real4,data[index*2+1]=imag4}function exponents(n,inverse){let real4=new Float32Array(n/2),imag4=new Float32Array(n/2);for(let i=0;i<Math.ceil(n/2);i++){let x=(inverse?2:-2)*Math.PI*(i/n);real4[i]=Math.cos(x),imag4[i]=Math.sin(x)}return{real:real4,imag:imag4}}function exponent(k,n,inverse){let x=(inverse?2:-2)*Math.PI*(k/n),real4=Math.cos(x),imag4=Math.sin(x);return{real:real4,imag:imag4}}function prepareSplitSize(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert(x.shape[axis]%numOrSizeSplits===0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)dim!==axis?outShape.push(aShape[dim]):outShape.push(numSegments);return outShape}function collectGatherOpShapeInfo(x,indices,axis,batchDims){let indicesRank=indices.shape.length,xRank=x.shape.length;if(batchDims!==0&&(batchDims<-indicesRank||batchDims>indicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
+    the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function logSumExp_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","logSumExp"),axes=parseAxisParam(axis,$x.shape),xMax=max($x,axes,!0),a=sub($x,xMax),b=exp(a),c=sum2(b,axes),d=log(c),res=add2(reshape(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,axes);return reshape(res,newShape)}return res}var logSumExp=op({logSumExp_});function logicalAnd_(a,b){let $a=convertToTensor(a,"a","logicalAnd","bool"),$b=convertToTensor(b,"b","logicalAnd","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalAnd,inputs)}var logicalAnd=op({logicalAnd_});function logicalNot_(x){let inputs={x:convertToTensor(x,"x","logicalNot","bool")};return ENGINE.runKernel(LogicalNot,inputs)}var logicalNot=op({logicalNot_});function logicalOr_(a,b){let $a=convertToTensor(a,"a","logicalOr","bool"),$b=convertToTensor(b,"b","logicalOr","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalOr,inputs)}var logicalOr=op({logicalOr_});function logicalXor_(a,b){let $a=convertToTensor(a,"a","logicalXor","bool"),$b=convertToTensor(b,"b","logicalXor","bool");return assertAndGetBroadcastShape($a.shape,$b.shape),logicalAnd(logicalOr(a,b),logicalNot(logicalAnd(a,b)))}var logicalXor=op({logicalXor_});function maxPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data,c,h){let $data=convertToTensor(data,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i<lstmCells.length;i++){let output=lstmCells[i](input2,$c[i],$h[i]);newStates.push(output[0]),newStates.push(output[1]),input2=output[1]}let newC=[],newH=[];for(let i=0;i<newStates.length;i+=2)newC.push(newStates[i]),newH.push(newStates[i+1]);return[newC,newH]}var multiRNNCell=op({multiRNNCell_});function multinomial_(logits,numSamples,seed,normalized=!1){let $logits=convertToTensor(logits,"logits","multinomial"),numOutcomes=$logits.size,origRank=$logits.rank;if(numOutcomes<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);if(origRank>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]==0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i<size;i++)values[i]=randFunction();return ENGINE.makeTensor(values,shape,dtype)}var rand=op({rand_}),seedrandom=__toModule2(require_seedrandom2()),MPRandGauss=class{constructor(mean4,stdDeviation,dtype,truncated,seed){this.mean=mean4,this.stdDev=stdDeviation,this.dtype=dtype,this.nextVal=NaN,this.truncated=truncated,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let seedValue=seed||Math.random();this.random=seedrandom.alea(seedValue.toString())}nextValue(){if(!isNaN(this.nextVal)){let value=this.nextVal;return this.nextVal=NaN,value}let resultX,resultY,isValid=!1;for(;!isValid;){let v1,v2,s;do v1=2*this.random()-1,v2=2*this.random()-1,s=v1*v1+v2*v2;while(s>=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),u<v0||Math.log(u)<v1)break}return v=1/this.beta*this.d*v,this.alpha<1&&(v*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(v)}convertValue(value){return this.dtype==="float32"?value:Math.round(value)}},UniformRandom=class{constructor(min6=0,max6=1,dtype,seed){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=rgamma.nextValue();return res.toTensor()}var randomGamma=op({randomGamma_});function randomNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error(`Unsupported data type ${dtype}`);let randGauss=new MPRandGauss(mean4,stdDev,dtype,!1,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var randomNormal=op({randomNormal_});function randomUniform_(shape,minval=0,maxval=1,dtype="float32",seed){let res=buffer(shape,dtype),random=new UniformRandom(minval,maxval,null,seed);for(let i=0;i<res.values.length;i++)res.values[i]=random.nextValue();return res.toTensor()}var randomUniform=op({randomUniform_});function range(start,stop,step4=1,dtype="float32"){if(step4===0)throw new Error("Cannot have a step of zero");let attrs={start,stop,step:step4,dtype};return ENGINE.runKernel(Range,{},attrs)}function real_(input2){let inputs={input:convertToTensor(input2,"input","real")};return ENGINE.runKernel(Real,inputs)}var real=op({real_});function reciprocal_(x){let inputs={x:convertToTensor(x,"x","reciprocal")};return ENGINE.runKernel(Reciprocal,inputs)}var reciprocal=op({reciprocal_});function relu_(x){let inputs={x:convertToTensor(x,"x","relu")};return ENGINE.runKernel(Relu,inputs)}var relu=op({relu_});function relu6_(x){let inputs={x:convertToTensor(x,"x","relu6")};return ENGINE.runKernel(Relu6,inputs)}var relu6=op({relu6_});function reverse_(x,axis){let inputs={x:convertToTensor(x,"x","reverse")},attrs={dims:axis};return ENGINE.runKernel(Reverse,inputs,attrs)}var reverse=op({reverse_});function reverse1d_(x){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i<xVals.length;i++)ySet.has(xVals[i])||outputSize++;let buffer2=new TensorBuffer([outputSize],$x.dtype),indices=new TensorBuffer([outputSize],"int32");for(let i=0,p2=0;i<xVals.length;i++)ySet.has(xVals[i])||(buffer2.values[p2]=xVals[i],indices.values[p2]=i,p2++);return[buffer2.toTensor(),indices.toTensor()]}var setdiff1dAsync=setdiff1dAsync_;function sign_(x){let inputs={x:convertToTensor(x,"x","sign")};return ENGINE.runKernel(Sign,inputs)}var sign=op({sign_});function sin_(x){let inputs={x:convertToTensor(x,"x","sin")};return ENGINE.runKernel(Sin,inputs)}var sin=op({sin_});function sinh_(x){let inputs={x:convertToTensor(x,"x","sinh")};return ENGINE.runKernel(Sinh,inputs)}var sinh=op({sinh_});function slice1d_(x,begin,size){let $x=convertToTensor(x,"x","slice1d");return assert($x.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var truncatedNormal=op({truncatedNormal_});function unique_(x,axis=0){let $x=convertToTensor(x,"x","unique","string_or_numeric");assert($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i<condVals.length;i++)condVals[i]&&indices.push(i);let inBuffer=buffer(condShape,"int32"),out=buffer([indices.length,condShape.length],"int32");for(let i=0;i<indices.length;i++){let loc=inBuffer.indexToLoc(indices[i]),offset=i*condShape.length;out.values.set(loc,offset)}return out.toTensor()}async function whereAsync_(condition){let $condition=convertToTensor(condition,"condition","whereAsync","bool"),vals=await $condition.data(),res=whereImpl($condition.shape,vals);return condition!==$condition&&$condition.dispose(),res}var whereAsync=whereAsync_;async function booleanMaskAsync_(tensor2,mask,axis){let $tensor=convertToTensor(tensor2,"tensor","boolMask"),$mask=convertToTensor(mask,"mask","boolMask","bool"),axisFrom=axis==null?0:axis,maskDim=$mask.rank,tensorShape=$tensor.shape;assert(maskDim>0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i<axisFrom+maskDim;i++)leadingSize*=tensorShape[i];let targetTensorShape=tensorShape.slice(0,axisFrom).concat([leadingSize],tensorShape.slice(axisFrom+maskDim)),reshapedTensor=reshape($tensor,targetTensorShape),reshapedMask=reshape($mask,[-1]),positivePositions=await whereAsync(reshapedMask),indices=squeeze(positivePositions,[1]),res=gather(reshapedTensor,indices,axisFrom);return tensor2!==$tensor&&$tensor.dispose(),mask!==$mask&&$mask.dispose(),indices.dispose(),reshapedTensor.dispose(),reshapedMask.dispose(),positivePositions.dispose(),res}var booleanMaskAsync=booleanMaskAsync_;function notEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","notEqualStrict"),$b=convertToTensor(b,"b","notEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual($a,$b)}function lessStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessStrict"),$b=convertToTensor(b,"b","lessStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessStrict: "),less($a,$b)}function equalStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","equalStrict"),$b=convertToTensor(b,"b","equalStrict");return assertShapesMatch($a.shape,$b.shape,"Error in equalStrict: "),equal($a,$b)}function lessEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessEqualStrict"),$b=convertToTensor(b,"b","lessEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual($a,$b)}function greaterStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterStrict"),$b=convertToTensor(b,"b","greaterStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterStrict: "),greater($a,$b)}function greaterEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterEqualStrict"),$b=convertToTensor(b,"b","greaterEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual($a,$b)}var equalStrict=op({equalStrict_}),greaterEqualStrict=op({greaterEqualStrict_}),greaterStrict=op({greaterStrict_}),lessEqualStrict=op({lessEqualStrict_}),lessStrict=op({lessStrict_}),notEqualStrict=op({notEqualStrict_});function addStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","addStrict"),$b=convertToTensor(b,"b","addStrict");return assertShapesMatch($a.shape,$b.shape,"Error in addStrict: "),add2($a,$b)}function subStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","subStrict"),$b=convertToTensor(b,"b","subStrict");return assertShapesMatch($a.shape,$b.shape,"Error in subStrict: "),sub($a,$b)}function powStrict_(base2,exp4){return deprecationWarn("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch(base2.shape,exp4.shape,"Error in powStrict: "),pow(base2,exp4)}function mulStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");return assertShapesMatch($a.shape,$b.shape,"Error in multiplyStrict: "),mul($a,$b)}function divStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");return assertShapesMatch($a.shape,$b.shape,"Error in divideStrict: "),div($a,$b)}function modStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","modStrict"),$b=convertToTensor(b,"b","modStrict");return assertShapesMatch($a.shape,$b.shape,"Error in modStrict: "),mod($a,$b)}function minimumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","minimumStrict"),$b=convertToTensor(b,"b","minimumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in minimumStrict: "),minimum($a,$b)}function maximumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","maximumStrict"),$b=convertToTensor(b,"b","maximumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in maximumStrict: "),maximum($a,$b)}function squaredDifferenceStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","squaredDifferenceStrict"),$b=convertToTensor(b,"b","squaredDifferenceStrict");return assertShapesMatch($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference($a,$b)}var addStrict=op({addStrict_}),divStrict=op({divStrict_}),maximumStrict=op({maximumStrict_}),minimumStrict=op({minimumStrict_}),modStrict=op({modStrict_}),mulStrict=op({mulStrict_}),powStrict=op({powStrict_}),squaredDifferenceStrict=op({squaredDifferenceStrict_}),subStrict=op({subStrict_});function norm_(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor(x,"x","norm");let norm2=normImpl(x,ord,axis),keepDimsShape=norm2.shape;if(keepDims){let axes=parseAxisParam(axis,x.shape);keepDimsShape=expandShapeToKeepDim(norm2.shape,axes)}return reshape(norm2,keepDimsShape)}function normImpl(x,p2,axis=null){if(x.rank===0)return abs(x);if(x.rank!==1&&axis===null)return normImpl(reshape(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum2(abs(x),axis);if(p2===Infinity)return max(abs(x),axis);if(p2===-Infinity)return min(abs(x),axis);if(p2==="euclidean"||p2===2)return sqrt(sum2(pow(abs(x),scalar(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max(sum2(abs(x),axis[0]),axis[1]-1);if(p2===Infinity)return max(sum2(abs(x),axis[1]),axis[0]);if(p2===-Infinity)return min(sum2(abs(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt(sum2(square(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm=op({norm_});function movingAverage_(v,x,decay,step4,zeroDebias=!0){let $v=convertToTensor(v,"v","movingAverage"),$x=convertToTensor(x,"x","movingAverage"),$decay=convertToTensor(decay,"decay","movingAverage");assertTypesMatch($v,$x),assert(arraysEqual($v.shape,$x.shape),()=>"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i<x.shape.length;i++)noiseShape[i]==null&&x.shape[i]!=null?newDimension.push(x.shape[i]):newDimension.push(noiseShape[i]);return newDimension}return noiseShape}function dropout_(x,rate,noiseShape,seed){let $x=convertToTensor(x,"x","dropout");if(assert($x.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d(newValues,"float32")}async function inTopKAsync_(predictions,targets,k=1){let $predictions=convertToTensor(predictions,"predictions","inTopK"),$targets=convertToTensor(targets,"targets","inTopK");assert($predictions.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;b<batch;b++){let offset=b*size,vals=predictionsVals.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value),precision3[b]=0;for(let i=0;i<k;i++)if(valAndInd[i].index===targetsVals[b]){precision3[b]=1;break}}return predictions!==$predictions&&$predictions.dispose(),targets!==$targets&&$targets.dispose(),tensor(precision3,$targets.shape,"bool")}var inTopKAsync=inTopKAsync_,fused_ops_exports={};__export2(fused_ops_exports,{conv2d:()=>conv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal2.size;){let padLen=start+frameLength-signal2.size,pad3=concat([slice(signal2,start,frameLength-padLen),fill([padLen],padValue)]);output.push(pad3),start+=frameStep}return output.length===0?tensor2d([],[0,frameLength]):reshape(concat(output),[output.length,frameLength])}var frame=op({frame_});function stft_(signal2,frameLength,frameStep,fftLength,windowFn=hannWindow){fftLength==null&&(fftLength=enclosingPowerOfTwo(frameLength));let framedSignal=frame(signal2,frameLength,frameStep),windowedSignal=mul(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft(slice(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat(output)}var stft=op({stft_});function cropAndResize_(image3,boxes,boxInd,cropSize,method="bilinear",extrapolationValue=0){let $image=convertToTensor(image3,"image","cropAndResize"),$boxes=convertToTensor(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor(boxInd,"boxInd","cropAndResize","int32"),numBoxes=$boxes.shape[0];assert($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a<b?-1:0}function binarySearch_(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale22=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou2=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou2>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale22,iou2),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale22,iou2){let weight=Math.exp(scale22*iou2*iou2);return iou2<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1==0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1==0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul(sum2(mul(ys[j],x)),ys[j]);x=sub(x,proj)}return div(x,norm(x,"euclidean"))}));return inputIsTensor2D?stack(ys,0):ys}var gramSchmidt=op({gramSchmidt_});function qr_(x,fullMatrices=!1){if(assert(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE.tidy(()=>{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon32=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon32),l13=neg(mul($labels,log(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon32=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon32,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.rho,config.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config){return new cls(config.learningRate,config.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon,config.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config){return new cls(config.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config){return new cls(config.learningRate,config.momentum,config.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon32,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config){return new cls(config.learningRate,config.decay,config.momentum,config.epsilon,config.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon32,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon32=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon32)}static adadelta(learningRate=.001,rho=.95,epsilon32=null){return new AdadeltaOptimizer(learningRate,rho,epsilon32)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon32=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon32,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log2,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(inSize){return inSize<=PARALLELIZE_THRESHOLD?inSize:nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)))}function getImageCenter(center,imageHeight,imageWidth){let centerX=imageWidth*(typeof center=="number"?center:center[0]),centerY=imageHeight*(typeof center=="number"?center:center[1]);return[centerX,centerY]}function getReshaped(inputShape,blockShape,prod5,batchToSpace=!0){let reshaped=[];if(batchToSpace)reshaped=reshaped.concat(blockShape.slice(0)),reshaped.push(inputShape[0]/prod5),reshaped=reshaped.concat(inputShape.slice(1));else{reshaped=reshaped.concat(inputShape[0]);let spatialLength=blockShape.length;for(let i=0;i<spatialLength;++i)reshaped=reshaped.concat([inputShape[i+1]/blockShape[i],blockShape[i]]);reshaped=reshaped.concat(inputShape.slice(spatialLength+1))}return reshaped}function getPermuted(reshapedRank,blockShapeRank,batchToSpace=!0){let permuted=[];if(batchToSpace){permuted.push(blockShapeRank);for(let i=blockShapeRank+1;i<reshapedRank;++i)i<=2*blockShapeRank?(permuted.push(i),permuted.push(i-(blockShapeRank+1))):permuted.push(i)}else{let permutedBeforeBatch=[],permutedAfterBatch=[];for(let i=1;i<reshapedRank;++i)i>=blockShapeRank*2+1||i%2==1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i<inputShape.length;++i)i<=blockShape.length?batchToSpace?reshapedPermuted.push(blockShape[i-1]*inputShape[i]):reshapedPermuted.push(inputShape[i]/blockShape[i-1]):reshapedPermuted.push(inputShape[i]);return reshapedPermuted}function getSliceBeginCoords(crops,blockShape){let sliceBeginCoords=[0];for(let i=0;i<blockShape;++i)sliceBeginCoords.push(crops[i][0]);return sliceBeginCoords}function getSliceSize(uncroppedShape,crops,blockShape){let sliceSize=uncroppedShape.slice(0,1);for(let i=0;i<blockShape;++i)sliceSize.push(uncroppedShape[i+1]-crops[i][0]-crops[i][1]);return sliceSize}var SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429;function warn(...msg){env().getBool("IS_TEST")||console.warn(...msg)}function log2(...msg){env().getBool("IS_TEST")||console.log(...msg)}function mergeRealAndImagArrays(real4,imag4){if(real4.length!==imag4.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`);let result=new Float32Array(real4.length*2);for(let i=0;i<result.length;i+=2)result[i]=real4[i/2],result[i+1]=imag4[i/2];return result}function splitRealAndImagArrays(complex4){let real4=new Float32Array(complex4.length/2),imag4=new Float32Array(complex4.length/2);for(let i=0;i<complex4.length;i+=2)real4[i/2]=complex4[i],imag4[i/2]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithEvenIndex(complex4){let len=Math.ceil(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=0;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithOddIndex(complex4){let len=Math.floor(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=2;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function getComplexWithIndex(complex4,index){let real4=complex4[index*2],imag4=complex4[index*2+1];return{real:real4,imag:imag4}}function assignToTypedArray(data,real4,imag4,index){data[index*2]=real4,data[index*2+1]=imag4}function exponents(n,inverse){let real4=new Float32Array(n/2),imag4=new Float32Array(n/2);for(let i=0;i<Math.ceil(n/2);i++){let x=(inverse?2:-2)*Math.PI*(i/n);real4[i]=Math.cos(x),imag4[i]=Math.sin(x)}return{real:real4,imag:imag4}}function exponent(k,n,inverse){let x=(inverse?2:-2)*Math.PI*(k/n),real4=Math.cos(x),imag4=Math.sin(x);return{real:real4,imag:imag4}}function prepareSplitSize(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert(x.shape[axis]%numOrSizeSplits==0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)dim!==axis?outShape.push(aShape[dim]):outShape.push(numSegments);return outShape}function collectGatherOpShapeInfo(x,indices,axis,batchDims){let indicesRank=indices.shape.length,xRank=x.shape.length;if(batchDims!==0&&(batchDims<-indicesRank||batchDims>indicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
     ${xRank}).`);if(axis<batchDims)throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);for(let i=0;i<batchDims;++i)if(x.shape[i]!==indices.shape[i])throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`);let dimSize=x.shape[axis],outputShape=[],batchSize=1,outerSize=1,sliceSize=1;for(let i=0;i<batchDims;++i)outputShape.push(x.shape[i]),batchSize*=x.shape[i];for(let i=batchDims;i<axis;i++)outputShape.push(x.shape[i]),outerSize*=x.shape[i];for(let i=batchDims;i<indicesRank;i++)outputShape.push(indices.shape[i]);for(let i=axis+1;i<xRank;i++)outputShape.push(x.shape[i]),sliceSize*=x.shape[i];return{batchSize,sliceSize,outerSize,dimSize,outputShape}}function castTensor(x,dtype,backend2){if(dtype==="complex64"){if(x.dtype==="complex64")return x.clone();let zerosTensor=zeros(x.shape),floatX=cast(x,"float32"),result=backend2.complex(floatX,zerosTensor);return zerosTensor.dispose(),floatX.dispose(),result}if(!hasEncodingLoss(x.dtype,dtype))return ENGINE.makeTensorFromDataId(x.dataId,x.shape,dtype);if(x.dtype==="complex64"){let real4=backend2.real(x),result=cast(real4,dtype);return real4.dispose(),result}if(dtype==="int32")return backend2.int(x);if(dtype==="bool"){let zero=scalar(0,x.dtype),result=backend2.notEqual(x,zero);return zero.dispose(),result}else throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}function reshapeTensor(x,shape){return ENGINE.makeTensorFromDataId(x.dataId,shape,x.dtype)}function fromUint8ToStringArray(vals){try{return vals.map(val=>decodeString(val))}catch(err){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`)}}function fromStringArrayToUint8(strings){return strings.map(s=>encodeString(s))}var kernel_impls_exports={};__export2(kernel_impls_exports,{nonMaxSuppressionV3Impl:()=>nonMaxSuppressionV3Impl,nonMaxSuppressionV4Impl:()=>nonMaxSuppressionV4Impl,nonMaxSuppressionV5Impl:()=>nonMaxSuppressionV5Impl,whereImpl:()=>whereImpl});var absGradConfig={kernelName:Abs,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,step(cast(x,"float32"),-1))}}},acosGradConfig={kernelName:Acos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square(cast(x,"float32")),b=sqrt(sub(scalar(1),a));return neg(div(dy,b))}}}},acoshGradConfig={kernelName:Acosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(sub(square(cast(x,"float32")),1));return div(dy,a)}}}},addGradConfig={kernelName:Add,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}},addNGradConfig={kernelName:AddN,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}},argMaxGradConfig={kernelName:ArgMax,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},argMinGradConfig={kernelName:ArgMin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},asinGradConfig={kernelName:Asin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sqrt(sub(scalar(1),square(cast(x,"float32")))))}}},asinhGradConfig={kernelName:Asinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(add2(scalar(1),square(cast(x,"float32"))));return div(dy,a)}}}},atan2GradConfig={kernelName:Atan2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let d=add2(square(a),square(b)),res=mul(dy,div(b,d)),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let d=add2(square(a),square(b)),res=neg(mul(dy,div(a,d))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}},atanGradConfig={kernelName:Atan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(square(cast(x,"float32")),1))}}},atanhGradConfig={kernelName:Atanh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sub(scalar(1),square(cast(x,"float32"))))}}};function avgPool3dGrad_(dy,input2,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","avgPool3dGrad"),$input=convertToTensor(input2,"input","avgPool3dGrad"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert(dy5D.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dGrad=op({avgPool3dGrad_}),avgPool3DGradConfig={kernelName:AvgPool3D,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dGrad(dy,x,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function avgPoolGrad_(dy,input2,filterSize,strides,pad3){let $dy=convertToTensor(dy,"dy","avgPoolGrad"),$input=convertToTensor(input2,"input","avgPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert(dy4D.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`),assert(input4D.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);let inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad3},res=ENGINE.runKernel(AvgPoolGrad,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolGrad=op({avgPoolGrad_}),avgPoolGradConfig={kernelName:AvgPool,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>avgPoolGrad(dy,x,filterSize,strides,pad3)}}},batchMatMulGradConfig={kernelName:BatchMatMul,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul(dy,b,!1,!0),b:()=>matMul(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul(dy,b,!1,!1),b:()=>matMul(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul(b,dy,!1,!0),b:()=>matMul(a,dy,!1,!1)}:{a:()=>matMul(b,dy,!0,!0),b:()=>matMul(dy,a,!0,!0)}}},batchToSpaceNDGradConfig={kernelName:BatchToSpaceND,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND(dy,blockShape,crops)}}},broadcastToGradConfig={kernelName:BroadcastTo,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i<reps.length;i++)reps[i]>1&&axes.push(i);return{x:()=>sum2(dy,axes,!0)}}},castGradConfig={kernelName:Cast,gradFunc:dy=>({x:()=>dy.clone()})},ceilGradConfig={kernelName:Ceil,gradFunc:dy=>({x:()=>zerosLike(dy)})},clipByValueGradConfig={kernelName:ClipByValue,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where(logicalAnd(greaterEqual(x,clipValueMin),lessEqual(x,clipValueMax)),dy,zerosLike(dy))}}},complexAbsGradConfig={kernelName:ComplexAbs,inputsToSave:["x"],gradFunc:absGradConfig.gradFunc},concatGradConfig={kernelName:Concat,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]);return split(dy,sizeSplits,$axis).map(t=>()=>t)}},conv2DGradConfig={kernelName:Conv2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad3,dataFormat}=attrs;return assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput(x4D.shape,dy,$filter,strides,pad3,dataFormat),filter:()=>conv2DBackpropFilter(x4D,dy,$filter.shape,strides,pad3,dataFormat)}}},conv2DBackpropInputGradConfig={kernelName:Conv2DBackpropInput,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d(ddx,filter,strides,pad3,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter(ddx,dy,filter.shape,strides,pad3,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_(x,dy,filterShape,strides,pad3){let x5D=x;x.rank===4&&(x5D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad3,filterShape};return ENGINE.runKernel(Conv3DBackpropFilterV2,inputs,attrs)}var conv3DBackpropFilter=op({conv3DBackpropFilter_}),conv3DGradConfig={kernelName:Conv3D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3}=attrs;assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput(x5D.shape,dy,$filter,strides,pad3),filter:()=>conv3DBackpropFilter(x5D,dy,$filter.shape,strides,pad3)}}},cosGradConfig={kernelName:Cos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(neg(sin(cast(x,"float32"))),dy)}}},coshGradConfig={kernelName:Cosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(sinh(cast(x,"float32")),dy)}}},cumsumGradConfig={kernelName:Cumsum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse5}=attrs;return{x:()=>{let permutation=getAxesPermutation([axis],x.rank),out=cumsum(dy,axis,exclusive,!reverse5);return permutation!=null&&(out=transpose(out,permutation)),out}}}},depthwiseConv2dNativeGradConfig={kernelName:DepthwiseConv2dNative,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert(tupleValuesAreOne($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),{x:()=>depthwiseConv2dNativeBackpropInput(x.shape,dy,filter,strides,pad3,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter(x,dy,filter.shape,strides,pad3,dilations,dimRoundingMode)}}},dilation2dGradConfig={kernelName:Dilation2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE.runKernel(Dilation2DBackpropInput,inputInputs,attrs),filter:()=>ENGINE.runKernel(Dilation2DBackpropFilter,filterInputs,attrs)}}},eluGradConfig={kernelName:Elu,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,inputs={dy,y};return{x:()=>ENGINE.runKernel(EluGrad,inputs)}}},erfGradConfig={kernelName:Erf,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul(exp(neg(square(x))),2/Math.sqrt(Math.PI));return{x:()=>mul(dy,a)}}},expGradConfig={kernelName:Exp,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,y)}}},expandDimsGradConfig={kernelName:ExpandDims,inputsToSave:["input"],gradFunc:(dy,saved)=>{let[input2]=saved;return{input:()=>reshape(dy,input2.shape)}}},expm1GradConfig={kernelName:Expm1,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,exp(x))}}},floorGradConfig={kernelName:Floor,gradFunc:dy=>({x:()=>zerosLike(dy)})},floorDivGradConfig={kernelName:FloorDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))}}}},fusedBatchNormGradConfig={kernelName:FusedBatchNorm,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean4,variance,scale22]=saved,scaleValue=scale22==null?scalar(1):scale22,reductionAxes=getReductionAxes(mean4.shape,x.shape),tileShape=[];if(mean4.rank===1){for(let i=0;i<x.shape.length-1;++i)tileShape.push(x.shape[i]);tileShape.push(1)}let xMinusMean=sub(x,mean4),dyTimesScaleValue=mul(dy,scaleValue),oneOverSqrtVariance=rsqrt(add2(variance,scalar(varianceEpsilon))),minusHalfRCube=mul(mul(mul(oneOverSqrtVariance,oneOverSqrtVariance),oneOverSqrtVariance),scalar(-.5));return{x:()=>mean4.rank===1?reshape(mul(mul(dy,tile(reshape(oneOverSqrtVariance,[1,1,1,mean4.shape[0]]),tileShape)),scaleValue),x.shape):reshape(mul(mul(dy,oneOverSqrtVariance),scaleValue),x.shape),mean:()=>{let meanDer=mul(mul(oneOverSqrtVariance,scalar(-1)),dyTimesScaleValue);return mean4.rank===1&&(meanDer=sum2(meanDer,reductionAxes)),reshape(meanDer,mean4.shape)},variance:()=>{let varianceDer=mul(mul(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean4.rank===1&&(varianceDer=sum2(varianceDer,reductionAxes)),reshape(varianceDer,mean4.shape)},scale:()=>{let xMinusMean2TimesRsqrt=mul(xMinusMean,oneOverSqrtVariance),scaleDer=mul(dy,xMinusMean2TimesRsqrt);return mean4.rank===1&&(scaleDer=sum2(scaleDer,reductionAxes)),reshape(scaleDer,mean4.shape)},offset:()=>{let offsetDer=dy;return mean4.rank===1&&(offsetDer=sum2(offsetDer,reductionAxes)),reshape(offsetDer,mean4.shape)}}}},gatherGradConfig={kernelName:GatherV2,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam(axis,x.shape)[0];return{x:()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange(0,outerDims),innerAxesIndices=arrayRange(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat([outerShape,[indicesSize],innerShape]),values=reshape(dy,valuesShape),reshapedIndices=reshape(indices,[indicesSize]),transposeDims=arrayConcat([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose(values,transposeDims),paramsGrad=unsortedSegmentSum(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation(transposeDims);return paramsGrad=transpose(paramsGrad,invertTransposeDims),paramsGrad},indices:()=>indices}}};function arrayRange(start,stop){let result=[];for(let i=start;i<stop;++i)result.push(i);return result}function arrayConcat(arrays){let result=[];for(let i=0;i<arrays.length;++i)for(let j=0;j<arrays[i].length;++j)result.push(arrays[i][j]);return result}var greaterEqualGradConfig={kernelName:GreaterEqual,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>zerosLike(a),b:()=>zerosLike(b)}}},identityGradConfig={kernelName:Identity,gradFunc:dy=>({x:()=>cast(dy,"float32")})},isFiniteGradConfig={kernelName:IsFinite,gradFunc:dy=>({x:()=>zerosLike(dy)})},isInfGradConfig={kernelName:IsInf,gradFunc:dy=>({x:()=>zerosLike(dy)})},isNanGradConfig={kernelName:IsNan,gradFunc:dy=>({x:()=>zerosLike(dy)})},leakyReluGradConfig={kernelName:LeakyRelu,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{alpha}=attrs,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha))}}},log1pGradConfig={kernelName:Log1p,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(x,1))}}},logGradConfig={kernelName:Log,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,cast(x,"float32"))}}},logSoftmaxGradConfig={kernelName:LogSoftmax,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax6=exp(value);return sub(dy,mul(sum2(dy,axis,keepDims),softmax6))}}}};function localResponseNormalizationBackprop_(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE.runKernel(LRNGrad,inputs,attrs)}var localResponseNormalizationBackprop=op({localResponseNormalizationBackprop_}),lrnGradConfig={kernelName:LRN,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax(dy,y,xOrig,origAxes){return y.rank<xOrig.rank&&(y=reshape(y,expandShapeToKeepDim(y.shape,origAxes))),dy.rank<xOrig.rank&&(dy=reshape(dy,expandShapeToKeepDim(dy.shape,origAxes))),{x:()=>mul(dy,cast(equal(xOrig,y),dy.dtype))}}var maxGradConfig={kernelName:Max,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam(reductionIndices,x.shape),maxGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}},maximumGradConfig={kernelName:Maximum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(greaterEqual(a,b),"float32")),b:()=>mul(dy,cast(less(a,b),"float32"))}}};function maxPool3dGrad_(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPool3dGrad"),$input=convertToTensor(input2,"input","maxPool3dGrad"),$output=convertToTensor(output,"output","maxPool3dGrad"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert(dy5D.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(output5D.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dGrad=op({maxPool3dGrad_}),maxPool3DGradConfig={kernelName:MaxPool3D,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dGrad(dy,x,y,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function maxPoolGrad_(dy,input2,output,filterSize,strides,pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPoolGrad"),$input=convertToTensor(input2,"input","maxPoolGrad"),$output=convertToTensor(output,"output","maxPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert($dy.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`),assert($input.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad3,dimRoundingMode};return ENGINE.runKernel(MaxPoolGrad,inputs,attrs)}var maxPoolGrad=op({maxPoolGrad_}),maxPoolGradConfig={kernelName:MaxPool,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>maxPoolGrad(dy,x,y,filterSize,strides,pad3)}}},meanGradConfig={kernelName:Mean,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis}=attrs,axes=parseAxisParam(axis,x.shape),reduceShape=computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=sizeFromShape(reduceShape);return{x:()=>{let expandedDyShape=x.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape);return div(mul(expandedDy,ones2(x.shape,"float32")),reduceSize)}}}},minGradConfig={kernelName:Min,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam(axis,x.shape),minGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>minGrad.x()}}},minimumGradConfig={kernelName:Minimum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(lessEqual(a,b),"float32")),b:()=>mul(dy,cast(greater(a,b),"float32"))}}},mirrorPadGradConfig={kernelName:MirrorPad,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},modGradConfig={kernelName:Mod,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(dy,reduceAxes),a.shape):dy},b:()=>{let res=mul(dy,neg(floor(div(a,b)))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}},multiplyGradConfig={kernelName:Multiply,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=mul(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}},negGradConfig={kernelName:Neg,gradFunc:dy=>({x:()=>neg(dy)})},oneHotGradConfig={kernelName:OneHot,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros(indices.shape,"float32")}}},onesLikeGradConfig={kernelName:OnesLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},packGradConfig={kernelName:Pack,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return 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mask=greater(x,scalar(0)),scaleAlpha2=scalar(SELU_SCALEALPHA),scale22=scalar(SELU_SCALE),greaterThanZeroDer=mul(dy,scale22),lessEqualZeroDer=mul(mul(dy,scaleAlpha2),exp(cast(x,"float32")));return where(mask,greaterThanZeroDer,lessEqualZeroDer)}}}},sigmoidGradConfig={kernelName:Sigmoid,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,mul(y,sub(scalar(1),y)))}}},signGradConfig={kernelName:Sign,gradFunc:dy=>({x:()=>zerosLike(dy)})},sinGradConfig={kernelName:Sin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cos(cast(x,"float32")),dy)}}},sinhGradConfig={kernelName:Sinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cosh(cast(x,"float32")),dy)}}},sliceGradConfig={kernelName:Slice,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams(x,begin,size),paddings=[];for(let 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res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(neg(res),b.shape)}}}},sumGradConfig={kernelName:Sum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs;parseAxisParam(axis,x.shape).forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=mul(expandedDy,ones2(x.shape,"float32"));return{x:()=>derX}}},tanGradConfig={kernelName:Tan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,square(cos(x)))}}},tanhGradConfig={kernelName:Tanh,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(sub(scalar(1),square(y)),dy)}}},tileGradConfig={kernelName:Tile,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs;return{x:()=>{let 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transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation(perm);return{x:()=>transpose(dy,undoPerm)}}},unpackGradConfig={kernelName:Unpack,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack(dy,axis)}}},unsortedSegmentSumGradConfig={kernelName:UnsortedSegmentSum,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved;return{x:()=>gatherDropNegatives(dy,segmentIds)}}};function gatherDropNegatives(x,indices){let zeroClippedIndices=maximum(indices,zerosLike(indices)),gathered=gather(x,zeroClippedIndices),isPositive=greaterEqual(indices,scalar(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i<numIters;++i)isPositive=expandDims(isPositive,i+1);isPositive=logicalAnd(isPositive,ones2(gathered.shape,"bool"));let zeroSlice=zerosLike(gathered);return where(isPositive,gathered,zeroSlice)}var zerosLikeGradConfig={kernelName:ZerosLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},gradConfigs=[absGradConfig,acosGradConfig,acoshGradConfig,addGradConfig,addNGradConfig,argMaxGradConfig,argMinGradConfig,asinGradConfig,asinhGradConfig,atan2GradConfig,atanGradConfig,atanhGradConfig,avgPool3DGradConfig,avgPoolGradConfig,batchMatMulGradConfig,batchToSpaceNDGradConfig,broadcastToGradConfig,castGradConfig,ceilGradConfig,clipByValueGradConfig,complexAbsGradConfig,concatGradConfig,conv2DBackpropInputGradConfig,conv2DGradConfig,conv3DGradConfig,cosGradConfig,coshGradConfig,cumsumGradConfig,depthwiseConv2dNativeGradConfig,dilation2dGradConfig,divGradConfig,eluGradConfig,erfGradConfig,expGradConfig,expandDimsGradConfig,expm1GradConfig,floorDivGradConfig,floorGradConfig,fusedBatchNormGradConfig,gatherGradConfig,greaterEqualGradConfig,identityGradConfig,isFiniteGradConfig,isInfGradConfig,isNanGradConfig,leakyReluGradConfig,log1pGradConfig,logGradConfig,logSoftmaxGradConfig,lrnGradConfig,maxGradConfig,maxGradConfig,maximumGradConfig,maxPool3DGradConfig,maxPoolGradConfig,meanGradConfig,minGradConfig,minimumGradConfig,mirrorPadGradConfig,modGradConfig,multiplyGradConfig,negGradConfig,oneHotGradConfig,onesLikeGradConfig,packGradConfig,padV2GradConfig,padV2GradConfig,powGradConfig,preluGradConfig,reciprocalGradConfig,relu6GradConfig,reluGradConfig,reshapeGradConfig,resizeBilinearGradConfig,resizeNearestNeighborGradConfig,reverseGradConfig,roundGradConfig,rsqrtGradConfig,selectGradConfig,seluGradConfig,sigmoidGradConfig,signGradConfig,sinGradConfig,sinhGradConfig,sliceGradConfig,softmaxGradConfig,softplusGradConfig,spaceToBatchNDGradConfig,spaceToBatchNDGradConfig,splitVGradConfig,splitVGradConfig,sqrtGradConfig,squaredDifferenceGradConfig,squareGradConfig,stepGradConfig,subGradConfig,sumGradConfig,tanGradConfig,tanhGradConfig,tileGradConfig,transposeGradConfig,unpackGradConfig,unsortedSegmentSumGradConfig,zerosLikeGradConfig];for(let gradientConfig of gradConfigs)registerGradient(gradientConfig);Tensor.prototype.abs=function(){return this.throwIfDisposed(),abs(this)};Tensor.prototype.acos=function(){return this.throwIfDisposed(),acos(this)};Tensor.prototype.acosh=function(){return this.throwIfDisposed(),acosh(this)};Tensor.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict(this,x)};Tensor.prototype.add=function(b){return this.throwIfDisposed(),add2(this,b)};Tensor.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all(this,axis,keepDims)};Tensor.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any(this,axis,keepDims)};Tensor.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax(this,axis)};Tensor.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin(this,axis)};Tensor.prototype.asScalar=function(){return this.throwIfDisposed(),assert(this.size===1,()=>"The array must have only 1 element."),reshape(this,[])};Tensor.prototype.asType=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.as1D=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape(this,[rows,columns])};Tensor.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape(this,[rows,columns,depth])};Tensor.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2])};Tensor.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2,depth3])};Tensor.prototype.asin=function(){return this.throwIfDisposed(),asin(this)};Tensor.prototype.asinh=function(){return this.throwIfDisposed(),asinh(this)};Tensor.prototype.atan=function(){return this.throwIfDisposed(),atan(this)};Tensor.prototype.atan2=function(b){return this.throwIfDisposed(),atan2(this,b)};Tensor.prototype.atanh=function(){return this.throwIfDisposed(),atanh(this)};Tensor.prototype.avgPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),avgPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND(this,blockShape,crops)};Tensor.prototype.batchNorm=function(mean4,variance,offset,scale22,varianceEpsilon){return this.throwIfDisposed(),batchNorm(this,mean4,variance,offset,scale22,varianceEpsilon)};Tensor.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo(this,shape)};Tensor.prototype.cast=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.ceil=function(){return this.throwIfDisposed(),ceil(this)};Tensor.prototype.clipByValue=function(min6,max6){return this.throwIfDisposed(),clipByValue(this,min6,max6)};Tensor.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor&&(x=[x]),concat([this,...x],axis)};Tensor.prototype.conv1d=function(filter,stride,pad3,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d(this,filter,stride,pad3,dataFormat,dilation,dimRoundingMode)};Tensor.prototype.conv2dTranspose=function(filter,outputShape,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose(this,filter,outputShape,strides,pad3,dimRoundingMode)};Tensor.prototype.conv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.cos=function(){return this.throwIfDisposed(),cos(this)};Tensor.prototype.cosh=function(){return this.throwIfDisposed(),cosh(this)};Tensor.prototype.cumsum=function(axis,exclusive,reverse5){return this.throwIfDisposed(),cumsum(this,axis,exclusive,reverse5)};Tensor.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace(this,blockSize,dataFormat)};Tensor.prototype.depthwiseConv2D=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return deprecationWarn("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.depthwiseConv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.dilation2d=function(filter,strides,pad3,dilations,dataFormat){return this.throwIfDisposed(),dilation2d(this,filter,strides,pad3,dilations,dataFormat)};Tensor.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan(this,b)};Tensor.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict(this,x)};Tensor.prototype.div=function(b){return this.throwIfDisposed(),div(this,b)};Tensor.prototype.dot=function(b){return this.throwIfDisposed(),dot(this,b)};Tensor.prototype.elu=function(){return this.throwIfDisposed(),elu(this)};Tensor.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict(this,x)};Tensor.prototype.equal=function(b){return this.throwIfDisposed(),equal(this,b)};Tensor.prototype.erf=function(){return this.throwIfDisposed(),erf(this)};Tensor.prototype.exp=function(){return this.throwIfDisposed(),exp(this)};Tensor.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims(this,axis)};Tensor.prototype.expm1=function(){return this.throwIfDisposed(),expm1(this)};Tensor.prototype.fft=function(){return this.throwIfDisposed(),fft(this)};Tensor.prototype.flatten=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.floor=function(){return this.throwIfDisposed(),floor(this)};Tensor.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv(this,b)};Tensor.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather(this,indices,axis)};Tensor.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict(this,x)};Tensor.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual(this,b)};Tensor.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict(this,x)};Tensor.prototype.greater=function(b){return this.throwIfDisposed(),greater(this,b)};Tensor.prototype.ifft=function(){return this.throwIfDisposed(),ifft(this)};Tensor.prototype.irfft=function(){return this.throwIfDisposed(),irfft(this)};Tensor.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite2(this)};Tensor.prototype.isInf=function(){return this.throwIfDisposed(),isInf(this)};Tensor.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN2(this)};Tensor.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu(this,alpha)};Tensor.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict(this,x)};Tensor.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual(this,b)};Tensor.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict(this,x)};Tensor.prototype.less=function(b){return this.throwIfDisposed(),less(this,b)};Tensor.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization(this,depthRadius,bias,alpha,beta)};Tensor.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid(this)};Tensor.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax(this,axis)};Tensor.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp(this,axis,keepDims)};Tensor.prototype.log=function(){return this.throwIfDisposed(),log(this)};Tensor.prototype.log1p=function(){return this.throwIfDisposed(),log1p(this)};Tensor.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd(this,b)};Tensor.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot(this)};Tensor.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr(this,b)};Tensor.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor(this,b)};Tensor.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul(this,b,transposeA,transposeB)};Tensor.prototype.maxPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),maxPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max(this,axis,keepDims)};Tensor.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict(this,x)};Tensor.prototype.maximum=function(b){return this.throwIfDisposed(),maximum(this,b)};Tensor.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean(this,axis,keepDims)};Tensor.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min(this,axis,keepDims)};Tensor.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict(this,x)};Tensor.prototype.minimum=function(b){return this.throwIfDisposed(),minimum(this,b)};Tensor.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad(this,paddings,mode)};Tensor.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict(this,x)};Tensor.prototype.mod=function(b){return this.throwIfDisposed(),mod(this,b)};Tensor.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict(this,x)};Tensor.prototype.mul=function(b){return this.throwIfDisposed(),mul(this,b)};Tensor.prototype.neg=function(){return this.throwIfDisposed(),neg(this)};Tensor.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm(this,ord,axis,keepDims)};Tensor.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict(this,x)};Tensor.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual(this,b)};Tensor.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot(this,depth,onValue,offValue)};Tensor.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike(this)};Tensor.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad(this,paddings,constantValue)};Tensor.prototype.pool=function(windowShape,poolingType,padding,dilationRate,strides){return this.throwIfDisposed(),pool(this,windowShape,poolingType,padding,dilationRate,strides)};Tensor.prototype.powStrict=function(exp4){return this.throwIfDisposed(),powStrict(this,exp4)};Tensor.prototype.pow=function(exp4){return this.throwIfDisposed(),pow(this,exp4)};Tensor.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu(this,alpha)};Tensor.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod(this,axis,keepDims)};Tensor.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal(this)};Tensor.prototype.relu=function(){return this.throwIfDisposed(),relu(this)};Tensor.prototype.relu6=function(){return this.throwIfDisposed(),relu6(this)};Tensor.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape(this,x.shape)};Tensor.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape(this,shape)};Tensor.prototype.resizeBilinear=function(newShape2D,alignCorners,halfPixelCenters){return this.throwIfDisposed(),resizeBilinear(this,newShape2D,alignCorners,halfPixelCenters)};Tensor.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners,halfFloatCenters){return this.throwIfDisposed(),resizeNearestNeighbor(this,newShape2D,alignCorners,halfFloatCenters)};Tensor.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse(this,axis)};Tensor.prototype.rfft=function(){return this.throwIfDisposed(),rfft(this)};Tensor.prototype.round=function(){return this.throwIfDisposed(),round2(this)};Tensor.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt(this)};Tensor.prototype.selu=function(){return this.throwIfDisposed(),selu(this)};Tensor.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d(this,depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat)};Tensor.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid(this)};Tensor.prototype.sign=function(){return this.throwIfDisposed(),sign(this)};Tensor.prototype.sin=function(){return this.throwIfDisposed(),sin(this)};Tensor.prototype.sinh=function(){return this.throwIfDisposed(),sinh(this)};Tensor.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice(this,begin,size)};Tensor.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax(this,dim)};Tensor.prototype.softplus=function(){return this.throwIfDisposed(),softplus(this)};Tensor.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND(this,blockShape,paddings)};Tensor.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split(this,numOrSizeSplits,axis)};Tensor.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt(this)};Tensor.prototype.square=function(){return this.throwIfDisposed(),square(this)};Tensor.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference(this,b)};Tensor.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict(this,x)};Tensor.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze(this,axis)};Tensor.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor?[this,x]:[this,...x];return stack(tensorsToBeStacked,axis)};Tensor.prototype.step=function(alpha){return this.throwIfDisposed(),step(this,alpha)};Tensor.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict(this,x)};Tensor.prototype.sub=function(b){return this.throwIfDisposed(),sub(this,b)};Tensor.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum2(this,axis,keepDims)};Tensor.prototype.tan=function(){return this.throwIfDisposed(),tan(this)};Tensor.prototype.tanh=function(){return this.throwIfDisposed(),tanh2(this)};Tensor.prototype.tile=function(reps){return this.throwIfDisposed(),tile(this,reps)};Tensor.prototype.toBool=function(){return this.throwIfDisposed(),cast(this,"bool")};Tensor.prototype.toFloat=function(){return this.throwIfDisposed(),cast(this,"float32")};Tensor.prototype.toInt=function(){return this.throwIfDisposed(),cast(this,"int32")};Tensor.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk(this,k,sorted)};Tensor.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose(this,perm)};Tensor.prototype.unique=function(axis){return this.throwIfDisposed(),unique(this,axis)};Tensor.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum(this,segmentIds,numSegments)};Tensor.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack(this,axis)};Tensor.prototype.where=function(condition,x){return this.throwIfDisposed(),where(condition,this,x)};Tensor.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike(this)};var exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon(){return _epsilon==null&&(_epsilon=backend().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;i<numValues;i++)newArray=newArray.concat(value);return newArray}else{let newArray=new Array(numValues);return newArray.fill(value),newArray}}function assert2(val,message){if(!val)throw new AssertionError(message)}function count(array2,refernce){let counter=0;for(let item of array2)item===refernce&&counter++;return counter}function singletonOrArray(xs){return xs.length===1?xs[0]:xs}function toList(x){return Array.isArray(x)?x:[x]}function toSnakeCase(name){let insecure=name.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return insecure[0]!=="_"?insecure:"private"+insecure}function toCamelCase(identifier){return identifier.length<=1||identifier.indexOf("_")===-1?identifier:identifier.replace(/[_]+(\w|$)/g,(m,p1)=>p1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;let dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config){if(!(config==null||typeof config!="object"))if(Array.isArray(config))config.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config);for(let field of fields){let value=config[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
 2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config=identifier;if(config.className==null||config.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.
 'className' and 'config' must set.`);let className=config.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
-2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config.config);let returnObj=fromConfig(cls,config.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return a<b?-1:a>b?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique2(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now2=util_exports.now();return now2-lastTime<waitMs||(lastTime=now2,lastResult=f(...args)),lastResult}}function mapActivationToFusedKernel(activationName){return activationName==="relu"?"relu":activationName==="linear"?"linear":activationName==="elu"?"elu":null}function calcL2Norms(w,axis){return tidy(()=>sqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config){return new MinMaxNorm(config)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros2});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i<end;++i)prod5*=array2[i];return prod5}function toArray1D(array2){return array2=Array.isArray(array2)?new Float32Array(array2):array2,tensor1d(array2)}function min2(array2){return min(toArray1D(array2)).dataSync()[0]}function max2(array2){return max(toArray1D(array2)).dataSync()[0]}function range2(begin,end){if(end<begin)throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);let out=[];for(let i=begin;i<end;++i)out.push(i);return out}function cast2(x,dtype){return x.asType(dtype)}function expandDims2(x,axis=-1){let outShape=x.shape.slice();return axis<0&&(axis=outShape.length+axis+1),outShape.splice(axis,0,1),x.reshape(outShape)}function repeat(x,n){return tidy(()=>{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims2(x,1);return tile2(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile2(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square2(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu2(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale22=this.scale;if(this.mode==="fanIn"?scale22/=Math.max(1,fanIn):this.mode==="fanOut"?scale22/=Math.max(1,fanOut):scale22/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale22);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale22);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config={};return config.className=className,config.config={},deserializeInitializer(config)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros2(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity(args){return new Identity2(args)}function varianceScaling(config){return new VarianceScaling(config)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add3,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d3,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d4,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu3,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum2,minimum:()=>minimum2,multiply:()=>multiply,permute:()=>permute,prelu:()=>prelu2,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape2,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax2,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),!(this.inputSpec==null||this.inputSpec.length===0)){let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndex<inputs.length;inputIndex++){let x=inputs[inputIndex],spec=inputSpec[inputIndex];if(spec!=null){let ndim=x.rank;if(spec.ndim!=null&&ndim!==spec.ndim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);if(spec.maxNDim!=null&&ndim>spec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim<spec.minNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);if(spec.dtype!=null&&x.dtype!==spec.dtype)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);if(spec.axes){let xShape=x.shape;for(let key in spec.axes){let axis=Number(key),value=spec.axes[key],xShapeAtAxis=axis>=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i<spec.shape.length;++i){let specDim=spec.shape[i],dim=x.shape[i];if(specDim!=null&&dim!=null&&specDim!==dim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`)}}}}}call(inputs,kwargs){return inputs}invokeCallHook(inputs,kwargs){this._callHook!=null&&this._callHook(inputs,kwargs)}setCallHook(callHook){this._callHook=callHook}clearCallHook(){this._callHook=null}apply(inputs,kwargs){kwargs=kwargs||{},this.assertNotDisposed();let inputsList=toList(inputs),allAreSymbolic=!0;for(let input2 of inputsList)if(!(input2 instanceof SymbolicTensor)){allAreSymbolic=!1;break}let noneAreSymbolic=!0;for(let input2 of inputsList)if(input2 instanceof SymbolicTensor){noneAreSymbolic=!1;break}if(allAreSymbolic===noneAreSymbolic)throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return nameScope(this.name,()=>{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node2 of this.inboundNodes){let shapeString=JSON.stringify(node2.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length!==0){let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;i<paramValues.length;++i){let pv=paramValues[i],p2=params[i],w=weights[i];if(!util_exports.arraysEqual(pv.shape,w.shape))throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);weightValueTuples.push([p2,w])}batchSetValue(weightValueTuples)}})}addWeight(name,shape,dtype,initializer,regularizer,trainable,constraint){if(this._addedWeightNames.indexOf(name)!==-1)throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);this._addedWeightNames.push(name),dtype==null&&(dtype="float32"),this.fastWeightInitDuringBuild&&(initializer=getInitializer("zeros"));let initValue=initializer.apply(shape,dtype),weight=new LayerVariable(initValue,dtype,name,trainable,constraint);return initValue.dispose(),regularizer!=null&&this.addLoss(()=>regularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;i<outputTensors.length;i++)outputTensors[i].sourceLayer=this,outputTensors[i].nodeIndex=this.inboundNodes.length-1,outputTensors[i].tensorIndex=i}getConfig(){let config={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(config.batchInputShape=this.batchInputShape),this.dtype!=null&&(config.dtype=this.dtype),config}disposeWeights(){return this.weights.forEach(weight=>weight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount===0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node2=layer.inboundNodes[nodeIndex];if(node2.inboundLayers.length===0)return node2.inputTensors;{let sourceTensors=[];for(let i=0;i<node2.inboundLayers.length;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],previousSources=getSourceInputs(x,layer2,nodeIndex2);for(let x2 of previousSources)sourceTensors.indexOf(x2)===-1&&sourceTensors.push(x2)}return sourceTensors}}}var InputLayer=class extends Layer{constructor(args){super({dtype:args.dtype,name:args.name!=null?args.name:getUid("input").toString()});if(args.batchSize==null&&(args.batchSize=null),args.sparse==null&&(args.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=args.sparse,args.inputShape!=null&&args.batchInputShape!=null)throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let batchInputShape=args.batchInputShape;if(batchInputShape==null){if(args.inputShape==null)throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");batchInputShape=[args.batchSize].concat(args.inputShape)}else if(args.batchSize!=null)throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let dtype=args.dtype||"float32";this.batchInputShape=batchInputShape,this.dtype=dtype,this.inputSpec=[{shape:batchInputShape}];let inputTensor=new SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);inputTensor.nodeIndex=0,inputTensor.tensorIndex=0,new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[inputTensor],outputTensors:[inputTensor],inputMasks:[null],outputMasks:[null],inputShapes:[batchInputShape],outputShapes:[batchInputShape]})}apply(inputs,kwargs){throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};InputLayer.className="InputLayer";serialization_exports.registerClass(InputLayer);function Input(config){if(config.batchShape==null&&config.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(config.batchShape!=null&&config.shape!=null)throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let batchShape=config.batchShape;config.shape!=null&&batchShape==null&&(batchShape=[null].concat(config.shape));let dtype=config.dtype;return dtype==null&&(dtype="float32"),new InputLayer({batchInputShape:batchShape,name:config.name,dtype,sparse:config.sparse}).inboundNodes[0].outputTensors[0]}async function resolveScalarsInLogs(logs){if(logs!=null){let promises=[],keys=[],scalarsToDispose=[];for(let key in logs){let value=logs[key];if(typeof value!="number"){let valueScalar=value;promises.push(valueScalar.data()),keys.push(key),scalarsToDispose.push(valueScalar)}}if(promises.length>0){let values=await Promise.all(promises);for(let i=0;i<values.length;++i)logs[keys[i]]=values[i][0];dispose(scalarsToDispose)}}}function disposeTensorsInLogs(logs){if(logs!=null)for(let key in logs){let value=logs[key];typeof value!="number"&&value.dispose()}}var ModelLoggingVerbosity;(function(ModelLoggingVerbosity2){ModelLoggingVerbosity2[ModelLoggingVerbosity2.SILENT=0]="SILENT",ModelLoggingVerbosity2[ModelLoggingVerbosity2.VERBOSE=1]="VERBOSE"})(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));var DEFAULT_YIELD_EVERY_MS=125,BaseCallback=class{constructor(){this.validationData=null}setParams(params){this.params=params}async onEpochBegin(epoch,logs){}async onEpochEnd(epoch,logs){}async onBatchBegin(batch,logs){}async onBatchEnd(batch,logs){}async onTrainBegin(logs){}async onTrainEnd(logs){}setModel(model2){}},CallbackList=class{constructor(callbacks2,queueLength=10){callbacks2==null&&(callbacks2=[]),this.callbacks=callbacks2,this.queueLength=queueLength}append(callback){this.callbacks.push(callback)}setParams(params){for(let callback of this.callbacks)callback.setParams(params)}setModel(model2){for(let callback of this.callbacks)callback.setModel(model2)}async onEpochBegin(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochBegin(epoch,logs)}async onEpochEnd(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochEnd(epoch,logs)}async onBatchBegin(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchBegin(batch,logs)}async onBatchEnd(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchEnd(batch,logs)}async onTrainBegin(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainBegin(logs)}async onTrainEnd(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainEnd(logs)}},BaseLogger=class extends BaseCallback{constructor(){super()}async onEpochBegin(epoch){this.seen=0,this.totals={}}async onBatchEnd(batch,logs){logs==null&&(logs={});let batchSize=logs.size==null?0:logs.size;this.seen+=batchSize;for(let key in logs){let value=logs[key];if(typeof value=="number")this.totals.hasOwnProperty(key)||(this.totals[key]=0),this.totals[key]=this.totals[key]+value*batchSize;else{let oldTotalsToDispose;key in this.totals?oldTotalsToDispose=this.totals[key]:this.totals[key]=0;let total=tidy(()=>add2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log5=mul(div(1,this.seen),this.totals[key]);logs[key]=log5,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;i<valueArray.length;++i)if(typeof valueArray[i]!="number"){let valueScalar=valueArray[i];promises.push(valueScalar.data()),keys.push(key),indices.push(i)}}let values=await Promise.all(promises);for(let n=0;n<values.length;++n)this.history[keys[n]][indices[n]].dispose(),this.history[keys[n]][indices[n]]=values[n][0]}},CustomCallback=class extends BaseCallback{constructor(args,yieldEvery){super();if(this.currentEpoch=0,this.yieldEvery=yieldEvery||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=DEFAULT_YIELD_EVERY_MS),this.yieldEvery==="never"&&args.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");util_exports.isNumber(this.yieldEvery)&&(this.maybeWait=debounce(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=args.onTrainBegin,this.trainEnd=args.onTrainEnd,this.epochBegin=args.onEpochBegin,this.epochEnd=args.onEpochEnd,this.batchBegin=args.onBatchBegin,this.batchEnd=args.onBatchEnd,this.yield=args.onYield}async maybeWait(epoch,batch,logs){let ps=[];this.yield!=null&&(await resolveScalarsInLogs(logs),ps.push(this.yield(epoch,batch,logs))),ps.push(nextFrame()),await Promise.all(ps)}async onEpochBegin(epoch,logs){this.currentEpoch=epoch,this.epochBegin!=null&&(await resolveScalarsInLogs(logs),await this.epochBegin(epoch,logs))}async onEpochEnd(epoch,logs){let ps=[];this.epochEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.epochEnd(epoch,logs))),this.yieldEvery==="epoch"&&ps.push(nextFrame()),await Promise.all(ps)}async onBatchBegin(batch,logs){this.batchBegin!=null&&(await resolveScalarsInLogs(logs),await this.batchBegin(batch,logs))}async onBatchEnd(batch,logs){let ps=[];this.batchEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.batchEnd(batch,logs))),this.yieldEvery==="batch"?ps.push(nextFrame()):util_exports.isNumber(this.yieldEvery)&&ps.push(this.maybeWait(this.currentEpoch,batch,logs)),await Promise.all(ps)}async onTrainBegin(logs){this.trainBegin!=null&&(await resolveScalarsInLogs(logs),await this.trainBegin(logs))}async onTrainEnd(logs){this.trainEnd!=null&&(await resolveScalarsInLogs(logs),await this.trainEnd(logs))}};function standardizeCallbacks(callbacks2,yieldEvery){return callbacks2==null&&(callbacks2={}),callbacks2 instanceof BaseCallback?[callbacks2]:Array.isArray(callbacks2)&&callbacks2[0]instanceof BaseCallback?callbacks2:toList(callbacks2).map(callbackConfig=>new CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square2(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square2(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon(),Number.MAX_VALUE),firstLog=log(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon(),Number.MAX_VALUE),secondLog=log(add2(1,clippedTrue));return mean(square2(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square2(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log22=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log22);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon(),1-epsilon()),neg(sum2(mul(target.toFloat(),log(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon(),1-epsilon());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon(),1-epsilon()),y=log(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon(),1),clippedPred=clipByValue(yPred,epsilon(),1);return sum2(mul(yTrue,log(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log(add2(epsilon(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast2(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast2(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon()),Adam:()=>train.adam(.001,.9,.999,epsilon()),Adamax:()=>train.adamax(.002,.9,.999,epsilon(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys){if(typeof key!="string")return!1;if(!plainObjectCheck(x[key]))return!1}return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i<layers.length;++i)sequentialLike?printLayerSummary(layers[i],positions,printFn):printLayerSummaryWithConnections(layers[i],positions,relevantNodes,printFn),printFn((i===layers.length-1?"=":"_").repeat(lineLength));model2.checkTrainableWeightsConsistency();let trainableCount=countTrainableParams(model2),nonTrainableCount=countParamsInWeights(model2.nonTrainableWeights);printFn(`Total params: ${trainableCount+nonTrainableCount}`),printFn(`Trainable params: ${trainableCount}`),printFn(`Non-trainable params: ${nonTrainableCount}`),printFn("_".repeat(lineLength))}function countTrainableParams(model2){let trainableCount;return model2.collectedTrainableWeights!=null?trainableCount=countParamsInWeights(model2.collectedTrainableWeights):trainableCount=countParamsInWeights(model2.trainableWeights),trainableCount}function isModelSequentialLike(model2){let sequentialLike=!0,nodesByDepth=[],nodes=[];for(let depth in model2.nodesByDepth)nodesByDepth.push(model2.nodesByDepth[depth]);for(let depthNodes of nodesByDepth){if(depthNodes.length>1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node2 of layer.inboundNodes)if(nodes.indexOf(node2)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i<fields.length;++i)i>0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node2 of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node2)===-1))for(let i=0;i<node2.inboundLayers.length;++i){let inboundLayer=node2.inboundLayers[i].name,inboundLayerIndex=node2.nodeIndices[i],inboundTensorIndex=node2.tensorIndices[i];connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`)}let name=layer.name,className=layer.getClassName(),firstConnection=connections.length===0?"":connections[0],fields=[`${name} (${className})`,outputShape,layer.countParams().toString(),firstConnection];printRow(fields,positions,printFn);for(let i=1;i<connections.length;++i)printRow(["","","",connections[i]],positions,printFn)}function isArrayItemInputOrOutputName(key,index,value){return(key==="inboundNodes"||key==="outputLayers"||key==="inputLayers")&&index===0&&typeof value=="string"}function convertPythonicToTs(pythonicConfig,key){if(pythonicConfig===null)return null;if(typeof pythonicConfig=="string")return toCamelCase(pythonicConfig);if(typeof pythonicConfig=="number"||typeof pythonicConfig=="boolean")return pythonicConfig;if(pythonicConfig instanceof Array){let tsArray=[],arrayLength=pythonicConfig.length;for(let i=0;i<arrayLength;++i){let item=pythonicConfig[i];isArrayItemInputOrOutputName(key,i,item)?tsArray.push(item):tsArray.push(convertPythonicToTs(item,key))}return tsArray}else{let tsDict={};for(let pythonicKey of Object.keys(pythonicConfig)){let pythonicValue=pythonicConfig[pythonicKey];if(pythonicKey==="name"&&typeof pythonicValue=="string")tsDict[pythonicKey]=pythonicValue;else{let tsKey=toCamelCase(pythonicKey);tsDict[tsKey]=convertPythonicToTs(pythonicValue,tsKey)}}return tsDict}}function convertTsToPythonic(tsConfig,key){if(tsConfig==null)return null;if(typeof tsConfig=="string")return toSnakeCase(tsConfig);if(typeof tsConfig=="number"||typeof tsConfig=="boolean")return tsConfig;if(tsConfig instanceof Array){let pyArray=[],arrayLength=tsConfig.length;for(let i=0;i<arrayLength;++i){let item=tsConfig[i];isArrayItemInputOrOutputName(key,i,item)?pyArray.push(item):pyArray.push(convertTsToPythonic(item,key))}return pyArray}else{let pyDict={};for(let tsKey of Object.keys(tsConfig)){let tsValue=tsConfig[tsKey],pyKey=toSnakeCase(tsKey);(tsKey==="name"||tsKey==="className")&&typeof tsValue=="string"?pyDict[pyKey]=tsValue:pyDict[pyKey]=convertTsToPythonic(tsValue,tsKey)}return pyDict}}var version2="2.8.2";function assertFeedCompatibility(key,val){if(key.dtype==null||key.dtype===val.dtype)return val;try{return cast(val,key.dtype)}catch(err){throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`)}}var FeedDict=class{constructor(feeds){if(this.id2Value={},this.id2Mask={},this.name2Id={},feeds instanceof FeedDict)for(let id in feeds.id2Value)this.id2Value[id]=feeds.id2Value[id],id in feeds.id2Mask&&(this.id2Mask[id]=feeds.id2Mask[id]);else{if(feeds==null)return;for(let feed of feeds)this.add(feed.key,feed.value)}}add(key,value,mask){if(this.id2Value[key.id]==null)this.id2Value[key.id]=assertFeedCompatibility(key,value),this.name2Id[key.name]=key.id,mask!=null&&(this.id2Mask[key.id]=mask);else throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);return this}addFeed(feed){this.add(feed.key,feed.value)}hasKey(key){return this.id2Value[key.id]!=null}names(){return Object.keys(this.name2Id)}getValue(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Value[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Value[id]}}getMask(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Mask[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Mask[id]}}disposeMasks(){this.id2Mask!=null&&dispose(this.id2Mask)}},cachedSorted={},cachedRecipientCounts={};function execute(fetches,feedDict,kwargs,probe){let training=kwargs==null?!1:kwargs.training,arrayFetches=Array.isArray(fetches),fetchArray=arrayFetches?fetches:[fetches],outputNames=fetchArray.map(t=>t.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;i<sorted.length;++i){if(probe!=null){let numTensors=memory().numTensors;numTensors>probe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors<probe.minNumTensors&&(probe.minNumTensors=numTensors)}let symbolic=sorted[i],srcLayer=symbolic.sourceLayer;if(!(srcLayer instanceof InputLayer)){let inputValues=[],inputMasks=[],tensorsToDispose=[],maskExists=!1;for(let input2 of symbolic.inputs){let value=internalFeedDict.getValue(input2),mask=internalFeedDict.getMask(input2);inputValues.push(value),inputMasks.push(mask),mask!=null&&(maskExists=!0),training||(recipientCounts[input2.name]--,recipientCounts[input2.name]===0&&!feedDict.hasKey(input2)&&outputNames.indexOf(input2.name)===-1&&!value.isDisposed&&input2.sourceLayer.stateful!==!0&&tensorsToDispose.push(value))}maskExists&&(kwargs=kwargs||{},kwargs.mask=inputMasks[0]);let outputTensors=toList(srcLayer.apply(inputValues,kwargs)),outputMask=null;srcLayer.supportsMasking&&(outputMask=srcLayer.computeMask(inputValues,inputMasks));let layerOutputs=getNodeOutputs(symbolic),outputSymbolicTensors=Array.isArray(layerOutputs)?layerOutputs:[layerOutputs];for(let i2=0;i2<outputSymbolicTensors.length;++i2){internalFeedDict.hasKey(outputSymbolicTensors[i2])||internalFeedDict.add(outputSymbolicTensors[i2],outputTensors[i2],Array.isArray(outputMask)?outputMask[0]:outputMask);let index=outputNames.indexOf(outputSymbolicTensors[i2].name);index!==-1&&(finalOutputs[index]=outputTensors[i2])}training||dispose(tensorsToDispose)}}return internalFeedDict.disposeMasks(),arrayFetches?finalOutputs:finalOutputs[0]}function getTopologicalSortAndRecipientCounts(fetches,feedDict){util_exports.assert(fetches!=null&&fetches.length>0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)(recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name))&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;i<fetch3.sourceLayer.inboundNodes.length;++i)for(let outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors)if(outputTensor.id===fetch3.id){nodeIndex=i;break}layerOutputs=fetch3.sourceLayer.getOutputAt(nodeIndex)}return layerOutputs}var Container=class extends Layer{constructor(args){super({});if(this.containerNodes=new Set,this.name=args.name,this.name==null){let prefix=this.getClassName().toLowerCase();this.name=getUid(prefix)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(args.inputs)?this.inputs=args.inputs.slice():this.inputs=[args.inputs],Array.isArray(args.outputs)?this.outputs=args.outputs.slice():this.outputs=[args.outputs],unique2(this.inputs).length!==this.inputs.length)throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(x=>x.name)}`);unique2(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i];if(!(layer instanceof InputLayer))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);this.inputNames.push(layer.name),this.feedInputShapes.push(layer.batchInputShape),this.feedInputNames.push(layer.name)}for(let layer of this.outputLayers)this.outputNames.push(layer.name);this.internalInputShapes=this.inputs.map(x=>x.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node2=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node2)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node2)===-1){this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node2)===-1&&nodesInProgress2.push(node2);let numInboundLayers=node2.inboundLayers.length;for(let i=0;i<numInboundLayers;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],tensorIndex2=node2.tensorIndices[i];buildMapOfGraph(x,finishedNodes2,nodesInProgress2,layer2,nodeIndex2,tensorIndex2)}for(finishedNodes2.push(node2);nodesInProgress2.indexOf(node2)>=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node2),1);nodesInDecreasingDepth.push(node2)}},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node2 of reversedNodesInDecreasingDepth){nodeIDToNode[node2.id]=node2,node2.id in nodesDepths||(nodesDepths[node2.id]=0);let depth=nodesDepths[node2.id],previousDepth=layersDepths[node2.outboundLayer.id]==null?0:layersDepths[node2.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node2.outboundLayer.id]=depth,layerIDToLayer[node2.outboundLayer.id]=node2.outboundLayer,nodesDepths[node2.id]=depth;for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],inboundNode=inboundLayer.inboundNodes[nodeIndex],previousDepth2=nodesDepths[inboundNode.id]==null?0:nodesDepths[inboundNode.id];nodesDepths[inboundNode.id]=Math.max(depth+1,previousDepth2),nodeIDToNode[inboundNode.id]=inboundNode}}let nodesByDepth={};for(let nodeID in nodesDepths){let depth=nodesDepths[nodeID];depth in nodesByDepth||(nodesByDepth[depth]=[]),nodesByDepth[depth].push(nodeIDToNode[nodeID])}let layersByDepth={};for(let layerID in layersDepths){let depth=layersDepths[layerID];depth in layersByDepth||(layersByDepth[depth]=[]),layersByDepth[depth].push(layerIDToLayer[layerID])}let depthKeys=Object.keys(layersByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndex<bIndex?-1:aIndex>bIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node2 of nodesByDepth[depth]){let layer=node2.outboundLayer;if(layer!=null){for(let x of node2.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node2.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version2}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i]);return execute(this.outputs,feedDict,kwargs)})}computeMask(inputs,mask){return tidy(()=>{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;i<inputShapes.length;i++){let layer=this.inputLayers[i],inputShape2=inputShapes[i],shapeKey=layer.name+"_0_0";layersToOutputShapes[shapeKey]=inputShape2}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)===-1){let inputShapes2=[];for(let j=0;j<node2.inboundLayers.length;j++){let inboundLayer=node2.inboundLayers[j],nodeIndex2=node2.nodeIndices[j],tensorIndex=node2.tensorIndices[j],shapeKey=`${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`,inputShape2=layersToOutputShapes[shapeKey];inputShapes2.push(inputShape2)}let outputShape=layer.computeOutputShape(singletonOrArray(inputShapes2)),outputShapes2=normalizeShapeList(outputShape),nodeIndex=layer.inboundNodes.indexOf(node2);for(let j=0;j<outputShapes2.length;j++){let shapeKey=`${layer.name}_${nodeIndex}_${j}`;layersToOutputShapes[shapeKey]=outputShapes2[j]}}}}let outputShapes=[],outputShapeKeys=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],tensorIndex=this.outputLayersTensorIndices[i],shapeKey=`${layer.name}_${nodeIndex}_${tensorIndex}`;outputShapeKeys.push(shapeKey)}for(let i=0;i<outputShapeKeys.length;i++){let key=outputShapeKeys[i];assert2(key in layersToOutputShapes),outputShapes.push(layersToOutputShapes[key])}return singletonOrArray(outputShapes)}runInternalGraph(inputs,masks){masks==null&&(masks=pyListRepeat(null,inputs.length));let tensorMap={};for(let i=0;i<this.inputs.length;++i){let x=this.inputs[i],y=inputs[i],mask=masks[i];tensorMap[x.id]=[y,mask]}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer,referenceInputTensors=node2.inputTensors,referenceOutputTensors=node2.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node2.callArgs!=null&&(kwargs=node2.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i<referenceOutputTensors.length;++i){let x=referenceOutputTensors[i],y=outputTensors2[i],mask=outputMasks2[i];tensorMap[x.id]=[y,mask]}}}}let outputTensors=[],outputMasks=[],outputShapes=[];for(let x of this.outputs){assert2(x.id in tensorMap,`Could not compute output ${x.name} : ${x.id}`);let[tensor2,mask]=tensorMap[x.id];outputShapes.push(tensor2.shape),outputTensors.push(tensor2),outputMasks.push(mask)}return[outputTensors,outputMasks,outputShapes]}buildNodeConversionMap(layers){let nodeConversionMap={},keptNodes;for(let layer of this.layers){keptNodes=layer instanceof Container?1:0;for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let nodeKey=Container.nodeKey(layer,originalNodeIndex);this.containerNodes.has(nodeKey)&&(nodeConversionMap[nodeKey]=keptNodes,keptNodes+=1)}}return nodeConversionMap}getLayer(name,index){if(index!=null){if(this.layers.length<=index)throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);return this.layers[index]}else if(name==null)throw new ValueError("Provide either a layer name or layer index");for(let layer of this.layers)if(layer.name===name)return layer;throw new ValueError(`No such layer: ${name}`)}calculateLosses(){return tidy(()=>{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex<layer.inboundNodes.length;++nodeIndex){let nodeKey=Container.nodeKey(layer,nodeIndex);this.containerNodes.has(nodeKey)&&losses4.push(...layer.calculateLosses())}return losses4})}getConfig(){let config={name:this.name},nodeConversionMap=this.buildNodeConversionMap(this.layers),layerConfigs=[];for(let layer of this.layers){let layerClassName=layer.getClassName(),layerConfig=layer.getConfig(),filteredInboundNodes=[];for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let node2=layer.inboundNodes[originalNodeIndex],nodeKey=Container.nodeKey(layer,originalNodeIndex),kwargs={};if(this.containerNodes.has(nodeKey)){if(node2.callArgs)try{JSON.stringify(node2.callArgs),kwargs=node2.callArgs}catch(err){console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node2.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),kwargs={}}if(node2.inboundLayers.length>0){let nodeData=[];for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],tensorIndex=node2.tensorIndices[i],nodeKey2=Container.nodeKey(inboundLayer,nodeIndex),newNodeIndex=nodeConversionMap[nodeKey2];newNodeIndex==null&&(newNodeIndex=0),nodeData.push([inboundLayer.name,newNodeIndex,tensorIndex,kwargs])}filteredInboundNodes.push(nodeData)}}}let dict={};dict.name=layer.name,dict.className=layerClassName,dict.config=layerConfig,dict.inboundNodes=filteredInboundNodes,layerConfigs.push(dict)}config.layers=layerConfigs;let modelInputs=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i],nodeIndex=this.inputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(this.containerNodes.has(nodeKey)){let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.inputLayersTensorIndices[i];modelInputs.push([layer.name,newNodeIndex,tensorIndex])}}config.inputLayers=modelInputs;let modelOutputs=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(this.containerNodes.has(nodeKey)){let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.outputLayersTensorIndices[i];modelOutputs.push([layer.name,newNodeIndex,tensorIndex])}}return config.outputLayers=modelOutputs,config}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let createdLayers={},unprocessedNodes={};function addUnprocessedNode(layer,nodeData){layer.name in unprocessedNodes?unprocessedNodes[layer.name].push(nodeData):unprocessedNodes[layer.name]=[nodeData]}function processNode(layer,nodeData){let inputTensors2=[],kwargs;for(let inputData of nodeData){let inboundLayerName=inputData[0],inboundNodeIndex=inputData[1],inboundTensorIndex=inputData[2];if(kwargs=inputData[3]==null?{}:inputData[3],!(inboundLayerName in createdLayers)){addUnprocessedNode(layer,nodeData);return}let inboundLayer=createdLayers[inboundLayerName];if(inboundLayer.inboundNodes.length<=inboundNodeIndex){addUnprocessedNode(layer,nodeData);return}let inboundNode=inboundLayer.inboundNodes[inboundNodeIndex];inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex])}inputTensors2.length>0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config.customObjects!=null?config.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config.name,layersFromConfig=config.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex<flattenedXs.length;xIndex++)util_exports.assert(flattenedXs[xIndex].shape[0]===batchSize,()=>`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex<flattenedYs.length;yIndex++)util_exports.assert(flattenedYs[yIndex].shape[0]===batchSize,()=>`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data){if(data.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data[0],ys:data[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch<args.epochs;){let epochLogs={};await callbackList.onEpochBegin(epoch);let stepsDone=0,batchIndex=0;for(hasBatchesPerEpoch||(dataIterator=await dataset.iterator());hasBatchesPerEpoch?stepsDone<args.batchesPerEpoch:!0;){let iteratorOut=await dataIterator.next();if(hasBatchesPerEpoch&&iteratorOut.done){console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch*args.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(iteratorOut.value!=null){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),batchLogs={};batchLogs.batch=batchIndex,batchLogs.size=xs[0].shape[0],await callbackList.onBatchBegin(batchIndex,batchLogs);let sampleWeights=[];if(args.classWeight!=null){let standardClassWeights=standardizeClassWeights(args.classWeight,model2.outputNames);for(let i=0;i<standardClassWeights.length;++i)sampleWeights.push(await standardizeWeights(ys[i],null,standardClassWeights[i]))}let ins=xs.concat(ys).concat(sampleWeights),outs=trainFunction(ins);dispose(ins);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),batchIndex++,stepsDone++}if(hasBatchesPerEpoch?stepsDone>=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i<model2.metricsNames.length;++i)epochLogs[`val_${model2.metricsNames[i]}`]=valOuts[i]}break}if(model2.stopTraining_)break}if(await callbackList.onEpochEnd(epoch,epochLogs),epoch++,model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}finally{model2.isTraining=!1}}function getStepsPerEpoch(dataset,args){let stepsPerEpoch=null;return args.batchesPerEpoch!=null?stepsPerEpoch=args.batchesPerEpoch:Number.isFinite(dataset.size)&&(stepsPerEpoch=dataset.size),stepsPerEpoch}function isDatasetObject(dataset){return typeof dataset.iterator=="function"}function isLazyIteratorObject(iterator){return typeof iterator.next=="function"}async function evaluateDataset(model2,dataset,args){args=args||{};let hasBatches=args.batches!=null,f=model2.testFunction,outs=[];if(args.verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch<args.batches:!0;){let iteratorOut=await dataIterator.next();if(outs=tidy(()=>{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));let batchSize=xsAndYs[0].shape[0];for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i],oldScalar=outs[i];outs[i]=tidy(()=>add2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i<outs.length;++i){let oldScalar=outs[i];outs[i]=div(outs[i],numExamples),dispose(oldScalar)}return singletonOrArray(outs)}function checkBatchSize(batchSize){util_exports.assert(batchSize>0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart<size;)batchEnd=batchStart+batchSize,batchEnd>=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range2(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch<epochs;++epoch){await callbackList.onEpochBegin(epoch);let epochLogs={};if(stepsPerEpoch!=null)throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");{if(shuffle2==="batch")throw new NotImplementedError("batch shuffling is not implemneted yet");shuffle2&&util_exports.shuffle(indexArray);let epochIndexArray1D=tensor1d(indexArray),batches=makeBatches(numTrainSamples,batchSize);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchLogs={};if(await callbackList.onBatchBegin(batchIndex,batchLogs),tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}if(batchIndex===batches.length-1&&doValidation){let valOuts=model2.testLoop(valF,valIns,batchSize);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=valOuts[i];keep(out),epochLogs["val_"+label]=out}}}),await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),model2.stopTraining_)break}epochIndexArray1D.dispose()}if(await callbackList.onEpochEnd(epoch,epochLogs),model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}async function fitTensors(model2,x,y,args={}){if(model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;let inputs,targets,inputValX,inputValY,valX,valY,sampleWeights;try{let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!1,standardizedOuts=await model2.standardizeUserData(x,y,args.sampleWeight,args.classWeight,checkBatchAxis,batchSize);inputs=standardizedOuts[0],targets=standardizedOuts[1],sampleWeights=standardizedOuts[2];let doValidation=!1,valIns;if(args.validationData!=null&&args.validationData.length>0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;i<tensors.length;++i){let tensor2=tensors[i];if(tensor2.rank===1)outs.push(expandDims2(tensor2,1));else{if(tensor2.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");outs.push(tensor2)}}return outs}function disposeNewTensors(tensors,refTensors){if(tensors!=null){let oldTensorIds=[];if(refTensors instanceof Tensor)oldTensorIds.push(refTensors.id);else if(Array.isArray(refTensors))refTensors.forEach(t=>oldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data!=null){let gotUnexpectedData=!1;if(isDataArray(data)&&data.length>0)gotUnexpectedData=!0;else if(isDataDict(data)){for(let key in data)if(data.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`)}return[]}if(data==null)return names.map(name=>null);let arrays;if(isDataDict(data)){data=data,arrays=[];for(let name of names){if(data[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data[name])}}else if(isDataArray(data)){if(data=data,data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);arrays=data}else{if(data=data,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);arrays=[data]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null){for(let i=0;i<names.length;++i)if(shapes[i]!=null){let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);for(let j=0;j<shapes[i].length;++j)if(!(j===0&&!checkBatchAxis)){let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim>=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique2(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique2(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i<targets.length;++i){let y=targets[i],loss=lossFns[i],shape=outputShapes[i];if(loss!=null){if(loss===categoricalCrossentropy&&y.shape[y.shape.length-1]===1)throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(keyLosses.indexOf(loss)!==-1){let slicedYShape=y.shape.slice(1),slicedShape=shape.slice(1);for(let j=0;j<slicedYShape.length;++j){let targetDim=slicedYShape[j],outDim=slicedShape[j];if(outDim!=null&&targetDim!==outDim)throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function checkInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){let arrays;if(Array.isArray(data)){if(data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);arrays=data}else{if(names.length>1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);arrays=[data]}if(shapes!=null){for(let i=0;i<names.length;++i)if(shapes[i]!=null){let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);for(let j=0;j<shapes[i].length;++j)if(!(j===0&&!checkBatchAxis)){let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim!==dim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`)}}}}function collectMetrics(metrics2,outputNames){if(metrics2==null||Array.isArray(metrics2)&&metrics2.length===0)return outputNames.map(name=>[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let shape=this.internalOutputShapes[i],name=this.outputNames[i];this.feedOutputNames.push(name),this.feedOutputShapes.push(shape),this.feedLossFns.push(this.lossFunctions[i])}let skipTargetIndices=[];this.metrics=args.metrics,this.metricsNames=["loss"],this.metricsTensors=[],nameScope("loss",()=>{for(let i=0;i<this.outputs.length;++i)if(skipTargetIndices.indexOf(i)===-1){let weightedLoss=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i<this.outputs.length;++i)if(skipTargetIndices.indexOf(i)===-1){let outputMetrics=nestedMetrics[i];(metrics2=>{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i])}else for(let input2 of this.inputs){let tensorValue=inputs[input2.name];if(tensorValue==null)throw new ValueError(`No value is provided for the model's input ${input2.name}`);feedDict.add(input2,tensorValue)}let executeOutputs=execute(outputSymbolicTensors,feedDict);return outputsIsArray?executeOutputs:executeOutputs[0]}retrieveSymbolicTensors(symbolicTensorNames){let outputSymbolicTensors=pyListRepeat(null,symbolicTensorNames.length),outputsRemaining=symbolicTensorNames.length;for(let layer of this.layers){let layerOutputs=Array.isArray(layer.output)?layer.output:[layer.output],layerOutputNames=layerOutputs.map(output=>output.name);for(let i=0;i<symbolicTensorNames.length;++i){let index=layerOutputNames.indexOf(symbolicTensorNames[i]);if(index!==-1&&(outputSymbolicTensors[i]=layerOutputs[index],outputsRemaining--),outputsRemaining===0)break}if(outputsRemaining===0)break}if(outputsRemaining>0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex<batches.length;++batchIndex)tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;i<insBatch.length;++i)feeds.push({key:this.inputs[i],value:insBatch[i]});else feeds.push({key:this.inputs[0],value:insBatch});let feedDict=new FeedDict(feeds);return execute(this.outputs,feedDict)}).forEach((batchOut,i)=>outsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i<this.feedOutputShapes.length;++i){let outputShape=this.feedOutputShapes[i];this.feedLossFns[i]===sparseCategoricalCrossentropy?outputShapes.push(outputShape.slice(0,outputShape.length-1).concat([1])):outputShapes.push(outputShape)}if(x=standardizeInputData(x,this.feedInputNames,this.feedInputShapes,!1,"input"),y=standardizeInputData(y,this.feedOutputNames,outputShapes,!1,"target"),checkArrayLengths(x,y,null),checkLossAndTargetCompatibility(y,this.feedLossFns,this.feedOutputShapes),this.stateful&&batchSize!=null&&batchSize>0&&x[0].shape[0]%batchSize!==0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i<classWeights.length;++i)standardSampleWeights.push(await standardizeWeights(standardYs[i],null,classWeights[i]))}return[standardXs,standardYs,standardSampleWeights]}testLoop(f,ins,batchSize,verbose=0,steps){return tidy(()=>{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range2(0,numSamples));for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(indexArray,batchStart,batchEnd-batchStart),insBatch=sliceArraysByIndices(ins,batchIds),batchOuts=f(insBatch);if(batchIndex===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i];outs[i]=add2(outs[i],mul(batchEnd-batchStart,batchOut))}}for(let i=0;i<outs.length;++i)outs[i]=div(outs[i],numSamples)}return outs})}getDedupedMetricsNames(){let outLabels=this.metricsNames,dedupedOutLabels=[];for(let i=0;i<outLabels.length;++i){let label=outLabels[i],newLabel=label;count(outLabels,label)>1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data=>{let lossValues=[],inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict,{training:!0}),totalLoss;for(let i=0;i<this.lossFunctions.length;++i){let loss=this.lossFunctions[i](targets[i],outputs[i]);sampleWeights[i]!=null&&(loss=computeWeightedLoss2(loss,sampleWeights[i]));let meanLoss=mean(loss);lossValues.push(meanLoss),i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss)}for(let i=0;i<this.metricsTensors.length;++i){let weightedMetric;if(this.outputs.length>1&&i<this.outputs.length)weightedMetric=lossValues[i];else{let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1];weightedMetric=mean(metric(targets[outputIndex],outputs[outputIndex]))}keep(weightedMetric),metricsValues.push(weightedMetric)}return totalLoss=mean(totalLoss),this.calculateLosses().forEach(regularizerLoss=>{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict);for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=mean(lossFunction(targets[i],outputs[i]));i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss),valOutputs.push(totalLoss)}for(let i=0;i<this.metricsTensors.length;++i){let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1],meanMetric=mean(metric(targets[outputIndex],outputs[outputIndex]));valOutputs.push(meanMetric)}return valOutputs})}async fit(x,y,args={}){return fitTensors(this,x,y,args)}async fitDataset(dataset,args){return fitDataset(this,dataset,args)}async trainOnBatch(x,y){let standardizeOut=await this.standardizeUserData(x,y),inputs=standardizeOut[0],targets=standardizeOut[1],losses4=this.makeTrainFunction()(inputs.concat(targets)),lossValues=[];for(let loss of losses4){let v=await loss.data();lossValues.push(v[0])}return dispose(losses4),singletonOrArray(lossValues)}getNamedWeights(config){let namedWeights=[],trainableOnly=config!=null&&config.trainableOnly,weights=trainableOnly?this.trainableWeights:this.weights,weightValues=this.getWeights(trainableOnly);for(let i=0;i<weights.length;++i)trainableOnly&&!weights[i].trainable||namedWeights.push({name:weights[i].originalName,tensor:weightValues[i]});return namedWeights}set stopTraining(stop){this.stopTraining_=stop}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(optimizer){this.optimizer_!==optimizer&&(this.optimizer_=optimizer,this.isOptimizerOwned=!1)}dispose(){let result=super.dispose();if(result.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let numTensorsBeforeOptmizerDisposal=memory().numTensors;this.optimizer_.dispose(),result.numDisposedVariables+=numTensorsBeforeOptmizerDisposal-memory().numTensors}return result}getLossIdentifiers(){let lossNames;if(typeof this.loss=="string")lossNames=toSnakeCase(this.loss);else if(Array.isArray(this.loss)){for(let loss of this.loss)if(typeof loss!="string")throw new Error("Serialization of non-string loss is not supported.");lossNames=this.loss.map(name=>toSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version2}`,convertedBy:null};if((config==null?!1:config.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config instanceof Array){if(config[0].className==null||config[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config}else util_exports.assert(config.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config.layers,delete config.layers,extraModelConfig=config;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config){return new Sequential(config)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config){return Input(config)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu2(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config={};return config.className="linear",config.config={},deserializeActivation(config)}if(typeof identifier=="string"){let config={};return config.className=identifier,config.config={},deserializeActivation(config)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square2(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config){return new cls({l1:config.l1,l2:config.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i<inputShape.length;++i)axes[i]=inputShape[i];this.inputSpec=[new InputSpec({ndim:inputShape.length,axes})],this.built=!0}call(inputs,kwargs){return inputs=getExactlyOneTensor(inputs),prelu(inputs,this.alpha.read())}getConfig(){let config={alphaInitializer:serializeInitializer(this.alphaInitializer),alphaRegularizer:serializeRegularizer(this.alphaRegularizer),alphaConstraint:serializeConstraint(this.alphaConstraint),sharedAxes:this.sharedAxes},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};PReLU.className="PReLU";serialization_exports.registerClass(PReLU);var ELU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA=1,args==null&&(args={}),args.alpha!=null&&args.alpha!==this.DEFAULT_ALPHA)throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return elu(x)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ELU.className="ELU";serialization_exports.registerClass(ELU);var ThresholdedReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_THETA=1,args==null&&(args={}),this.theta=args.theta==null?this.DEFAULT_THETA:args.theta}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return x.mul(cast2(x.greater(this.theta),"float32"))}computeOutputShape(inputShape){return inputShape}getConfig(){let config={theta:this.theta},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ThresholdedReLU.className="ThresholdedReLU";serialization_exports.registerClass(ThresholdedReLU);var Softmax3=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_AXIS=1,args==null&&(args={}),this.softmax=new Softmax2().apply,this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return this.softmax(x,this.axis)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Softmax3.className="Softmax";serialization_exports.registerClass(Softmax3);function normalizeArray(value,n,name){if(typeof value=="number")return pyListRepeat(value,n);if(value.length!==n)throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);for(let i=0;i<n;++i){let singleValue=value[i];if(!isInteger(singleValue))throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`)}return value}function convOutputLength(inputLength,filterSize,padding,stride,dilation=1){if(inputLength==null)return inputLength;let dilatedFilterSize=filterSize+(filterSize-1)*(dilation-1),outputLength;return padding==="same"?outputLength=inputLength:outputLength=inputLength-dilatedFilterSize+1,Math.floor((outputLength+stride-1)/stride)}function deconvLength(dimSize,strideSize,kernelSize,padding){if(dimSize==null)return null;if(padding==="valid")dimSize=dimSize*strideSize+max2([kernelSize-strideSize,0]);else if(padding==="same")dimSize=dimSize*strideSize;else throw new ValueError(`Unsupport padding mode: ${padding}.`);return dimSize}function preprocessConv2DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i<space.length;++i){let newDim=convOutputLength(space[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);newSpace.push(newDim)}let outputShape=[inputShape[0]];return this.dataFormat==="channelsLast"?(outputShape=outputShape.concat(newSpace),outputShape.push(this.filters)):(outputShape.push(this.filters),outputShape=outputShape.concat(newSpace)),outputShape}getConfig(){let config={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static verifyArgs(args){if(!("filters"in args)||typeof args.filters!="number"||args.filters<1)throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config=super.getConfig();return delete config.dilationRate,config}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config){super(rank,config);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config.kernelInitializer!=null||config.kernelRegularizer!=null||config.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config.padding!=null&&config.padding!=="same"&&config.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);this.depthMultiplier=config.depthMultiplier==null?1:config.depthMultiplier,this.depthwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<this.rank+2)throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(inputShape)}`);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=this.kernelSize.concat([inputDim,this.depthMultiplier]),pointwiseKernelShape=[];for(let i=0;i<this.rank;++i)pointwiseKernelShape.push(1);pointwiseKernelShape.push(inputDim*this.depthMultiplier,this.filters);let trainable=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,trainable,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",pointwiseKernelShape,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,trainable,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,trainable,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config=super.getConfig();return delete config.rank,delete config.kernelInitializer,delete config.kernelRegularizer,delete config.kernelConstraint,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config=super.getConfig();return delete config.rank,delete config.dataFormat,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d3(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d3(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config=super.getConfig();return config.depthMultiplier=this.depthMultiplier,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range2(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;t<timeSteps;++t){let currentInput=perStepInputs[t],stepOutputs=tidy(()=>stepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range2(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;i<numStates;++i)output.push(null);return output}else return this.states_}set states(s){this.states_=s}build(inputShape){let constantShape=null;if(this.numConstants!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let batchSize=this.stateful?inputShape[0]:null,inputDim=inputShape.slice(2);this.inputSpec[0]=new InputSpec({shape:[batchSize,null,...inputDim]});let stepInputShape=[inputShape[0]].concat(inputShape.slice(2));if(constantShape!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");this.cell.build(stepInputShape);let stateSize;if(Array.isArray(this.cell.stateSize)?stateSize=this.cell.stateSize:stateSize=[this.cell.stateSize],this.stateSpec!=null){if(!util_exports.arraysEqual(this.stateSpec.map(spec=>spec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],dim=Array.isArray(this.cell.stateSize)?this.cell.stateSize[index]:this.cell.stateSize,expectedShape=[batchSize,dim];if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims2(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile2(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile2(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cellConfig=config.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return new cls(config)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let matrixX=dot2(inputs,this.kernel.read());this.useBias&&(matrixX=biasAdd(matrixX,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0]));let recurrentKernelValue=this.recurrentKernel.read(),[rk1,rk2]=split(recurrentKernelValue,[2*this.units,this.units],recurrentKernelValue.rank-1),matrixInner=dot2(hTMinus1,rk1),[xZ,xR,xH]=split(matrixX,3,matrixX.rank-1),[recurrentZ,recurrentR]=split(matrixInner,2,matrixInner.rank-1);z=this.recurrentActivation.apply(add2(xZ,recurrentZ)),r=this.recurrentActivation.apply(add2(xR,recurrentR));let recurrentH=dot2(mul(r,hTMinus1),rk2);hh=this.activation.apply(add2(xH,recurrentH));let h=add2(mul(z,hTMinus1),mul(add2(1,neg(z)),hh));return[h,h]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},baseConfig,config)}};GRUCell.className="GRUCell";serialization_exports.registerClass(GRUCell);var GRU=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new GRUCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let z=dot2(inputs,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0])),z=add2(z,dot2(hTMinus1,this.recurrentKernel.read())),this.useBias&&(z=biasAdd(z,this.bias.read()));let[z0,z1,z2,z3]=split(z,4,z.rank-1);i=this.recurrentActivation.apply(z0),f=this.recurrentActivation.apply(z1),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(z2))),o=this.recurrentActivation.apply(z3);let h=mul(o,this.activation.apply(c));return[h,h,c]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},baseConfig,config)}};LSTMCell.className="LSTMCell";serialization_exports.registerClass(LSTMCell);var LSTM=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new LSTMCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i<this.cells.length;++i){let cell=this.cells[i];states=nestedStates[i],i===0?callInputs=[inputs[0]].concat(states):callInputs=[callInputs[0]].concat(states),callInputs=cell.call(callInputs,kwargs),newNestedStates.push(callInputs.slice(1))}states=[];for(let cellStates of newNestedStates.slice().reverse())states.push(...cellStates);return[callInputs[0]].concat(states)})}build(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let outputDim;this.cells.forEach((cell,i)=>{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cells=[];for(let cellConfig of config.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;i<cell.weights.length;++i)tuples.push([cell.weights[i],inputWeights[i]])}batchSetValue(tuples)}};StackedRNNCells.className="StackedRNNCells";serialization_exports.registerClass(StackedRNNCells);function generateDropoutMask(args){let{ones:ones4,rate,training=!1,count:count2=1}=args,droppedInputs=()=>dropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i<p2.length;i++)e.indexOf(p2[i])<0&&Object.prototype.propertyIsEnumerable.call(s,p2[i])&&(t[p2[i]]=s[p2[i]]);return t},ConvRNN2D=class extends RNN{constructor(args){if(args.unroll)throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(args.cell))throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(args);this.inputSpec=[new InputSpec({ndim:5})]}call(inputs,kwargs){return tidy(()=>{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],expectedShape=stateShape;if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config){return new cls(config)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i<this.noiseShape.length;++i)noiseShape.push(this.noiseShape[i]==null?inputShape[i]:this.noiseShape[i]);return noiseShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0<this.rate&&this.rate<1){let training=kwargs.training==null?!1:kwargs.training,noiseShape=this.getNoiseShape(input2);return inTrainPhase(()=>dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i<input2.rank;++i)permutation.push(i);permutation.push(1),input2=input2.transpose(permutation)}return batchFlatten(input2)})}getConfig(){let config={};this.dataFormat!=null&&(config.dataFormat=this.dataFormat);let baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Flatten.className="Flatten";serialization_exports.registerClass(Flatten);var Activation2=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.activation=getActivation(args.activation)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config={n:this.n},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i<this.targetShape.length;++i)this.isUnknown(this.targetShape[i])&&(this.targetShape[i]=null)}isUnknown(dim){return dim<0||dim==null}fixUnknownDimension(inputShape,outputShape){let errorMsg="Total size of new array must be unchanged.",finalShape=outputShape.slice(),known=1,unknown=null;for(let i=0;i<finalShape.length;++i){let dim=finalShape[i];if(this.isUnknown(dim))if(unknown===null)unknown=i;else throw new ValueError("Can only specifiy one unknown dimension.");else known*=dim}let originalSize=arrayProd(inputShape);if(unknown!==null){if(known===0||originalSize%known!==0)throw new ValueError(errorMsg);finalShape[unknown]=originalSize/known}else if(originalSize!==known)throw new ValueError(errorMsg);return finalShape}computeOutputShape(inputShape){let anyUnknownDims=!1;for(let i=0;i<inputShape.length;++i)if(this.isUnknown(inputShape[i])){anyUnknownDims=!0;break}return anyUnknownDims?inputShape.slice(0,1).concat(this.targetShape):inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape))}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range2(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={maskValue:this.maskValue};return Object.assign(config,baseConfig),config}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k<inLens.length;++k){let s1=inLens[k],s2=inputShape[k+1];if(s1!=null&&s2!=null&&s1!==s2)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);s1==null&&(inLens[i]=s2),i++}}return[inputShape[0],...inLens,this.outputDim]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast2(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length<shape2.length)return this.computeElementwiseOpOutputShape(shape2,shape1);if(shape2.length===0)return shape1;let outputShape=shape1.slice(0,shape1.length-shape2.length);for(let k=0;k<shape2.length;++k){let i=shape1[shape1.length-shape2.length+k],j=shape2[k];if(i==null||j==null||i<0||j<0)outputShape.push(null);else if(i===1)outputShape.push(j);else if(j===1)outputShape.push(i);else{if(i!==j)throw new ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(shape1)+" "+JSON.stringify(shape2));outputShape.push(i)}}return outputShape}build(inputShape){if(Array.isArray(inputShape)&&!Array.isArray(inputShape[0])&&(inputShape=[getExactlyOneShape(inputShape)]),inputShape=inputShape,inputShape.length<2)throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);if(batchSizes=unique2(batchSizes),batchSizes.length>1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let allRanks=inputShape.map(shape=>shape.length);inputShape.indexOf(null)===-1&&unique2(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max2(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k<maxNDim-xNDim;++k)x=expandDims2(x,1);reshapedInputs.push(x)}return this.mergeFunction(reshapedInputs)}else{let transposed=!1;for(let x of inputs){let xNDim=x.rank;if(xNDim==null){let xShape=x.shape,batchSize=xShape[0],newShape=xShape.slice(1).concat([batchSize]),xTransposed=x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));xTransposed=transpose(xTransposed,[1,0]),xTransposed=xTransposed.reshape(newShape),reshapedInputs.push(xTransposed),transposed=!0}else if(xNDim>1){let dims=range2(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range2(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);return batchSizes=unique2(batchSizes),batchSizes.length===1?outputShape=batchSizes.concat(outputShape):outputShape=[null].concat(outputShape),outputShape}computeMask(inputs,mask){return tidy(()=>{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i<mask.length-1;++i)output=logicalAnd(output,mask[i]);return output})}},Add2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return output})}};Add2.className="Add";serialization_exports.registerClass(Add2);var Multiply2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=mul(output,inputs[i]);return output})}};Multiply2.className="Multiply";serialization_exports.registerClass(Multiply2);var Average=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return mul(1/inputs.length,output)})}};Average.className="Average";serialization_exports.registerClass(Average);var Maximum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=maximum(output,inputs[i]);return output})}};Maximum2.className="Maximum";serialization_exports.registerClass(Maximum2);var Minimum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=minimum(output,inputs[i]);return output})}};Minimum2.className="Minimum";serialization_exports.registerClass(Minimum2);var Concatenate=class extends Merge{constructor(args){super(args);this.DEFAULT_AXIS=-1,args==null&&(args={}),this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0]))||inputShape.length===1)throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");inputShape=inputShape;let allNoneShape=!0;for(let shape of inputShape)if(shape!=null){allNoneShape=!1;break}if(!allNoneShape){let shapeSet=[];for(let i=0;i<inputShape.length;++i){let shapeWithoutConcatAxis=inputShape[i].slice();shapeWithoutConcatAxis.splice(this.axis,1);let exists=!1;for(let shape of shapeSet)if(util_exports.arraysEqual(shape,shapeWithoutConcatAxis)){exists=!0;break}exists||shapeSet.push(shapeWithoutConcatAxis)}if(shapeSet.length>1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i<inputs.length;++i)mask[i]==null?outputMasks.push(onesLike(inputs[i]).asType("bool")):mask[i].rank<inputs[i].rank?outputMasks.push(expandDims(mask[i],-1)):outputMasks.push(mask[i]);let concatenatedMasks=concat(outputMasks,this.axis);return all(concatenatedMasks,-1,!1)})}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Concatenate.className="Concatenate";serialization_exports.registerClass(Concatenate);function interpretAxis(axis,dim){for(;axis<0;)axis+=dim;return axis}function batchDot(x,y,axes){if(x.shape.length>3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);y=y.reshape(y.shape.concat(diffShape))}else if(yNDim>xNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);x=x.reshape(x.shape.concat(diffShape))}else diff=0;let out;if(x.shape.length===2&&y.shape.length===2)axesArray[0]===axesArray[1]?out=x.mul(y).sum(axesArray[0]):out=x.transpose([1,0]).mul(y).sum(axesArray[1]);else{let adjX=axesArray[0]!==x.shape.length-1,adjY=axesArray[1]===y.shape.length-1;out=x.matMul(y,adjX,adjY)}if(diff>0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i<idx+diff;++i)squeezeAxes.push(i);out=out.squeeze(squeezeAxes)}return out.shape.length===1&&(out=out.expandDims(1)),out})}var Dot=class extends Merge{constructor(args){super(args);this.axes=args.axes,this.normalize=args.normalize==null?!1:args.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={stddev:this.stddev};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale22=1.0507009873554805,alphaP=-alpha*scale22,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast2(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon32=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon32);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon32);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon32);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon32),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range2(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon32),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range2(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range2(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range2(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=nDims);for(let axis of this.axis)if(axis<0||axis>=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique2(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale22=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i<nDims;++i)this.axis.indexOf(i)!==-1?(momentsTiling.push(inputShape[i]),scaleOffsetTiling.push(1)):(momentsTiling.push(1),scaleOffsetTiling.push(inputShape[i]));return mean4=mean4.tile(momentsTiling),variance=variance.tile(momentsTiling),scale22=scale22.tile(scaleOffsetTiling),offset=offset.tile(scaleOffsetTiling),batchNormalization(input2,mean4,variance,offset,scale22,this.epsilon)})}getConfig(){let config={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LayerNormalization.className="LayerNormalization";serialization_exports.registerClass(LayerNormalization);function spatial2dPadding(x,padding,dataFormat){return tidy(()=>{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims2(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config,customObjects={}){let layerConfig=config.layer,layer=deserialize(layerConfig,customObjects);delete config.layer;let newConfig={layer};return Object.assign(newConfig,config),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static fromConfig(cls,config){let rnnLayer=deserialize(config.layer);if(delete config.layer,config.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu3(args){return new ELU(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu2(args){return new PReLU(args)}function softmax2(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d3(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d4(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape2(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add3(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum2(args){return new Maximum2(args)}function minimum2(args){return new Minimum2(args)}function multiply(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config){return new L1L2(config)}function l12(config){return l1(config)}function l22(config){return l2(config)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less2(currVal,prevVal){return currVal<prevVal}function greater2(currVal,prevVal){return currVal>prevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less2:this.mode==="max"?this.monitorFunc=greater2:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater2:this.monitorFunc=less2,this.monitorFunc===less2&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less2?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node2,tensorMap,context,resourceManager){let inputParam=node2.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node2.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node2.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node2.inputNames.slice(start)[0],tensorMap,context,resourceManager),data=tensor2.dataSync();return inputParam.type==="number"?data[0]:util_exports.toNestedArray(tensor2.shape,data)}let attrParam=node2.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node2,tensorMap,context){let pad3=getParamValue("pad",node2,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node2,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});var 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json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op.startsWith("Placeholder")?placeholders.push(map[node2.name]):node2.op==="Const"?weights.push(map[node2.name]):(node2.input==null||node2.input.length===0)&&initNodes.push(map[node2.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node2=nodes[key];node2.children.length===0&&outputs.push(node2)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2!=null&&(node2.signatureKey=outputNodeNameToKey[name],outputs.push(node2))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2&&(node2.signatureKey=inputNodeNameToKey[name],inputs.push(node2))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node2){let mapper=getRegisteredOp(node2.op)||this.opMappers[node2.op]||{};node2.attr==null&&(node2.attr={});let newNode={name:node2.name,op:node2.op,category:mapper.category,inputNames:(node2.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node2.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node2.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node2.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op==="Const"&&weights.push(map[node2.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node2={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node2.signatureKey=arg.name,inputs.push(node2),nodes[nodeName]=node2}),Object.keys(nodes).forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node2=nodes[nodeName];node2!=null&&(node2.defaultOutput=index,outputs.push(node2))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){typeof value=="string"&&(value=DataType[value]);switch(value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&&param.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&&param.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&&param.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&&param.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&&param.list&&param.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node2,tensorMap,context){this.node=node2,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node2.inputNames.map(name=>this.getInput(name)),node2.rawAttrs!=null&&(this.attrs=Object.keys(node2.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node2,tensorMap,context)=>{switch(node2.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node2,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp2=(node2,tensorMap,context)=>{switch(node2.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node2,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node2,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node2,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node2,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node2,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node2,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node2,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node2,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node2,tensorMap,context),getParamValue("imag",node2,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node2,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node2,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node2,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node2,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node2,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node2,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node2,tensorMap,context))];case"Log":return[log(getParamValue("x",node2,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node2,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node2,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node2,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node2,tensorMap,context))];case"Real":return[real(getParamValue("x",node2,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node2,tensorMap,context))];case"Round":return[round2(getParamValue("x",node2,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node2,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node2,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node2,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node2,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node2,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node2,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node2,tensorMap,context))];case"Square":return[square(getParamValue("x",node2,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node2,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node2,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node2,tensorMap,context),getParamValue("clipValueMin",node2,tensorMap,context),getParamValue("clipValueMax",node2,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node2,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node2.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node2,tensorMap,context),getParamValue("axes",node2,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==-1&&n2[i]!==-1&&n1[i]!==n2[i])return!1;return!0}var TensorArray=class{constructor(name,dtype,maxSize,elementShape,identicalElementShapes,dynamicSize,clearAfterRead){this.name=name,this.dtype=dtype,this.maxSize=maxSize,this.elementShape=elementShape,this.identicalElementShapes=identicalElementShapes,this.dynamicSize=dynamicSize,this.clearAfterRead=clearAfterRead,this.tensors=[],this.closed_=!1,this.idTensor=scalar(0),keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config.config);let returnObj=fromConfig(cls,config.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return a<b?-1:a>b?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique2(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now2=util_exports.now();return now2-lastTime<waitMs||(lastTime=now2,lastResult=f(...args)),lastResult}}function mapActivationToFusedKernel(activationName){return activationName==="relu"?"relu":activationName==="linear"?"linear":activationName==="elu"?"elu":null}function calcL2Norms(w,axis){return tidy(()=>sqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config){return new MinMaxNorm(config)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros2});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i<end;++i)prod5*=array2[i];return prod5}function toArray1D(array2){return array2=Array.isArray(array2)?new Float32Array(array2):array2,tensor1d(array2)}function min2(array2){return min(toArray1D(array2)).dataSync()[0]}function max2(array2){return max(toArray1D(array2)).dataSync()[0]}function range2(begin,end){if(end<begin)throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);let out=[];for(let i=begin;i<end;++i)out.push(i);return out}function cast2(x,dtype){return x.asType(dtype)}function expandDims2(x,axis=-1){let outShape=x.shape.slice();return axis<0&&(axis=outShape.length+axis+1),outShape.splice(axis,0,1),x.reshape(outShape)}function repeat(x,n){return tidy(()=>{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims2(x,1);return tile2(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile2(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square2(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu2(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale22=this.scale;if(this.mode==="fanIn"?scale22/=Math.max(1,fanIn):this.mode==="fanOut"?scale22/=Math.max(1,fanOut):scale22/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale22);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale22);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config={};return config.className=className,config.config={},deserializeInitializer(config)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros2(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity(args){return new Identity2(args)}function varianceScaling(config){return new VarianceScaling(config)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add3,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d3,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d4,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu3,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum2,minimum:()=>minimum2,multiply:()=>multiply,permute:()=>permute,prelu:()=>prelu2,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape2,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax2,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndex<inputs.length;inputIndex++){let x=inputs[inputIndex],spec=inputSpec[inputIndex];if(spec==null)continue;let ndim=x.rank;if(spec.ndim!=null&&ndim!==spec.ndim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);if(spec.maxNDim!=null&&ndim>spec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim<spec.minNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);if(spec.dtype!=null&&x.dtype!==spec.dtype)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);if(spec.axes){let xShape=x.shape;for(let key in spec.axes){let axis=Number(key),value=spec.axes[key],xShapeAtAxis=axis>=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i<spec.shape.length;++i){let specDim=spec.shape[i],dim=x.shape[i];if(specDim!=null&&dim!=null&&specDim!==dim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`)}}}call(inputs,kwargs){return inputs}invokeCallHook(inputs,kwargs){this._callHook!=null&&this._callHook(inputs,kwargs)}setCallHook(callHook){this._callHook=callHook}clearCallHook(){this._callHook=null}apply(inputs,kwargs){kwargs=kwargs||{},this.assertNotDisposed();let inputsList=toList(inputs),allAreSymbolic=!0;for(let input2 of inputsList)if(!(input2 instanceof SymbolicTensor)){allAreSymbolic=!1;break}let noneAreSymbolic=!0;for(let input2 of inputsList)if(input2 instanceof SymbolicTensor){noneAreSymbolic=!1;break}if(allAreSymbolic===noneAreSymbolic)throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return nameScope(this.name,()=>{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node2 of this.inboundNodes){let shapeString=JSON.stringify(node2.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;i<paramValues.length;++i){let pv=paramValues[i],p2=params[i],w=weights[i];if(!util_exports.arraysEqual(pv.shape,w.shape))throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);weightValueTuples.push([p2,w])}batchSetValue(weightValueTuples)})}addWeight(name,shape,dtype,initializer,regularizer,trainable,constraint){if(this._addedWeightNames.indexOf(name)!==-1)throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);this._addedWeightNames.push(name),dtype==null&&(dtype="float32"),this.fastWeightInitDuringBuild&&(initializer=getInitializer("zeros"));let initValue=initializer.apply(shape,dtype),weight=new LayerVariable(initValue,dtype,name,trainable,constraint);return initValue.dispose(),regularizer!=null&&this.addLoss(()=>regularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;i<outputTensors.length;i++)outputTensors[i].sourceLayer=this,outputTensors[i].nodeIndex=this.inboundNodes.length-1,outputTensors[i].tensorIndex=i}getConfig(){let config={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(config.batchInputShape=this.batchInputShape),this.dtype!=null&&(config.dtype=this.dtype),config}disposeWeights(){return this.weights.forEach(weight=>weight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount==0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node2=layer.inboundNodes[nodeIndex];if(node2.inboundLayers.length===0)return node2.inputTensors;{let sourceTensors=[];for(let i=0;i<node2.inboundLayers.length;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],previousSources=getSourceInputs(x,layer2,nodeIndex2);for(let x2 of previousSources)sourceTensors.indexOf(x2)===-1&&sourceTensors.push(x2)}return sourceTensors}}}var InputLayer=class extends Layer{constructor(args){super({dtype:args.dtype,name:args.name!=null?args.name:getUid("input").toString()});if(args.batchSize==null&&(args.batchSize=null),args.sparse==null&&(args.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=args.sparse,args.inputShape!=null&&args.batchInputShape!=null)throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let batchInputShape=args.batchInputShape;if(batchInputShape==null){if(args.inputShape==null)throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");batchInputShape=[args.batchSize].concat(args.inputShape)}else if(args.batchSize!=null)throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let dtype=args.dtype||"float32";this.batchInputShape=batchInputShape,this.dtype=dtype,this.inputSpec=[{shape:batchInputShape}];let inputTensor=new SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);inputTensor.nodeIndex=0,inputTensor.tensorIndex=0,new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[inputTensor],outputTensors:[inputTensor],inputMasks:[null],outputMasks:[null],inputShapes:[batchInputShape],outputShapes:[batchInputShape]})}apply(inputs,kwargs){throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};InputLayer.className="InputLayer";serialization_exports.registerClass(InputLayer);function Input(config){if(config.batchShape==null&&config.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(config.batchShape!=null&&config.shape!=null)throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let batchShape=config.batchShape;config.shape!=null&&batchShape==null&&(batchShape=[null].concat(config.shape));let dtype=config.dtype;return dtype==null&&(dtype="float32"),new InputLayer({batchInputShape:batchShape,name:config.name,dtype,sparse:config.sparse}).inboundNodes[0].outputTensors[0]}async function resolveScalarsInLogs(logs){if(logs==null)return;let promises=[],keys=[],scalarsToDispose=[];for(let key in logs){let value=logs[key];if(typeof value!="number"){let valueScalar=value;promises.push(valueScalar.data()),keys.push(key),scalarsToDispose.push(valueScalar)}}if(promises.length>0){let values=await Promise.all(promises);for(let i=0;i<values.length;++i)logs[keys[i]]=values[i][0];dispose(scalarsToDispose)}}function disposeTensorsInLogs(logs){if(logs!=null)for(let key in logs){let value=logs[key];typeof value!="number"&&value.dispose()}}var ModelLoggingVerbosity;(function(ModelLoggingVerbosity2){ModelLoggingVerbosity2[ModelLoggingVerbosity2.SILENT=0]="SILENT",ModelLoggingVerbosity2[ModelLoggingVerbosity2.VERBOSE=1]="VERBOSE"})(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));var DEFAULT_YIELD_EVERY_MS=125,BaseCallback=class{constructor(){this.validationData=null}setParams(params){this.params=params}async onEpochBegin(epoch,logs){}async onEpochEnd(epoch,logs){}async onBatchBegin(batch,logs){}async onBatchEnd(batch,logs){}async onTrainBegin(logs){}async onTrainEnd(logs){}setModel(model2){}},CallbackList=class{constructor(callbacks2,queueLength=10){callbacks2==null&&(callbacks2=[]),this.callbacks=callbacks2,this.queueLength=queueLength}append(callback){this.callbacks.push(callback)}setParams(params){for(let callback of this.callbacks)callback.setParams(params)}setModel(model2){for(let callback of this.callbacks)callback.setModel(model2)}async onEpochBegin(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochBegin(epoch,logs)}async onEpochEnd(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochEnd(epoch,logs)}async onBatchBegin(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchBegin(batch,logs)}async onBatchEnd(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchEnd(batch,logs)}async onTrainBegin(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainBegin(logs)}async onTrainEnd(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainEnd(logs)}},BaseLogger=class extends BaseCallback{constructor(){super()}async onEpochBegin(epoch){this.seen=0,this.totals={}}async onBatchEnd(batch,logs){logs==null&&(logs={});let batchSize=logs.size==null?0:logs.size;this.seen+=batchSize;for(let key in logs){let value=logs[key];if(typeof value=="number")this.totals.hasOwnProperty(key)||(this.totals[key]=0),this.totals[key]=this.totals[key]+value*batchSize;else{let oldTotalsToDispose;key in this.totals?oldTotalsToDispose=this.totals[key]:this.totals[key]=0;let total=tidy(()=>add2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log5=mul(div(1,this.seen),this.totals[key]);logs[key]=log5,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;i<valueArray.length;++i)if(typeof valueArray[i]!="number"){let valueScalar=valueArray[i];promises.push(valueScalar.data()),keys.push(key),indices.push(i)}}let values=await Promise.all(promises);for(let n=0;n<values.length;++n)this.history[keys[n]][indices[n]].dispose(),this.history[keys[n]][indices[n]]=values[n][0]}},CustomCallback=class extends BaseCallback{constructor(args,yieldEvery){super();if(this.currentEpoch=0,this.yieldEvery=yieldEvery||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=DEFAULT_YIELD_EVERY_MS),this.yieldEvery==="never"&&args.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");util_exports.isNumber(this.yieldEvery)&&(this.maybeWait=debounce(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=args.onTrainBegin,this.trainEnd=args.onTrainEnd,this.epochBegin=args.onEpochBegin,this.epochEnd=args.onEpochEnd,this.batchBegin=args.onBatchBegin,this.batchEnd=args.onBatchEnd,this.yield=args.onYield}async maybeWait(epoch,batch,logs){let ps=[];this.yield!=null&&(await resolveScalarsInLogs(logs),ps.push(this.yield(epoch,batch,logs))),ps.push(nextFrame()),await Promise.all(ps)}async onEpochBegin(epoch,logs){this.currentEpoch=epoch,this.epochBegin!=null&&(await resolveScalarsInLogs(logs),await this.epochBegin(epoch,logs))}async onEpochEnd(epoch,logs){let ps=[];this.epochEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.epochEnd(epoch,logs))),this.yieldEvery==="epoch"&&ps.push(nextFrame()),await Promise.all(ps)}async onBatchBegin(batch,logs){this.batchBegin!=null&&(await resolveScalarsInLogs(logs),await this.batchBegin(batch,logs))}async onBatchEnd(batch,logs){let ps=[];this.batchEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.batchEnd(batch,logs))),this.yieldEvery==="batch"?ps.push(nextFrame()):util_exports.isNumber(this.yieldEvery)&&ps.push(this.maybeWait(this.currentEpoch,batch,logs)),await Promise.all(ps)}async onTrainBegin(logs){this.trainBegin!=null&&(await resolveScalarsInLogs(logs),await this.trainBegin(logs))}async onTrainEnd(logs){this.trainEnd!=null&&(await resolveScalarsInLogs(logs),await this.trainEnd(logs))}};function standardizeCallbacks(callbacks2,yieldEvery){return callbacks2==null&&(callbacks2={}),callbacks2 instanceof BaseCallback?[callbacks2]:Array.isArray(callbacks2)&&callbacks2[0]instanceof BaseCallback?callbacks2:toList(callbacks2).map(callbackConfig=>new CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square2(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square2(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon(),Number.MAX_VALUE),firstLog=log(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon(),Number.MAX_VALUE),secondLog=log(add2(1,clippedTrue));return mean(square2(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square2(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log22=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log22);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon(),1-epsilon()),neg(sum2(mul(target.toFloat(),log(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon(),1-epsilon());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon(),1-epsilon()),y=log(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon(),1),clippedPred=clipByValue(yPred,epsilon(),1);return sum2(mul(yTrue,log(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log(add2(epsilon(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast2(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast2(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon()),Adam:()=>train.adam(.001,.9,.999,epsilon()),Adamax:()=>train.adamax(.002,.9,.999,epsilon(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys)if(typeof key!="string"||!plainObjectCheck(x[key]))return!1;return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i<layers.length;++i)sequentialLike?printLayerSummary(layers[i],positions,printFn):printLayerSummaryWithConnections(layers[i],positions,relevantNodes,printFn),printFn((i===layers.length-1?"=":"_").repeat(lineLength));model2.checkTrainableWeightsConsistency();let trainableCount=countTrainableParams(model2),nonTrainableCount=countParamsInWeights(model2.nonTrainableWeights);printFn(`Total params: ${trainableCount+nonTrainableCount}`),printFn(`Trainable params: ${trainableCount}`),printFn(`Non-trainable params: ${nonTrainableCount}`),printFn("_".repeat(lineLength))}function countTrainableParams(model2){let trainableCount;return model2.collectedTrainableWeights!=null?trainableCount=countParamsInWeights(model2.collectedTrainableWeights):trainableCount=countParamsInWeights(model2.trainableWeights),trainableCount}function isModelSequentialLike(model2){let sequentialLike=!0,nodesByDepth=[],nodes=[];for(let depth in model2.nodesByDepth)nodesByDepth.push(model2.nodesByDepth[depth]);for(let depthNodes of nodesByDepth){if(depthNodes.length>1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node2 of layer.inboundNodes)if(nodes.indexOf(node2)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i<fields.length;++i)i>0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node2 of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node2)===-1))for(let i=0;i<node2.inboundLayers.length;++i){let inboundLayer=node2.inboundLayers[i].name,inboundLayerIndex=node2.nodeIndices[i],inboundTensorIndex=node2.tensorIndices[i];connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`)}let name=layer.name,className=layer.getClassName(),firstConnection=connections.length===0?"":connections[0],fields=[`${name} (${className})`,outputShape,layer.countParams().toString(),firstConnection];printRow(fields,positions,printFn);for(let i=1;i<connections.length;++i)printRow(["","","",connections[i]],positions,printFn)}function isArrayItemInputOrOutputName(key,index,value){return(key==="inboundNodes"||key==="outputLayers"||key==="inputLayers")&&index===0&&typeof value=="string"}function convertPythonicToTs(pythonicConfig,key){if(pythonicConfig===null)return null;if(typeof pythonicConfig=="string")return toCamelCase(pythonicConfig);if(typeof pythonicConfig=="number"||typeof pythonicConfig=="boolean")return pythonicConfig;if(pythonicConfig instanceof Array){let tsArray=[],arrayLength=pythonicConfig.length;for(let i=0;i<arrayLength;++i){let item=pythonicConfig[i];isArrayItemInputOrOutputName(key,i,item)?tsArray.push(item):tsArray.push(convertPythonicToTs(item,key))}return tsArray}else{let tsDict={};for(let pythonicKey of Object.keys(pythonicConfig)){let pythonicValue=pythonicConfig[pythonicKey];if(pythonicKey==="name"&&typeof pythonicValue=="string")tsDict[pythonicKey]=pythonicValue;else{let tsKey=toCamelCase(pythonicKey);tsDict[tsKey]=convertPythonicToTs(pythonicValue,tsKey)}}return tsDict}}function convertTsToPythonic(tsConfig,key){if(tsConfig==null)return null;if(typeof tsConfig=="string")return toSnakeCase(tsConfig);if(typeof tsConfig=="number"||typeof tsConfig=="boolean")return tsConfig;if(tsConfig instanceof Array){let pyArray=[],arrayLength=tsConfig.length;for(let i=0;i<arrayLength;++i){let item=tsConfig[i];isArrayItemInputOrOutputName(key,i,item)?pyArray.push(item):pyArray.push(convertTsToPythonic(item,key))}return pyArray}else{let pyDict={};for(let tsKey of Object.keys(tsConfig)){let tsValue=tsConfig[tsKey],pyKey=toSnakeCase(tsKey);(tsKey==="name"||tsKey==="className")&&typeof tsValue=="string"?pyDict[pyKey]=tsValue:pyDict[pyKey]=convertTsToPythonic(tsValue,tsKey)}return pyDict}}var version2="2.8.3";function assertFeedCompatibility(key,val){if(key.dtype==null||key.dtype===val.dtype)return val;try{return cast(val,key.dtype)}catch(err){throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`)}}var FeedDict=class{constructor(feeds){if(this.id2Value={},this.id2Mask={},this.name2Id={},feeds instanceof FeedDict)for(let id in feeds.id2Value)this.id2Value[id]=feeds.id2Value[id],id in feeds.id2Mask&&(this.id2Mask[id]=feeds.id2Mask[id]);else{if(feeds==null)return;for(let feed of feeds)this.add(feed.key,feed.value)}}add(key,value,mask){if(this.id2Value[key.id]==null)this.id2Value[key.id]=assertFeedCompatibility(key,value),this.name2Id[key.name]=key.id,mask!=null&&(this.id2Mask[key.id]=mask);else throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);return this}addFeed(feed){this.add(feed.key,feed.value)}hasKey(key){return this.id2Value[key.id]!=null}names(){return Object.keys(this.name2Id)}getValue(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Value[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Value[id]}}getMask(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Mask[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Mask[id]}}disposeMasks(){this.id2Mask!=null&&dispose(this.id2Mask)}},cachedSorted={},cachedRecipientCounts={};function execute(fetches,feedDict,kwargs,probe){let training=kwargs==null?!1:kwargs.training,arrayFetches=Array.isArray(fetches),fetchArray=arrayFetches?fetches:[fetches],outputNames=fetchArray.map(t=>t.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;i<sorted.length;++i){if(probe!=null){let numTensors=memory().numTensors;numTensors>probe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors<probe.minNumTensors&&(probe.minNumTensors=numTensors)}let symbolic=sorted[i],srcLayer=symbolic.sourceLayer;if(srcLayer instanceof InputLayer)continue;let inputValues=[],inputMasks=[],tensorsToDispose=[],maskExists=!1;for(let input2 of symbolic.inputs){let value=internalFeedDict.getValue(input2),mask=internalFeedDict.getMask(input2);inputValues.push(value),inputMasks.push(mask),mask!=null&&(maskExists=!0),training||(recipientCounts[input2.name]--,recipientCounts[input2.name]===0&&!feedDict.hasKey(input2)&&outputNames.indexOf(input2.name)===-1&&!value.isDisposed&&input2.sourceLayer.stateful!==!0&&tensorsToDispose.push(value))}maskExists&&(kwargs=kwargs||{},kwargs.mask=inputMasks[0]);let outputTensors=toList(srcLayer.apply(inputValues,kwargs)),outputMask=null;srcLayer.supportsMasking&&(outputMask=srcLayer.computeMask(inputValues,inputMasks));let layerOutputs=getNodeOutputs(symbolic),outputSymbolicTensors=Array.isArray(layerOutputs)?layerOutputs:[layerOutputs];for(let i2=0;i2<outputSymbolicTensors.length;++i2){internalFeedDict.hasKey(outputSymbolicTensors[i2])||internalFeedDict.add(outputSymbolicTensors[i2],outputTensors[i2],Array.isArray(outputMask)?outputMask[0]:outputMask);let index=outputNames.indexOf(outputSymbolicTensors[i2].name);index!==-1&&(finalOutputs[index]=outputTensors[i2])}training||dispose(tensorsToDispose)}return internalFeedDict.disposeMasks(),arrayFetches?finalOutputs:finalOutputs[0]}function getTopologicalSortAndRecipientCounts(fetches,feedDict){util_exports.assert(fetches!=null&&fetches.length>0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name)&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;i<fetch3.sourceLayer.inboundNodes.length;++i)for(let outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors)if(outputTensor.id===fetch3.id){nodeIndex=i;break}layerOutputs=fetch3.sourceLayer.getOutputAt(nodeIndex)}return layerOutputs}var Container=class extends Layer{constructor(args){super({});if(this.containerNodes=new Set,this.name=args.name,this.name==null){let prefix=this.getClassName().toLowerCase();this.name=getUid(prefix)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(args.inputs)?this.inputs=args.inputs.slice():this.inputs=[args.inputs],Array.isArray(args.outputs)?this.outputs=args.outputs.slice():this.outputs=[args.outputs],unique2(this.inputs).length!==this.inputs.length)throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(x=>x.name)}`);unique2(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i];if(!(layer instanceof InputLayer))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);this.inputNames.push(layer.name),this.feedInputShapes.push(layer.batchInputShape),this.feedInputNames.push(layer.name)}for(let layer of this.outputLayers)this.outputNames.push(layer.name);this.internalInputShapes=this.inputs.map(x=>x.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node2=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node2)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node2)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node2)===-1&&nodesInProgress2.push(node2);let numInboundLayers=node2.inboundLayers.length;for(let i=0;i<numInboundLayers;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],tensorIndex2=node2.tensorIndices[i];buildMapOfGraph(x,finishedNodes2,nodesInProgress2,layer2,nodeIndex2,tensorIndex2)}for(finishedNodes2.push(node2);nodesInProgress2.indexOf(node2)>=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node2),1);nodesInDecreasingDepth.push(node2)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node2 of reversedNodesInDecreasingDepth){nodeIDToNode[node2.id]=node2,node2.id in nodesDepths||(nodesDepths[node2.id]=0);let depth=nodesDepths[node2.id],previousDepth=layersDepths[node2.outboundLayer.id]==null?0:layersDepths[node2.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node2.outboundLayer.id]=depth,layerIDToLayer[node2.outboundLayer.id]=node2.outboundLayer,nodesDepths[node2.id]=depth;for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],inboundNode=inboundLayer.inboundNodes[nodeIndex],previousDepth2=nodesDepths[inboundNode.id]==null?0:nodesDepths[inboundNode.id];nodesDepths[inboundNode.id]=Math.max(depth+1,previousDepth2),nodeIDToNode[inboundNode.id]=inboundNode}}let nodesByDepth={};for(let nodeID in nodesDepths){let depth=nodesDepths[nodeID];depth in nodesByDepth||(nodesByDepth[depth]=[]),nodesByDepth[depth].push(nodeIDToNode[nodeID])}let layersByDepth={};for(let layerID in layersDepths){let depth=layersDepths[layerID];depth in layersByDepth||(layersByDepth[depth]=[]),layersByDepth[depth].push(layerIDToLayer[layerID])}let depthKeys=Object.keys(layersByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndex<bIndex?-1:aIndex>bIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node2 of nodesByDepth[depth]){let layer=node2.outboundLayer;if(layer!=null){for(let x of node2.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node2.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version2}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i]);return execute(this.outputs,feedDict,kwargs)})}computeMask(inputs,mask){return tidy(()=>{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;i<inputShapes.length;i++){let layer=this.inputLayers[i],inputShape2=inputShapes[i],shapeKey=layer.name+"_0_0";layersToOutputShapes[shapeKey]=inputShape2}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;j<node2.inboundLayers.length;j++){let inboundLayer=node2.inboundLayers[j],nodeIndex2=node2.nodeIndices[j],tensorIndex=node2.tensorIndices[j],shapeKey=`${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`,inputShape2=layersToOutputShapes[shapeKey];inputShapes2.push(inputShape2)}let outputShape=layer.computeOutputShape(singletonOrArray(inputShapes2)),outputShapes2=normalizeShapeList(outputShape),nodeIndex=layer.inboundNodes.indexOf(node2);for(let j=0;j<outputShapes2.length;j++){let shapeKey=`${layer.name}_${nodeIndex}_${j}`;layersToOutputShapes[shapeKey]=outputShapes2[j]}}}let outputShapes=[],outputShapeKeys=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],tensorIndex=this.outputLayersTensorIndices[i],shapeKey=`${layer.name}_${nodeIndex}_${tensorIndex}`;outputShapeKeys.push(shapeKey)}for(let i=0;i<outputShapeKeys.length;i++){let key=outputShapeKeys[i];assert2(key in layersToOutputShapes),outputShapes.push(layersToOutputShapes[key])}return singletonOrArray(outputShapes)}runInternalGraph(inputs,masks){masks==null&&(masks=pyListRepeat(null,inputs.length));let tensorMap={};for(let i=0;i<this.inputs.length;++i){let x=this.inputs[i],y=inputs[i],mask=masks[i];tensorMap[x.id]=[y,mask]}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer,referenceInputTensors=node2.inputTensors,referenceOutputTensors=node2.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node2.callArgs!=null&&(kwargs=node2.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i<referenceOutputTensors.length;++i){let x=referenceOutputTensors[i],y=outputTensors2[i],mask=outputMasks2[i];tensorMap[x.id]=[y,mask]}}}}let outputTensors=[],outputMasks=[],outputShapes=[];for(let x of this.outputs){assert2(x.id in tensorMap,`Could not compute output ${x.name} : ${x.id}`);let[tensor2,mask]=tensorMap[x.id];outputShapes.push(tensor2.shape),outputTensors.push(tensor2),outputMasks.push(mask)}return[outputTensors,outputMasks,outputShapes]}buildNodeConversionMap(layers){let nodeConversionMap={},keptNodes;for(let layer of this.layers){keptNodes=layer instanceof Container?1:0;for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let nodeKey=Container.nodeKey(layer,originalNodeIndex);this.containerNodes.has(nodeKey)&&(nodeConversionMap[nodeKey]=keptNodes,keptNodes+=1)}}return nodeConversionMap}getLayer(name,index){if(index!=null){if(this.layers.length<=index)throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);return this.layers[index]}else if(name==null)throw new ValueError("Provide either a layer name or layer index");for(let layer of this.layers)if(layer.name===name)return layer;throw new ValueError(`No such layer: ${name}`)}calculateLosses(){return tidy(()=>{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex<layer.inboundNodes.length;++nodeIndex){let nodeKey=Container.nodeKey(layer,nodeIndex);this.containerNodes.has(nodeKey)&&losses4.push(...layer.calculateLosses())}return losses4})}getConfig(){let config={name:this.name},nodeConversionMap=this.buildNodeConversionMap(this.layers),layerConfigs=[];for(let layer of this.layers){let layerClassName=layer.getClassName(),layerConfig=layer.getConfig(),filteredInboundNodes=[];for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let node2=layer.inboundNodes[originalNodeIndex],nodeKey=Container.nodeKey(layer,originalNodeIndex),kwargs={};if(this.containerNodes.has(nodeKey)){if(node2.callArgs)try{JSON.stringify(node2.callArgs),kwargs=node2.callArgs}catch(err){console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node2.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),kwargs={}}if(node2.inboundLayers.length>0){let nodeData=[];for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],tensorIndex=node2.tensorIndices[i],nodeKey2=Container.nodeKey(inboundLayer,nodeIndex),newNodeIndex=nodeConversionMap[nodeKey2];newNodeIndex==null&&(newNodeIndex=0),nodeData.push([inboundLayer.name,newNodeIndex,tensorIndex,kwargs])}filteredInboundNodes.push(nodeData)}}}let dict={};dict.name=layer.name,dict.className=layerClassName,dict.config=layerConfig,dict.inboundNodes=filteredInboundNodes,layerConfigs.push(dict)}config.layers=layerConfigs;let modelInputs=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i],nodeIndex=this.inputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.inputLayersTensorIndices[i];modelInputs.push([layer.name,newNodeIndex,tensorIndex])}config.inputLayers=modelInputs;let modelOutputs=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.outputLayersTensorIndices[i];modelOutputs.push([layer.name,newNodeIndex,tensorIndex])}return config.outputLayers=modelOutputs,config}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let createdLayers={},unprocessedNodes={};function addUnprocessedNode(layer,nodeData){layer.name in unprocessedNodes?unprocessedNodes[layer.name].push(nodeData):unprocessedNodes[layer.name]=[nodeData]}function processNode(layer,nodeData){let inputTensors2=[],kwargs;for(let inputData of nodeData){let inboundLayerName=inputData[0],inboundNodeIndex=inputData[1],inboundTensorIndex=inputData[2];if(kwargs=inputData[3]==null?{}:inputData[3],!(inboundLayerName in createdLayers)){addUnprocessedNode(layer,nodeData);return}let inboundLayer=createdLayers[inboundLayerName];if(inboundLayer.inboundNodes.length<=inboundNodeIndex){addUnprocessedNode(layer,nodeData);return}let inboundNode=inboundLayer.inboundNodes[inboundNodeIndex];inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex])}inputTensors2.length>0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config.customObjects!=null?config.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config.name,layersFromConfig=config.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex<flattenedXs.length;xIndex++)util_exports.assert(flattenedXs[xIndex].shape[0]===batchSize,()=>`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex<flattenedYs.length;yIndex++)util_exports.assert(flattenedYs[yIndex].shape[0]===batchSize,()=>`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data){if(data.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data[0],ys:data[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch<args.epochs;){let epochLogs={};await callbackList.onEpochBegin(epoch);let stepsDone=0,batchIndex=0;for(hasBatchesPerEpoch||(dataIterator=await dataset.iterator());hasBatchesPerEpoch?stepsDone<args.batchesPerEpoch:!0;){let iteratorOut=await dataIterator.next();if(hasBatchesPerEpoch&&iteratorOut.done){console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch*args.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(iteratorOut.value!=null){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),batchLogs={};batchLogs.batch=batchIndex,batchLogs.size=xs[0].shape[0],await callbackList.onBatchBegin(batchIndex,batchLogs);let sampleWeights=[];if(args.classWeight!=null){let standardClassWeights=standardizeClassWeights(args.classWeight,model2.outputNames);for(let i=0;i<standardClassWeights.length;++i)sampleWeights.push(await standardizeWeights(ys[i],null,standardClassWeights[i]))}let ins=xs.concat(ys).concat(sampleWeights),outs=trainFunction(ins);dispose(ins);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),batchIndex++,stepsDone++}if(hasBatchesPerEpoch?stepsDone>=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i<model2.metricsNames.length;++i)epochLogs[`val_${model2.metricsNames[i]}`]=valOuts[i]}break}if(model2.stopTraining_)break}if(await callbackList.onEpochEnd(epoch,epochLogs),epoch++,model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}finally{model2.isTraining=!1}}function getStepsPerEpoch(dataset,args){let stepsPerEpoch=null;return args.batchesPerEpoch!=null?stepsPerEpoch=args.batchesPerEpoch:Number.isFinite(dataset.size)&&(stepsPerEpoch=dataset.size),stepsPerEpoch}function isDatasetObject(dataset){return typeof dataset.iterator=="function"}function isLazyIteratorObject(iterator){return typeof iterator.next=="function"}async function evaluateDataset(model2,dataset,args){args=args||{};let hasBatches=args.batches!=null,f=model2.testFunction,outs=[];if(args.verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch<args.batches:!0;){let iteratorOut=await dataIterator.next();if(outs=tidy(()=>{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));let batchSize=xsAndYs[0].shape[0];for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i],oldScalar=outs[i];outs[i]=tidy(()=>add2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i<outs.length;++i){let oldScalar=outs[i];outs[i]=div(outs[i],numExamples),dispose(oldScalar)}return singletonOrArray(outs)}function checkBatchSize(batchSize){util_exports.assert(batchSize>0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart<size;)batchEnd=batchStart+batchSize,batchEnd>=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range2(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch<epochs;++epoch){await callbackList.onEpochBegin(epoch);let epochLogs={};if(stepsPerEpoch!=null)throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");{if(shuffle2==="batch")throw new NotImplementedError("batch shuffling is not implemneted yet");shuffle2&&util_exports.shuffle(indexArray);let epochIndexArray1D=tensor1d(indexArray),batches=makeBatches(numTrainSamples,batchSize);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchLogs={};if(await callbackList.onBatchBegin(batchIndex,batchLogs),tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}if(batchIndex===batches.length-1&&doValidation){let valOuts=model2.testLoop(valF,valIns,batchSize);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=valOuts[i];keep(out),epochLogs["val_"+label]=out}}}),await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),model2.stopTraining_)break}epochIndexArray1D.dispose()}if(await callbackList.onEpochEnd(epoch,epochLogs),model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}async function fitTensors(model2,x,y,args={}){if(model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;let inputs,targets,inputValX,inputValY,valX,valY,sampleWeights;try{let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!1,standardizedOuts=await model2.standardizeUserData(x,y,args.sampleWeight,args.classWeight,checkBatchAxis,batchSize);inputs=standardizedOuts[0],targets=standardizedOuts[1],sampleWeights=standardizedOuts[2];let doValidation=!1,valIns;if(args.validationData!=null&&args.validationData.length>0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;i<tensors.length;++i){let tensor2=tensors[i];if(tensor2.rank===1)outs.push(expandDims2(tensor2,1));else{if(tensor2.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");outs.push(tensor2)}}return outs}function disposeNewTensors(tensors,refTensors){if(tensors==null)return;let oldTensorIds=[];if(refTensors instanceof Tensor)oldTensorIds.push(refTensors.id);else if(Array.isArray(refTensors))refTensors.forEach(t=>oldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data!=null){let gotUnexpectedData=!1;if(isDataArray(data)&&data.length>0)gotUnexpectedData=!0;else if(isDataDict(data)){for(let key in data)if(data.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`)}return[]}if(data==null)return names.map(name=>null);let arrays;if(isDataDict(data)){data=data,arrays=[];for(let name of names){if(data[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data[name])}}else if(isDataArray(data)){if(data=data,data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);arrays=data}else{if(data=data,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);arrays=[data]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim>=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique2(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique2(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i<targets.length;++i){let y=targets[i],loss=lossFns[i],shape=outputShapes[i];if(loss!=null){if(loss===categoricalCrossentropy&&y.shape[y.shape.length-1]===1)throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(keyLosses.indexOf(loss)!==-1){let slicedYShape=y.shape.slice(1),slicedShape=shape.slice(1);for(let j=0;j<slicedYShape.length;++j){let targetDim=slicedYShape[j],outDim=slicedShape[j];if(outDim!=null&&targetDim!==outDim)throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function checkInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){let arrays;if(Array.isArray(data)){if(data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);arrays=data}else{if(names.length>1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);arrays=[data]}if(shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim!==dim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`)}}}function collectMetrics(metrics2,outputNames){if(metrics2==null||Array.isArray(metrics2)&&metrics2.length===0)return outputNames.map(name=>[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let shape=this.internalOutputShapes[i],name=this.outputNames[i];this.feedOutputNames.push(name),this.feedOutputShapes.push(shape),this.feedLossFns.push(this.lossFunctions[i])}let skipTargetIndices=[];this.metrics=args.metrics,this.metricsNames=["loss"],this.metricsTensors=[],nameScope("loss",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let weightedLoss=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let outputMetrics=nestedMetrics[i];(metrics2=>{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i])}else for(let input2 of this.inputs){let tensorValue=inputs[input2.name];if(tensorValue==null)throw new ValueError(`No value is provided for the model's input ${input2.name}`);feedDict.add(input2,tensorValue)}let executeOutputs=execute(outputSymbolicTensors,feedDict);return outputsIsArray?executeOutputs:executeOutputs[0]}retrieveSymbolicTensors(symbolicTensorNames){let outputSymbolicTensors=pyListRepeat(null,symbolicTensorNames.length),outputsRemaining=symbolicTensorNames.length;for(let layer of this.layers){let layerOutputs=Array.isArray(layer.output)?layer.output:[layer.output],layerOutputNames=layerOutputs.map(output=>output.name);for(let i=0;i<symbolicTensorNames.length;++i){let index=layerOutputNames.indexOf(symbolicTensorNames[i]);if(index!==-1&&(outputSymbolicTensors[i]=layerOutputs[index],outputsRemaining--),outputsRemaining===0)break}if(outputsRemaining===0)break}if(outputsRemaining>0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex<batches.length;++batchIndex)tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;i<insBatch.length;++i)feeds.push({key:this.inputs[i],value:insBatch[i]});else feeds.push({key:this.inputs[0],value:insBatch});let feedDict=new FeedDict(feeds);return execute(this.outputs,feedDict)}).forEach((batchOut,i)=>outsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i<this.feedOutputShapes.length;++i){let outputShape=this.feedOutputShapes[i];this.feedLossFns[i]===sparseCategoricalCrossentropy?outputShapes.push(outputShape.slice(0,outputShape.length-1).concat([1])):outputShapes.push(outputShape)}if(x=standardizeInputData(x,this.feedInputNames,this.feedInputShapes,!1,"input"),y=standardizeInputData(y,this.feedOutputNames,outputShapes,!1,"target"),checkArrayLengths(x,y,null),checkLossAndTargetCompatibility(y,this.feedLossFns,this.feedOutputShapes),this.stateful&&batchSize!=null&&batchSize>0&&x[0].shape[0]%batchSize!=0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i<classWeights.length;++i)standardSampleWeights.push(await standardizeWeights(standardYs[i],null,classWeights[i]))}return[standardXs,standardYs,standardSampleWeights]}testLoop(f,ins,batchSize,verbose=0,steps){return tidy(()=>{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range2(0,numSamples));for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(indexArray,batchStart,batchEnd-batchStart),insBatch=sliceArraysByIndices(ins,batchIds),batchOuts=f(insBatch);if(batchIndex===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i];outs[i]=add2(outs[i],mul(batchEnd-batchStart,batchOut))}}for(let i=0;i<outs.length;++i)outs[i]=div(outs[i],numSamples)}return outs})}getDedupedMetricsNames(){let outLabels=this.metricsNames,dedupedOutLabels=[];for(let i=0;i<outLabels.length;++i){let label=outLabels[i],newLabel=label;count(outLabels,label)>1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data=>{let lossValues=[],inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict,{training:!0}),totalLoss;for(let i=0;i<this.lossFunctions.length;++i){let loss=this.lossFunctions[i](targets[i],outputs[i]);sampleWeights[i]!=null&&(loss=computeWeightedLoss2(loss,sampleWeights[i]));let meanLoss=mean(loss);lossValues.push(meanLoss),i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss)}for(let i=0;i<this.metricsTensors.length;++i){let weightedMetric;if(this.outputs.length>1&&i<this.outputs.length)weightedMetric=lossValues[i];else{let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1];weightedMetric=mean(metric(targets[outputIndex],outputs[outputIndex]))}keep(weightedMetric),metricsValues.push(weightedMetric)}return totalLoss=mean(totalLoss),this.calculateLosses().forEach(regularizerLoss=>{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict);for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=mean(lossFunction(targets[i],outputs[i]));i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss),valOutputs.push(totalLoss)}for(let i=0;i<this.metricsTensors.length;++i){let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1],meanMetric=mean(metric(targets[outputIndex],outputs[outputIndex]));valOutputs.push(meanMetric)}return valOutputs})}async fit(x,y,args={}){return fitTensors(this,x,y,args)}async fitDataset(dataset,args){return fitDataset(this,dataset,args)}async trainOnBatch(x,y){let standardizeOut=await this.standardizeUserData(x,y),inputs=standardizeOut[0],targets=standardizeOut[1],losses4=this.makeTrainFunction()(inputs.concat(targets)),lossValues=[];for(let loss of losses4){let v=await loss.data();lossValues.push(v[0])}return dispose(losses4),singletonOrArray(lossValues)}getNamedWeights(config){let namedWeights=[],trainableOnly=config!=null&&config.trainableOnly,weights=trainableOnly?this.trainableWeights:this.weights,weightValues=this.getWeights(trainableOnly);for(let i=0;i<weights.length;++i)trainableOnly&&!weights[i].trainable||namedWeights.push({name:weights[i].originalName,tensor:weightValues[i]});return namedWeights}set stopTraining(stop){this.stopTraining_=stop}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(optimizer){this.optimizer_!==optimizer&&(this.optimizer_=optimizer,this.isOptimizerOwned=!1)}dispose(){let result=super.dispose();if(result.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let numTensorsBeforeOptmizerDisposal=memory().numTensors;this.optimizer_.dispose(),result.numDisposedVariables+=numTensorsBeforeOptmizerDisposal-memory().numTensors}return result}getLossIdentifiers(){let lossNames;if(typeof this.loss=="string")lossNames=toSnakeCase(this.loss);else if(Array.isArray(this.loss)){for(let loss of this.loss)if(typeof loss!="string")throw new Error("Serialization of non-string loss is not supported.");lossNames=this.loss.map(name=>toSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version2}`,convertedBy:null};if((config==null?!1:config.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config instanceof Array){if(config[0].className==null||config[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config}else util_exports.assert(config.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config.layers,delete config.layers,extraModelConfig=config;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config){return new Sequential(config)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config){return Input(config)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu2(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config={};return config.className="linear",config.config={},deserializeActivation(config)}if(typeof identifier=="string"){let config={};return config.className=identifier,config.config={},deserializeActivation(config)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square2(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config){return new cls({l1:config.l1,l2:config.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i<inputShape.length;++i)axes[i]=inputShape[i];this.inputSpec=[new InputSpec({ndim:inputShape.length,axes})],this.built=!0}call(inputs,kwargs){return inputs=getExactlyOneTensor(inputs),prelu(inputs,this.alpha.read())}getConfig(){let config={alphaInitializer:serializeInitializer(this.alphaInitializer),alphaRegularizer:serializeRegularizer(this.alphaRegularizer),alphaConstraint:serializeConstraint(this.alphaConstraint),sharedAxes:this.sharedAxes},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};PReLU.className="PReLU";serialization_exports.registerClass(PReLU);var ELU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA=1,args==null&&(args={}),args.alpha!=null&&args.alpha!==this.DEFAULT_ALPHA)throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return elu(x)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ELU.className="ELU";serialization_exports.registerClass(ELU);var ThresholdedReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_THETA=1,args==null&&(args={}),this.theta=args.theta==null?this.DEFAULT_THETA:args.theta}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return x.mul(cast2(x.greater(this.theta),"float32"))}computeOutputShape(inputShape){return inputShape}getConfig(){let config={theta:this.theta},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ThresholdedReLU.className="ThresholdedReLU";serialization_exports.registerClass(ThresholdedReLU);var Softmax3=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_AXIS=1,args==null&&(args={}),this.softmax=new Softmax2().apply,this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return this.softmax(x,this.axis)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Softmax3.className="Softmax";serialization_exports.registerClass(Softmax3);function normalizeArray(value,n,name){if(typeof value=="number")return pyListRepeat(value,n);if(value.length!==n)throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);for(let i=0;i<n;++i){let singleValue=value[i];if(!isInteger(singleValue))throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`)}return value}function convOutputLength(inputLength,filterSize,padding,stride,dilation=1){if(inputLength==null)return inputLength;let dilatedFilterSize=filterSize+(filterSize-1)*(dilation-1),outputLength;return padding==="same"?outputLength=inputLength:outputLength=inputLength-dilatedFilterSize+1,Math.floor((outputLength+stride-1)/stride)}function deconvLength(dimSize,strideSize,kernelSize,padding){if(dimSize==null)return null;if(padding==="valid")dimSize=dimSize*strideSize+max2([kernelSize-strideSize,0]);else if(padding==="same")dimSize=dimSize*strideSize;else throw new ValueError(`Unsupport padding mode: ${padding}.`);return dimSize}function preprocessConv2DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i<space.length;++i){let newDim=convOutputLength(space[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);newSpace.push(newDim)}let outputShape=[inputShape[0]];return this.dataFormat==="channelsLast"?(outputShape=outputShape.concat(newSpace),outputShape.push(this.filters)):(outputShape.push(this.filters),outputShape=outputShape.concat(newSpace)),outputShape}getConfig(){let config={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static verifyArgs(args){if(!("filters"in args)||typeof args.filters!="number"||args.filters<1)throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config=super.getConfig();return delete config.dilationRate,config}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config){super(rank,config);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config.kernelInitializer!=null||config.kernelRegularizer!=null||config.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config.padding!=null&&config.padding!=="same"&&config.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);this.depthMultiplier=config.depthMultiplier==null?1:config.depthMultiplier,this.depthwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<this.rank+2)throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(inputShape)}`);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=this.kernelSize.concat([inputDim,this.depthMultiplier]),pointwiseKernelShape=[];for(let i=0;i<this.rank;++i)pointwiseKernelShape.push(1);pointwiseKernelShape.push(inputDim*this.depthMultiplier,this.filters);let trainable=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,trainable,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",pointwiseKernelShape,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,trainable,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,trainable,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config=super.getConfig();return delete config.rank,delete config.kernelInitializer,delete config.kernelRegularizer,delete config.kernelConstraint,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config=super.getConfig();return delete config.rank,delete config.dataFormat,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d3(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d3(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config=super.getConfig();return config.depthMultiplier=this.depthMultiplier,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range2(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;t<timeSteps;++t){let currentInput=perStepInputs[t],stepOutputs=tidy(()=>stepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range2(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;i<numStates;++i)output.push(null);return output}else return this.states_}set states(s){this.states_=s}build(inputShape){let constantShape=null;if(this.numConstants!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let batchSize=this.stateful?inputShape[0]:null,inputDim=inputShape.slice(2);this.inputSpec[0]=new InputSpec({shape:[batchSize,null,...inputDim]});let stepInputShape=[inputShape[0]].concat(inputShape.slice(2));if(constantShape!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");this.cell.build(stepInputShape);let stateSize;if(Array.isArray(this.cell.stateSize)?stateSize=this.cell.stateSize:stateSize=[this.cell.stateSize],this.stateSpec!=null){if(!util_exports.arraysEqual(this.stateSpec.map(spec=>spec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],dim=Array.isArray(this.cell.stateSize)?this.cell.stateSize[index]:this.cell.stateSize,expectedShape=[batchSize,dim];if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims2(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile2(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile2(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cellConfig=config.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return new cls(config)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let matrixX=dot2(inputs,this.kernel.read());this.useBias&&(matrixX=biasAdd(matrixX,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0]));let recurrentKernelValue=this.recurrentKernel.read(),[rk1,rk2]=split(recurrentKernelValue,[2*this.units,this.units],recurrentKernelValue.rank-1),matrixInner=dot2(hTMinus1,rk1),[xZ,xR,xH]=split(matrixX,3,matrixX.rank-1),[recurrentZ,recurrentR]=split(matrixInner,2,matrixInner.rank-1);z=this.recurrentActivation.apply(add2(xZ,recurrentZ)),r=this.recurrentActivation.apply(add2(xR,recurrentR));let recurrentH=dot2(mul(r,hTMinus1),rk2);hh=this.activation.apply(add2(xH,recurrentH));let h=add2(mul(z,hTMinus1),mul(add2(1,neg(z)),hh));return[h,h]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},baseConfig,config)}};GRUCell.className="GRUCell";serialization_exports.registerClass(GRUCell);var GRU=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new GRUCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let z=dot2(inputs,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0])),z=add2(z,dot2(hTMinus1,this.recurrentKernel.read())),this.useBias&&(z=biasAdd(z,this.bias.read()));let[z0,z1,z2,z3]=split(z,4,z.rank-1);i=this.recurrentActivation.apply(z0),f=this.recurrentActivation.apply(z1),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(z2))),o=this.recurrentActivation.apply(z3);let h=mul(o,this.activation.apply(c));return[h,h,c]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},baseConfig,config)}};LSTMCell.className="LSTMCell";serialization_exports.registerClass(LSTMCell);var LSTM=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new LSTMCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i<this.cells.length;++i){let cell=this.cells[i];states=nestedStates[i],i===0?callInputs=[inputs[0]].concat(states):callInputs=[callInputs[0]].concat(states),callInputs=cell.call(callInputs,kwargs),newNestedStates.push(callInputs.slice(1))}states=[];for(let cellStates of newNestedStates.slice().reverse())states.push(...cellStates);return[callInputs[0]].concat(states)})}build(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let outputDim;this.cells.forEach((cell,i)=>{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cells=[];for(let cellConfig of config.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;i<cell.weights.length;++i)tuples.push([cell.weights[i],inputWeights[i]])}batchSetValue(tuples)}};StackedRNNCells.className="StackedRNNCells";serialization_exports.registerClass(StackedRNNCells);function generateDropoutMask(args){let{ones:ones4,rate,training=!1,count:count2=1}=args,droppedInputs=()=>dropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i<p2.length;i++)e.indexOf(p2[i])<0&&Object.prototype.propertyIsEnumerable.call(s,p2[i])&&(t[p2[i]]=s[p2[i]]);return t},ConvRNN2D=class extends RNN{constructor(args){if(args.unroll)throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(args.cell))throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(args);this.inputSpec=[new InputSpec({ndim:5})]}call(inputs,kwargs){return tidy(()=>{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],expectedShape=stateShape;if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config){return new cls(config)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i<this.noiseShape.length;++i)noiseShape.push(this.noiseShape[i]==null?inputShape[i]:this.noiseShape[i]);return noiseShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0<this.rate&&this.rate<1){let training=kwargs.training==null?!1:kwargs.training,noiseShape=this.getNoiseShape(input2);return inTrainPhase(()=>dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i<input2.rank;++i)permutation.push(i);permutation.push(1),input2=input2.transpose(permutation)}return batchFlatten(input2)})}getConfig(){let config={};this.dataFormat!=null&&(config.dataFormat=this.dataFormat);let baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Flatten.className="Flatten";serialization_exports.registerClass(Flatten);var Activation2=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.activation=getActivation(args.activation)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config={n:this.n},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i<this.targetShape.length;++i)this.isUnknown(this.targetShape[i])&&(this.targetShape[i]=null)}isUnknown(dim){return dim<0||dim==null}fixUnknownDimension(inputShape,outputShape){let errorMsg="Total size of new array must be unchanged.",finalShape=outputShape.slice(),known=1,unknown=null;for(let i=0;i<finalShape.length;++i){let dim=finalShape[i];if(this.isUnknown(dim))if(unknown===null)unknown=i;else throw new ValueError("Can only specifiy one unknown dimension.");else known*=dim}let originalSize=arrayProd(inputShape);if(unknown!==null){if(known===0||originalSize%known!=0)throw new ValueError(errorMsg);finalShape[unknown]=originalSize/known}else if(originalSize!==known)throw new ValueError(errorMsg);return finalShape}computeOutputShape(inputShape){let anyUnknownDims=!1;for(let i=0;i<inputShape.length;++i)if(this.isUnknown(inputShape[i])){anyUnknownDims=!0;break}return anyUnknownDims?inputShape.slice(0,1).concat(this.targetShape):inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape))}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range2(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={maskValue:this.maskValue};return Object.assign(config,baseConfig),config}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k<inLens.length;++k){let s1=inLens[k],s2=inputShape[k+1];if(s1!=null&&s2!=null&&s1!==s2)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);s1==null&&(inLens[i]=s2),i++}}return[inputShape[0],...inLens,this.outputDim]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast2(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length<shape2.length)return this.computeElementwiseOpOutputShape(shape2,shape1);if(shape2.length===0)return shape1;let outputShape=shape1.slice(0,shape1.length-shape2.length);for(let k=0;k<shape2.length;++k){let i=shape1[shape1.length-shape2.length+k],j=shape2[k];if(i==null||j==null||i<0||j<0)outputShape.push(null);else if(i===1)outputShape.push(j);else if(j===1)outputShape.push(i);else{if(i!==j)throw new ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(shape1)+" "+JSON.stringify(shape2));outputShape.push(i)}}return outputShape}build(inputShape){if(Array.isArray(inputShape)&&!Array.isArray(inputShape[0])&&(inputShape=[getExactlyOneShape(inputShape)]),inputShape=inputShape,inputShape.length<2)throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);if(batchSizes=unique2(batchSizes),batchSizes.length>1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let allRanks=inputShape.map(shape=>shape.length);inputShape.indexOf(null)===-1&&unique2(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max2(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k<maxNDim-xNDim;++k)x=expandDims2(x,1);reshapedInputs.push(x)}return this.mergeFunction(reshapedInputs)}else{let transposed=!1;for(let x of inputs){let xNDim=x.rank;if(xNDim==null){let xShape=x.shape,batchSize=xShape[0],newShape=xShape.slice(1).concat([batchSize]),xTransposed=x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));xTransposed=transpose(xTransposed,[1,0]),xTransposed=xTransposed.reshape(newShape),reshapedInputs.push(xTransposed),transposed=!0}else if(xNDim>1){let dims=range2(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range2(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);return batchSizes=unique2(batchSizes),batchSizes.length===1?outputShape=batchSizes.concat(outputShape):outputShape=[null].concat(outputShape),outputShape}computeMask(inputs,mask){return tidy(()=>{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i<mask.length-1;++i)output=logicalAnd(output,mask[i]);return output})}},Add2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return output})}};Add2.className="Add";serialization_exports.registerClass(Add2);var Multiply2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=mul(output,inputs[i]);return output})}};Multiply2.className="Multiply";serialization_exports.registerClass(Multiply2);var Average=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return mul(1/inputs.length,output)})}};Average.className="Average";serialization_exports.registerClass(Average);var Maximum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=maximum(output,inputs[i]);return output})}};Maximum2.className="Maximum";serialization_exports.registerClass(Maximum2);var Minimum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=minimum(output,inputs[i]);return output})}};Minimum2.className="Minimum";serialization_exports.registerClass(Minimum2);var Concatenate=class extends Merge{constructor(args){super(args);this.DEFAULT_AXIS=-1,args==null&&(args={}),this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0]))||inputShape.length===1)throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");inputShape=inputShape;let allNoneShape=!0;for(let shape of inputShape)if(shape!=null){allNoneShape=!1;break}if(allNoneShape)return;let shapeSet=[];for(let i=0;i<inputShape.length;++i){let shapeWithoutConcatAxis=inputShape[i].slice();shapeWithoutConcatAxis.splice(this.axis,1);let exists=!1;for(let shape of shapeSet)if(util_exports.arraysEqual(shape,shapeWithoutConcatAxis)){exists=!0;break}exists||shapeSet.push(shapeWithoutConcatAxis)}if(shapeSet.length>1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i<inputs.length;++i)mask[i]==null?outputMasks.push(onesLike(inputs[i]).asType("bool")):mask[i].rank<inputs[i].rank?outputMasks.push(expandDims(mask[i],-1)):outputMasks.push(mask[i]);let concatenatedMasks=concat(outputMasks,this.axis);return all(concatenatedMasks,-1,!1)})}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Concatenate.className="Concatenate";serialization_exports.registerClass(Concatenate);function interpretAxis(axis,dim){for(;axis<0;)axis+=dim;return axis}function batchDot(x,y,axes){if(x.shape.length>3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);y=y.reshape(y.shape.concat(diffShape))}else if(yNDim>xNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);x=x.reshape(x.shape.concat(diffShape))}else diff=0;let out;if(x.shape.length===2&&y.shape.length===2)axesArray[0]===axesArray[1]?out=x.mul(y).sum(axesArray[0]):out=x.transpose([1,0]).mul(y).sum(axesArray[1]);else{let adjX=axesArray[0]!==x.shape.length-1,adjY=axesArray[1]===y.shape.length-1;out=x.matMul(y,adjX,adjY)}if(diff>0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i<idx+diff;++i)squeezeAxes.push(i);out=out.squeeze(squeezeAxes)}return out.shape.length===1&&(out=out.expandDims(1)),out})}var Dot=class extends Merge{constructor(args){super(args);this.axes=args.axes,this.normalize=args.normalize==null?!1:args.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={stddev:this.stddev};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale22=1.0507009873554805,alphaP=-alpha*scale22,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast2(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon32=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon32);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon32);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon32);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon32),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range2(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon32),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range2(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range2(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range2(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=nDims);for(let axis of this.axis)if(axis<0||axis>=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique2(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale22=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i<nDims;++i)this.axis.indexOf(i)!==-1?(momentsTiling.push(inputShape[i]),scaleOffsetTiling.push(1)):(momentsTiling.push(1),scaleOffsetTiling.push(inputShape[i]));return mean4=mean4.tile(momentsTiling),variance=variance.tile(momentsTiling),scale22=scale22.tile(scaleOffsetTiling),offset=offset.tile(scaleOffsetTiling),batchNormalization(input2,mean4,variance,offset,scale22,this.epsilon)})}getConfig(){let config={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LayerNormalization.className="LayerNormalization";serialization_exports.registerClass(LayerNormalization);function spatial2dPadding(x,padding,dataFormat){return tidy(()=>{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims2(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config,customObjects={}){let layerConfig=config.layer,layer=deserialize(layerConfig,customObjects);delete config.layer;let newConfig={layer};return Object.assign(newConfig,config),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static fromConfig(cls,config){let rnnLayer=deserialize(config.layer);if(delete config.layer,config.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu3(args){return new ELU(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu2(args){return new PReLU(args)}function softmax2(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d3(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d4(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape2(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add3(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum2(args){return new Maximum2(args)}function minimum2(args){return new Minimum2(args)}function multiply(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config){return new L1L2(config)}function l12(config){return l1(config)}function l22(config){return l2(config)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less2(currVal,prevVal){return currVal<prevVal}function greater2(currVal,prevVal){return currVal>prevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less2:this.mode==="max"?this.monitorFunc=greater2:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater2:this.monitorFunc=less2,this.monitorFunc===less2&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less2?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node2,tensorMap,context,resourceManager){let inputParam=node2.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node2.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node2.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node2.inputNames.slice(start)[0],tensorMap,context,resourceManager),data=tensor2.dataSync();return inputParam.type==="number"?data[0]:util_exports.toNestedArray(tensor2.shape,data)}let attrParam=node2.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node2,tensorMap,context){let pad3=getParamValue("pad",node2,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node2,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var 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json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],reduction_exports={};__export2(reduction_exports,{json:()=>json14});var json14=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],slice_join_exports={};__export2(slice_join_exports,{json:()=>json15});var json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],spectral_exports={};__export2(spectral_exports,{json:()=>json16});var json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],transformation_exports={};__export2(transformation_exports,{json:()=>json17});var json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op.startsWith("Placeholder")?placeholders.push(map[node2.name]):node2.op==="Const"?weights.push(map[node2.name]):(node2.input==null||node2.input.length===0)&&initNodes.push(map[node2.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node2=nodes[key];node2.children.length===0&&outputs.push(node2)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2!=null&&(node2.signatureKey=outputNodeNameToKey[name],outputs.push(node2))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2&&(node2.signatureKey=inputNodeNameToKey[name],inputs.push(node2))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node2){let mapper=getRegisteredOp(node2.op)||this.opMappers[node2.op]||{};node2.attr==null&&(node2.attr={});let newNode={name:node2.name,op:node2.op,category:mapper.category,inputNames:(node2.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node2.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node2.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node2.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op==="Const"&&weights.push(map[node2.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node2={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node2.signatureKey=arg.name,inputs.push(node2),nodes[nodeName]=node2}),Object.keys(nodes).forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node2=nodes[nodeName];node2!=null&&(node2.defaultOutput=index,outputs.push(node2))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){switch(typeof value=="string"&&(value=DataType[value]),value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&&param.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&&param.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&&param.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&&param.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&&param.list&&param.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node2,tensorMap,context){this.node=node2,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node2.inputNames.map(name=>this.getInput(name)),node2.rawAttrs!=null&&(this.attrs=Object.keys(node2.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node2,tensorMap,context)=>{switch(node2.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node2,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp2=(node2,tensorMap,context)=>{switch(node2.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node2,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node2,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node2,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node2,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node2,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node2,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node2,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node2,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node2,tensorMap,context),getParamValue("imag",node2,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node2,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node2,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node2,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node2,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node2,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node2,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node2,tensorMap,context))];case"Log":return[log(getParamValue("x",node2,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node2,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node2,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node2,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node2,tensorMap,context))];case"Real":return[real(getParamValue("x",node2,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node2,tensorMap,context))];case"Round":return[round2(getParamValue("x",node2,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node2,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node2,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node2,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node2,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node2,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node2,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node2,tensorMap,context))];case"Square":return[square(getParamValue("x",node2,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node2,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node2,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node2,tensorMap,context),getParamValue("clipValueMin",node2,tensorMap,context),getParamValue("clipValueMax",node2,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node2,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node2.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node2,tensorMap,context),getParamValue("axes",node2,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==-1&&n2[i]!==-1&&n1[i]!==n2[i])return!1;return!0}var TensorArray=class{constructor(name,dtype,maxSize,elementShape,identicalElementShapes,dynamicSize,clearAfterRead){this.name=name,this.dtype=dtype,this.maxSize=maxSize,this.elementShape=elementShape,this.identicalElementShapes=identicalElementShapes,this.dynamicSize=dynamicSize,this.clearAfterRead=clearAfterRead,this.tensors=[],this.closed_=!1,this.idTensor=scalar(0),keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
           because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=tensor2.shape),assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${index}.`),t.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);if(t.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);t.tensor=tensor2,keep(tensor2),t.written=!0,this.tensors[index]=t}writeMany(indices,tensors){if(indices.length!==tensors.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);indices.forEach((i,index)=>this.write(i,tensors[index]))}gather(indices,dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);if(indices)indices=indices.slice(0,this.size());else{indices=[];for(let i=0;i<this.size();i++)indices.push(i)}if(indices.length===0)return tensor([],[0].concat(this.elementShape));let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,"TensorArray shape mismatch: "),stack(tensors,0)}concat(dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);if(this.size()===0)return tensor([],[0].concat(this.elementShape));let indices=[];for(let i=0;i<this.size();i++)indices.push(i);let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`),concat(tensors,0)}scatter(indices,tensor2){if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);if(indices.length!==tensor2.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);let maxIndex=Math.max(...indices);if(!this.dynamicSize&&maxIndex>=this.maxSize)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);this.writeMany(indices,unstack(tensor2,0))}split(length,tensor2){if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
         ${totalLength}, and tensor's shape is: ${tensor2.shape}`);if(!this.dynamicSize&&length.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=[];tidy(()=>{tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices2=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors[i]=reshape(slice(tensor2,indices2,sizes),this.elementShape)}return tensors});let indices=[];for(let i=0;i<length.length;i++)indices[i]=i;this.writeMany(indices,tensors)}},TensorList=class{constructor(tensors,elementShape,elementDtype,maxNumElements=-1){this.tensors=tensors,this.elementShape=elementShape,this.elementDtype=elementDtype,tensors!=null&&tensors.forEach(tensor2=>{if(elementDtype!==tensor2.dtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);assertShapesMatchAllowUndefinedSize(elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2)}),this.idTensor=scalar(0),this.maxNumElements=maxNumElements,keep(this.idTensor)}get id(){return this.idTensor.id}copy(){return new TensorList([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.id))&&tensor2.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(elementShape,elementDtype,numElements=-1){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(numElements!==-1&&this.tensors.length!==numElements)throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);return assertShapesMatchAllowUndefinedSize(elementShape,this.elementShape,"TensorList shape mismatch: "),tidy(()=>{let reshapedTensors=this.tensors.map(tensor2=>reshape(tensor2,elementShape));return stack(reshapedTensors,0)})}popBack(elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let tensor2=this.tensors.pop();return assertShapesMatchAllowUndefinedSize(tensor2.shape,elementShape,"TensorList shape mismatch: "),reshape(tensor2,elementShape)}pushBack(tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(assertShapesMatchAllowUndefinedSize(tensor2.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");keep(tensor2),this.tensors.push(tensor2)}resize(size){if(size<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);if(this.maxNumElements!==-1&&size>this.maxNumElements)throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=size}getItem(elementIndex,elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||elementIndex>this.tensors.length)throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);if(this.tensors[elementIndex]==null)throw new Error(`element at index ${elementIndex} is null.`);return assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape,elementShape,"TensorList shape mismatch: "),this.tensors[elementIndex]}setItem(elementIndex,tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||this.maxNumElements!==-1&&elementIndex>=this.maxNumElements)throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2),this.tensors[elementIndex]=tensor2}gather(indices,elementDtype,elementShape){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),indices=indices.slice(0,this.size()),indices.length===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=indices.map(i=>reshape(this.tensors[i],elementShape));return stack(tensors,0)})}concat(elementDtype,elementShape){if(!!elementDtype&&elementDtype!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),this.size()===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=this.tensors.map(t=>reshape(t,elementShape));return concat(tensors,0)})}};function fromTensor(tensor2,elementShape,elementDtype){let dtype=tensor2.dtype;if(tensor2.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);if(tensor2.dtype!==elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);let outputShape=tensor2.shape.slice(1);assertShapesMatchAllowUndefinedSize(outputShape,elementShape,"TensorList shape mismatch: ");let tensorList=unstack(tensor2);return new TensorList(tensorList,elementShape,dtype)}function reserve(elementShape,elementDtype,numElements){return new TensorList([],elementShape,elementDtype,numElements)}function scatter(tensor2,indices,elementShape,numElements){if(indices.length!==tensor2.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);let maxIndex=Math.max(...indices);if(numElements!=null&&numElements!==-1&&maxIndex>=numElements)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);let list=new TensorList([],elementShape,tensor2.dtype,numElements),tensors=unstack(tensor2,0);return indices.forEach((value,index)=>{list.setItem(value,tensors[index])}),list}function split2(tensor2,length,elementShape){let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
-        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors2[i]=reshape(slice(tensor2,indices,sizes),elementShape)}return tensor2.dispose(),tensors2}),list=new TensorList([],elementShape,tensor2.dtype,length.length);for(let i=0;i<tensors.length;i++)list.setItem(i,tensors[i]);return list}var executeOp3=async(node2,tensorMap,context)=>{switch(node2.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node2,tensorMap,context),elseFunc=getParamValue("elseBranch",node2,tensorMap,context),cond=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node2,tensorMap,context),condFunc=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node2,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context);return data.kept||(data=cloneTensor(data)),(await pred.data())[0]?[void 0,data]:[data,void 0]}case"Merge":{let inputName=node2.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data=getTensor(inputName,tensorMap,context);return[cloneTensor(data)]}return}case"Enter":{let frameId=getParamValue("frameName",node2,tensorMap,context),data=getParamValue("tensor",node2,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data)]}case"Exit":{let data=getParamValue("tensor",node2,tensorMap,context);return context.exitFrame(),[cloneTensor(data)]}case"NextIteration":{let data=getParamValue("tensor",node2,tensorMap,context);return context.nextIteration(),[cloneTensor(data)]}case"TensorArrayV3":{let size=getParamValue("size",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node2,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node2,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node2,tensorMap,context),name=getParamValue("name",node2,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),gatherDtype=getParamValue("dtype",node2,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node2,tensorMap,context),scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node2,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node2,tensorMap,context),splitTensor=getParamValue("tensor",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node2,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node2,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElementsParam;node2.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node2,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node2,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node2,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node2,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node2,tensorMap,context)=>{switch(node2.op){case"Conv1D":{let stride=getParamValue("stride",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node2,tensorMap,context);return[conv1d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node2,tensorMap,context),stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context);return[conv2dTranspose(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv3d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node2,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp5=(node2,tensorMap,context)=>{switch(node2.op){case"Fill":{let shape=getParamValue("shape",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),value=getParamValue("value",node2,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),num=getParamValue("num",node2,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node2,tensorMap,context),numSamples=getParamValue("numSamples",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node2,tensorMap,context),depth=getParamValue("depth",node2,tensorMap,context),onValue=getParamValue("onValue",node2,tensorMap,context),offValue=getParamValue("offValue",node2,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node2,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node2,tensorMap,context),getParamValue("minval",node2,tensorMap,context),getParamValue("maxval",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"Range":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),step4=getParamValue("step",node2,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node2,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node2,tensorMap,context),mean4=getParamValue("mean",node2,tensorMap,context),stdDev=getParamValue("stdDev",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node2,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function nmsParams(node2,tensorMap,context){let boxes=getParamValue("boxes",node2,tensorMap,context),scores=getParamValue("scores",node2,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node2,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node2,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node2,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node2,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node2,tensorMap,context)=>{switch(node2.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node2,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node2,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node2,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context));default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp7=(node2,tensorMap,context)=>{switch(node2.op){case"TopKV2":{let x=getParamValue("x",node2,tensorMap,context),k=getParamValue("k",node2,tensorMap,context),sorted=getParamValue("sorted",node2,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node2,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp8=(node2,tensorMap,context)=>{switch(node2.op){case"Const":return tensorMap[node2.name];case"PlaceholderWithDefault":let def=getParamValue("default",node2,tensorMap,context);return[getTensor(node2.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node2.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data2=getParamValue("x",node2,tensorMap,context);return[cloneTensor(data2)]}case"IdentityN":return getParamValue("x",node2,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node2,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node2,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node2,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node2,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node2,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context),message=getParamValue("message",node2,tensorMap,context),summarize=getParamValue("summarize",node2,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;i<data.length;i++)console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0,summarize));return[input2];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},HashTable=class{constructor(keyDType,valueDType){this.keyDType=keyDType,this.valueDType=valueDType,this.handle=scalar(0),this.tensorMap=new Map,keep(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i<keysLength;i++){let key=$keys[i],value=$values[i];keep(value),this.tensorMap.set(key,value)}return this.handle})}async find(keys,defaultValue){this.checkKeyAndValueTensor(keys,defaultValue);let $keys=await keys.data();return tidy(()=>{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node2,tensorMap,context,resourceManager)=>{switch(node2.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node2,tensorMap,context),valueDType=getParamValue("valueDType",node2,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node2.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp10=(node2,tensorMap,context)=>{switch(node2.op){case"ResizeBilinear":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node2,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node2,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node2,tensorMap,context),boxes=getParamValue("boxes",node2,tensorMap,context),boxInd=getParamValue("boxInd",node2,tensorMap,context),cropSize=getParamValue("cropSize",node2,tensorMap,context),method=getParamValue("method",node2,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node2,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp11=(node2,tensorMap,context)=>{switch(node2.op){case"Equal":return[equal(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Less":return[less(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node2,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node2,tensorMap,context),getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp12=(node2,tensorMap,context)=>{switch(node2.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context),getParamValue("transposeA",node2,tensorMap,context),getParamValue("transposeB",node2,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node2,tensorMap,context),getParamValue("perm",node2,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node2,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node2,tensorMap,context),b:getParamValue("b",node2,tensorMap,context),transposeA:getParamValue("transposeA",node2,tensorMap,context),transposeB:getParamValue("transposeB",node2,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp13=(node2,tensorMap,context)=>{switch(node2.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node2,tensorMap,context),getParamValue("radius",node2,tensorMap,context),getParamValue("bias",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context),getParamValue("beta",node2,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node2,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node2,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node2,tensorMap,context),getParamValue("outputShape",node2,tensorMap,context),getParamValue("sparseValues",node2,tensorMap,context),getParamValue("defaultValue",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp14=(node2,tensorMap,context)=>{switch(node2.op){case"Max":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[max(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[mean(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[min(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[sum2(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[all(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[any(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMax(getParamValue("x",node2,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMin(getParamValue("x",node2,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[prod(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node2,tensorMap,context),exclusive=getParamValue("exclusive",node2,tensorMap,context),reverse5=getParamValue("reverse",node2,tensorMap,context);return[cumsum(getParamValue("x",node2,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node2,tensorMap,context),weights=getParamValue("weights",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node2,tensorMap,context),weights2=getParamValue("weights",node2,tensorMap,context),size2=getParamValue("size",node2,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node2,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp15=(node2,tensorMap,context)=>{switch(node2.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),inputs=getParamValue("tensors",node2,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node2,tensorMap,context),batchDims=getParamValue("batchDims",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node2,tensorMap,context),axis=[];for(let i=0;i<dims.length;i++)dims[i]&&axis.push(i);let input2=getParamValue("x",node2,tensorMap,context);return[reverse(input2,axis)]}case"ReverseV2":{let axis=getParamValue("axis",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context);return[reverse(input2,axis)]}case"Slice":{let begin=getParamValue("begin",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context);return[slice(getParamValue("x",node2,tensorMap,context),begin,size)]}case"StridedSlice":{let begin=getParamValue("begin",node2,tensorMap,context),end=getParamValue("end",node2,tensorMap,context),strides=getParamValue("strides",node2,tensorMap,context),beginMask=getParamValue("beginMask",node2,tensorMap,context),endMask=getParamValue("endMask",node2,tensorMap,context),ellipsisMask=getParamValue("ellipsisMask",node2,tensorMap,context),newAxisMask=getParamValue("newAxisMask",node2,tensorMap,context),shrinkAxisMask=getParamValue("shrinkAxisMask",node2,tensorMap,context),tensor2=getParamValue("x",node2,tensorMap,context);return[stridedSlice(tensor2,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)]}case"Pack":return tidy(()=>{let axis=getParamValue("axis",node2,tensorMap,context),tensors=getParamValue("tensors",node2,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node2,tensorMap,context),tensor2=getParamValue("tensor",node2,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node2,tensorMap,context);return[tile(getParamValue("x",node2,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node2,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node2,tensorMap,context),tensor2=getParamValue("x",node2,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context),shape=getParamValue("shape",node2,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node2,tensorMap,context),shape=getParamValue("outputShape",node2,tensorMap,context),sparseValues=getParamValue("sparseValues",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp16=(node2,tensorMap,context)=>{switch(node2.op){case"FFT":return[fft(getParamValue("x",node2,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node2,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node2,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp17=(node2,tensorMap,context)=>{switch(node2.op){case"Cast":return[cast(getParamValue("x",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node2,tensorMap,context);return[expandDims(getParamValue("x",node2,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node2,tensorMap,context);return[squeeze(getParamValue("x",node2,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("mode",node2,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("constantValue",node2,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),paddings=getParamValue("paddings",node2,tensorMap,context);return[spaceToBatchND(getParamValue("x",node2,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),crops=getParamValue("crops",node2,tensorMap,context);return[batchToSpaceND(getParamValue("x",node2,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node2,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function executeOp18(node2,tensorMap,context,resourceManager){let value=((node22,tensorMap2,context2)=>{switch(node22.category){case"arithmetic":return tidy(()=>executeOp(node22,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node22,tensorMap2,context2));case"control":return executeOp3(node22,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node22,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node22,tensorMap2,context2));case"dynamic":return executeOp6(node22,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node22,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node22,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node22,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node22,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node22,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node22,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node22,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node22,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node22,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node22,tensorMap2,context2));case"hash_table":return executeOp9(node22,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node22.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node22,tensorMap2,context2));throw TypeError(`Custom op ${node22.op} is not registered.`);default:throw TypeError(`Unknown op '${node22.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node2,tensorMap,context);return util_exports.isPromise(value)?value.then(data=>[].concat(data)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;i<this.contexts.length-1;i++){let contexts2=this.contexts.slice(0,this.contexts.length-i);names.push(this.contextIdforContexts(contexts2))}names.push(""),this._currentContextIds=names}contextIdforContexts(contexts2){return contexts2?contexts2.map(context=>context.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node2=>parseNodeName(node2.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node2=frontier.pop();if(((isControlFlow(node2)||isDynamicShape(node2)||isHashTable(node2))&&dynamicNode==null&&(dynamicNode=node2,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node2.name),weightMap[node2.name]==null)&&inputNodeNames.indexOf(node2.name)===-1&&initNodeNames.indexOf(node2.name)===-1){if(node2.inputs.length===0){missingInputs.push(node2.name);continue}node2.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node2=>{usedNodes.has(node2.name)&&frontier.push(node2)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node2=frontier.pop();seen.add(node2.name),weightMap[node2.name]||orderedNodes.push(node2),node2.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node2){return CONTROL_FLOW_OPS.indexOf(node2.op)>=0}function isDynamicShape(node2){return DYNAMIC_SHAPE_OPS.indexOf(node2.op)>=0}function isHashTable(node2){return HASH_TABLE_OPS.indexOf(node2.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node2=>node2.signatureKey||node2.name)}get outputNodes(){return this._outputs.map(node2=>{let name=node2.signatureKey||node2.name;return node2.defaultOutput?`${name}:${node2.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node2=>node2.name).sort(),sortedOutputs=outputs.map(node2=>node2.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;i<orderedNodes.length;i++){let node2=orderedNodes[i];if(!tensorsMap[node2.name]){let tensors=executeOp18(node2,tensorsMap,context,this._resourceManager);if(util_exports.isPromise(tensors))throw new Error(`The execution of the op '${node2.op}' returned a promise. Please use model.executeAsync() instead.`);tensorsMap[node2.name]=tensors,this.checkTensorForDisposal(node2.name,node2,tensorsMap,context,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount)}}return this.parent==null&&context.dispose(tensorsToKeep),outputs.map(name=>getTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node2,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node2.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node2.children.length)}),node2.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node2=>({node:node2,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node2=>!isControlFlow(node2)&&!getTensor(node2.name,tensorsMap,context)).map(node2=>node2.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node2,stack2,context,tensorMap,added,usedNodes){node2.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node2=this.graph.nodes[nodeName];if(node2.attrParams.shape&&node2.attrParams.shape.value){let shape=node2.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node2.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node2.attrParams.dtype&&node2.attrParams.dtype.value&&util_exports.assert(input2.dtype===node2.attrParams.dtype.value,()=>`The dtype of dict['${node2.name}'] provided in model.execute(dict) must be ${node2.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version3="2.8.2",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version4,webcam:()=>webcam,zip:()=>zip});var seedrandom3=__toModule2(require_seedrandom2()),seedrandom2=__toModule2(require_seedrandom2());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;i<len;i++)newData[i]=this.get(this.wrap(this.begin+i));this.data=newData,this.capacity=newCapacity,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=len}};GrowingRingBuffer.INITIAL_CAPACITY=32;function iteratorFromItems(items){return new ArrayIterator(items)}function iteratorFromFunction(func2){return new FunctionCallIterator(func2)}function iteratorFromConcatenated(baseIterators,baseErrorHandler){return new ChainedIterator(baseIterators,baseErrorHandler)}function iteratorFromZipped(iterators,mismatchMode=ZipMismatchMode.FAIL){return new ZipIterator(iterators,mismatchMode)}var LazyIterator=class{async toArray(){let result=[],x=await this.next();for(;!x.done;)result.push(x.value),x=await this.next();return result}async toArrayForTest(){let stream=this.prefetch(100),result=[],x=await stream.next();for(;!x.done;)result.push(x.value),x=await stream.next();return result}async resolveFully(){let x=await this.next();for(;!x.done;)x=await this.next()}async resolveWhile(predicate){let x=await this.next(),shouldContinue=predicate(x.value);for(;!x.done&&shouldContinue;)x=await this.next(),shouldContinue=predicate(x.value)}handleErrors(handler){return new ErrorHandlingLazyIterator(this,handler)}filter(predicate){return new FilterIterator(this,predicate)}map(transform){return new MapIterator(this,transform)}mapAsync(transform){return new AsyncMapIterator(this,transform)}serialMapAsync(transform){return new AsyncMapIterator(this,transform).serial()}flatmap(transform){return new FlatmapIterator(this,transform)}async forEachAsync(f){return this.map(f).resolveFully()}async serialForEach(f){return this.serialMapAsync(f).resolveWhile(x=>x===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let skipped=await this.upstream.next();if(skipped.done)return skipped;dispose(skipped.value)}return this.upstream.next()}},TakeIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length<this.batchSize;){let item=await this.upstream.next();if(item.done)return this.enableSmallLastBatch&&batch.length>0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom2.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is
+        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors2[i]=reshape(slice(tensor2,indices,sizes),elementShape)}return tensor2.dispose(),tensors2}),list=new TensorList([],elementShape,tensor2.dtype,length.length);for(let i=0;i<tensors.length;i++)list.setItem(i,tensors[i]);return list}var executeOp3=async(node2,tensorMap,context)=>{switch(node2.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node2,tensorMap,context),elseFunc=getParamValue("elseBranch",node2,tensorMap,context),cond=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node2,tensorMap,context),condFunc=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node2,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context);return data.kept||(data=cloneTensor(data)),(await pred.data())[0]?[void 0,data]:[data,void 0]}case"Merge":{let inputName=node2.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data=getTensor(inputName,tensorMap,context);return[cloneTensor(data)]}return}case"Enter":{let frameId=getParamValue("frameName",node2,tensorMap,context),data=getParamValue("tensor",node2,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data)]}case"Exit":{let data=getParamValue("tensor",node2,tensorMap,context);return context.exitFrame(),[cloneTensor(data)]}case"NextIteration":{let data=getParamValue("tensor",node2,tensorMap,context);return context.nextIteration(),[cloneTensor(data)]}case"TensorArrayV3":{let size=getParamValue("size",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node2,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node2,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node2,tensorMap,context),name=getParamValue("name",node2,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),gatherDtype=getParamValue("dtype",node2,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node2,tensorMap,context),scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node2,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node2,tensorMap,context),splitTensor=getParamValue("tensor",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node2,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node2,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElementsParam;node2.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node2,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node2,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node2,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node2,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node2,tensorMap,context)=>{switch(node2.op){case"Conv1D":{let stride=getParamValue("stride",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node2,tensorMap,context);return[conv1d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node2,tensorMap,context),stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context);return[conv2dTranspose(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getPadding(node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv3d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node2,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node2,tensorMap,context),pad3=getParamValue("pad",node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp5=(node2,tensorMap,context)=>{switch(node2.op){case"Fill":{let shape=getParamValue("shape",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),value=getParamValue("value",node2,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),num=getParamValue("num",node2,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node2,tensorMap,context),numSamples=getParamValue("numSamples",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node2,tensorMap,context),depth=getParamValue("depth",node2,tensorMap,context),onValue=getParamValue("onValue",node2,tensorMap,context),offValue=getParamValue("offValue",node2,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node2,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node2,tensorMap,context),getParamValue("minval",node2,tensorMap,context),getParamValue("maxval",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"Range":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),step4=getParamValue("step",node2,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node2,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node2,tensorMap,context),mean4=getParamValue("mean",node2,tensorMap,context),stdDev=getParamValue("stdDev",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node2,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function nmsParams(node2,tensorMap,context){let boxes=getParamValue("boxes",node2,tensorMap,context),scores=getParamValue("scores",node2,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node2,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node2,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node2,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node2,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node2,tensorMap,context)=>{switch(node2.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node2,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node2,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node2,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context));default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp7=(node2,tensorMap,context)=>{switch(node2.op){case"TopKV2":{let x=getParamValue("x",node2,tensorMap,context),k=getParamValue("k",node2,tensorMap,context),sorted=getParamValue("sorted",node2,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node2,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp8=(node2,tensorMap,context)=>{switch(node2.op){case"Const":return tensorMap[node2.name];case"PlaceholderWithDefault":let def=getParamValue("default",node2,tensorMap,context);return[getTensor(node2.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node2.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data2=getParamValue("x",node2,tensorMap,context);return[cloneTensor(data2)]}case"IdentityN":return getParamValue("x",node2,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node2,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node2,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node2,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node2,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node2,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context),message=getParamValue("message",node2,tensorMap,context),summarize=getParamValue("summarize",node2,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;i<data.length;i++)console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0,summarize));return[input2];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},HashTable=class{constructor(keyDType,valueDType){this.keyDType=keyDType,this.valueDType=valueDType,this.handle=scalar(0),this.tensorMap=new Map,keep(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i<keysLength;i++){let key=$keys[i],value=$values[i];keep(value),this.tensorMap.set(key,value)}return this.handle})}async find(keys,defaultValue){this.checkKeyAndValueTensor(keys,defaultValue);let $keys=await keys.data();return tidy(()=>{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node2,tensorMap,context,resourceManager)=>{switch(node2.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node2,tensorMap,context),valueDType=getParamValue("valueDType",node2,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node2.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp10=(node2,tensorMap,context)=>{switch(node2.op){case"ResizeBilinear":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node2,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node2,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node2,tensorMap,context),boxes=getParamValue("boxes",node2,tensorMap,context),boxInd=getParamValue("boxInd",node2,tensorMap,context),cropSize=getParamValue("cropSize",node2,tensorMap,context),method=getParamValue("method",node2,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node2,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp11=(node2,tensorMap,context)=>{switch(node2.op){case"Equal":return[equal(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Less":return[less(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node2,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node2,tensorMap,context),getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp12=(node2,tensorMap,context)=>{switch(node2.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context),getParamValue("transposeA",node2,tensorMap,context),getParamValue("transposeB",node2,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node2,tensorMap,context),getParamValue("perm",node2,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node2,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node2,tensorMap,context),b:getParamValue("b",node2,tensorMap,context),transposeA:getParamValue("transposeA",node2,tensorMap,context),transposeB:getParamValue("transposeB",node2,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp13=(node2,tensorMap,context)=>{switch(node2.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node2,tensorMap,context),getParamValue("radius",node2,tensorMap,context),getParamValue("bias",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context),getParamValue("beta",node2,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node2,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node2,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node2,tensorMap,context),getParamValue("outputShape",node2,tensorMap,context),getParamValue("sparseValues",node2,tensorMap,context),getParamValue("defaultValue",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp14=(node2,tensorMap,context)=>{switch(node2.op){case"Max":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[max(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[mean(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[min(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[sum2(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[all(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[any(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMax(getParamValue("x",node2,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMin(getParamValue("x",node2,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[prod(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node2,tensorMap,context),exclusive=getParamValue("exclusive",node2,tensorMap,context),reverse5=getParamValue("reverse",node2,tensorMap,context);return[cumsum(getParamValue("x",node2,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node2,tensorMap,context),weights=getParamValue("weights",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node2,tensorMap,context),weights2=getParamValue("weights",node2,tensorMap,context),size2=getParamValue("size",node2,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node2,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp15=(node2,tensorMap,context)=>{switch(node2.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),inputs=getParamValue("tensors",node2,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node2,tensorMap,context),batchDims=getParamValue("batchDims",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node2,tensorMap,context),axis=[];for(let i=0;i<dims.length;i++)dims[i]&&axis.push(i);let input2=getParamValue("x",node2,tensorMap,context);return[reverse(input2,axis)]}case"ReverseV2":{let axis=getParamValue("axis",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context);return[reverse(input2,axis)]}case"Slice":{let begin=getParamValue("begin",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context);return[slice(getParamValue("x",node2,tensorMap,context),begin,size)]}case"StridedSlice":{let begin=getParamValue("begin",node2,tensorMap,context),end=getParamValue("end",node2,tensorMap,context),strides=getParamValue("strides",node2,tensorMap,context),beginMask=getParamValue("beginMask",node2,tensorMap,context),endMask=getParamValue("endMask",node2,tensorMap,context),ellipsisMask=getParamValue("ellipsisMask",node2,tensorMap,context),newAxisMask=getParamValue("newAxisMask",node2,tensorMap,context),shrinkAxisMask=getParamValue("shrinkAxisMask",node2,tensorMap,context),tensor2=getParamValue("x",node2,tensorMap,context);return[stridedSlice(tensor2,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)]}case"Pack":return tidy(()=>{let axis=getParamValue("axis",node2,tensorMap,context),tensors=getParamValue("tensors",node2,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node2,tensorMap,context),tensor2=getParamValue("tensor",node2,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node2,tensorMap,context);return[tile(getParamValue("x",node2,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node2,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node2,tensorMap,context),tensor2=getParamValue("x",node2,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context),shape=getParamValue("shape",node2,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node2,tensorMap,context),shape=getParamValue("outputShape",node2,tensorMap,context),sparseValues=getParamValue("sparseValues",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp16=(node2,tensorMap,context)=>{switch(node2.op){case"FFT":return[fft(getParamValue("x",node2,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node2,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node2,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp17=(node2,tensorMap,context)=>{switch(node2.op){case"Cast":return[cast(getParamValue("x",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node2,tensorMap,context);return[expandDims(getParamValue("x",node2,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node2,tensorMap,context);return[squeeze(getParamValue("x",node2,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("mode",node2,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("constantValue",node2,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),paddings=getParamValue("paddings",node2,tensorMap,context);return[spaceToBatchND(getParamValue("x",node2,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),crops=getParamValue("crops",node2,tensorMap,context);return[batchToSpaceND(getParamValue("x",node2,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node2,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function executeOp18(node2,tensorMap,context,resourceManager){let value=((node22,tensorMap2,context2)=>{switch(node22.category){case"arithmetic":return tidy(()=>executeOp(node22,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node22,tensorMap2,context2));case"control":return executeOp3(node22,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node22,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node22,tensorMap2,context2));case"dynamic":return executeOp6(node22,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node22,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node22,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node22,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node22,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node22,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node22,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node22,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node22,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node22,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node22,tensorMap2,context2));case"hash_table":return executeOp9(node22,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node22.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node22,tensorMap2,context2));throw TypeError(`Custom op ${node22.op} is not registered.`);default:throw TypeError(`Unknown op '${node22.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node2,tensorMap,context);return util_exports.isPromise(value)?value.then(data=>[].concat(data)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;i<this.contexts.length-1;i++){let contexts2=this.contexts.slice(0,this.contexts.length-i);names.push(this.contextIdforContexts(contexts2))}names.push(""),this._currentContextIds=names}contextIdforContexts(contexts2){return contexts2?contexts2.map(context=>context.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node2=>parseNodeName(node2.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node2=frontier.pop();if((isControlFlow(node2)||isDynamicShape(node2)||isHashTable(node2))&&dynamicNode==null&&(dynamicNode=node2,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node2.name),weightMap[node2.name]==null&&inputNodeNames.indexOf(node2.name)===-1&&initNodeNames.indexOf(node2.name)===-1){if(node2.inputs.length===0){missingInputs.push(node2.name);continue}node2.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node2=>{usedNodes.has(node2.name)&&frontier.push(node2)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node2=frontier.pop();seen.add(node2.name),weightMap[node2.name]||orderedNodes.push(node2),node2.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node2){return CONTROL_FLOW_OPS.indexOf(node2.op)>=0}function isDynamicShape(node2){return DYNAMIC_SHAPE_OPS.indexOf(node2.op)>=0}function isHashTable(node2){return HASH_TABLE_OPS.indexOf(node2.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node2=>node2.signatureKey||node2.name)}get outputNodes(){return this._outputs.map(node2=>{let name=node2.signatureKey||node2.name;return node2.defaultOutput?`${name}:${node2.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node2=>node2.name).sort(),sortedOutputs=outputs.map(node2=>node2.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;i<orderedNodes.length;i++){let node2=orderedNodes[i];if(!tensorsMap[node2.name]){let tensors=executeOp18(node2,tensorsMap,context,this._resourceManager);if(util_exports.isPromise(tensors))throw new Error(`The execution of the op '${node2.op}' returned a promise. Please use model.executeAsync() instead.`);tensorsMap[node2.name]=tensors,this.checkTensorForDisposal(node2.name,node2,tensorsMap,context,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount)}}return this.parent==null&&context.dispose(tensorsToKeep),outputs.map(name=>getTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node2,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node2.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node2.children.length)}),node2.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node2=>({node:node2,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node2=>!isControlFlow(node2)&&!getTensor(node2.name,tensorsMap,context)).map(node2=>node2.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node2,stack2,context,tensorMap,added,usedNodes){node2.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node2=this.graph.nodes[nodeName];if(node2.attrParams.shape&&node2.attrParams.shape.value){let shape=node2.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node2.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node2.attrParams.dtype&&node2.attrParams.dtype.value&&util_exports.assert(input2.dtype===node2.attrParams.dtype.value,()=>`The dtype of dict['${node2.name}'] provided in model.execute(dict) must be ${node2.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version3="2.8.3",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version4,webcam:()=>webcam,zip:()=>zip});var seedrandom3=__toModule2(require_seedrandom2()),seedrandom2=__toModule2(require_seedrandom2());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;i<len;i++)newData[i]=this.get(this.wrap(this.begin+i));this.data=newData,this.capacity=newCapacity,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=len}};GrowingRingBuffer.INITIAL_CAPACITY=32;function iteratorFromItems(items){return new ArrayIterator(items)}function iteratorFromFunction(func2){return new FunctionCallIterator(func2)}function iteratorFromConcatenated(baseIterators,baseErrorHandler){return new ChainedIterator(baseIterators,baseErrorHandler)}function iteratorFromZipped(iterators,mismatchMode=ZipMismatchMode.FAIL){return new ZipIterator(iterators,mismatchMode)}var LazyIterator=class{async toArray(){let result=[],x=await this.next();for(;!x.done;)result.push(x.value),x=await this.next();return result}async toArrayForTest(){let stream=this.prefetch(100),result=[],x=await stream.next();for(;!x.done;)result.push(x.value),x=await stream.next();return result}async resolveFully(){let x=await this.next();for(;!x.done;)x=await this.next()}async resolveWhile(predicate){let x=await this.next(),shouldContinue=predicate(x.value);for(;!x.done&&shouldContinue;)x=await this.next(),shouldContinue=predicate(x.value)}handleErrors(handler){return new ErrorHandlingLazyIterator(this,handler)}filter(predicate){return new FilterIterator(this,predicate)}map(transform){return new MapIterator(this,transform)}mapAsync(transform){return new AsyncMapIterator(this,transform)}serialMapAsync(transform){return new AsyncMapIterator(this,transform).serial()}flatmap(transform){return new FlatmapIterator(this,transform)}async forEachAsync(f){return this.map(f).resolveFully()}async serialForEach(f){return this.serialMapAsync(f).resolveWhile(x=>x===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let skipped=await this.upstream.next();if(skipped.done)return skipped;dispose(skipped.value)}return this.upstream.next()}},TakeIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length<this.batchSize;){let item=await this.upstream.next();if(item.done)return this.enableSmallLastBatch&&batch.length>0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom2.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is
       ${batchSize}`);let size;return this.size===Infinity||this.size==null?size=this.size:smallLastBatch?size=Math.ceil(this.size/batchSize):size=Math.floor(this.size/batchSize),datasetFromIteratorFn(async()=>(await base2.iterator()).columnMajorBatch(batchSize,smallLastBatch,deepBatchConcat),size)}concatenate(dataset){let base2=this,size;return this.size===Infinity||dataset.size===Infinity?size=Infinity:this.size!=null&&dataset.size!=null?size=this.size+dataset.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).concatenate(await dataset.iterator()),size)}filter(predicate){let base2=this,size;return this.size===Infinity?size=Infinity:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).filter(x=>tidy(()=>predicate(x))),size)}async forEachAsync(f){return(await this.iterator()).forEachAsync(f)}map(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).map(x=>tidy(()=>transform(x))),this.size)}mapAsync(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).mapAsync(transform),this.size)}prefetch(bufferSize){if(bufferSize==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).prefetch(bufferSize),this.size)}repeat(count2){let base2=this,size;return this.size!=null&&count2>0?size=this.size*count2:count2===0?size=0:this.size!=null&&(count2===void 0||count2<0)?size=Infinity:size=null,datasetFromIteratorFn(async()=>{let iteratorIterator=iteratorFromFunction(async()=>({value:await base2.iterator(),done:!1}));return iteratorFromConcatenated(iteratorIterator.take(count2))},size)}skip(count2){let base2=this,size;return this.size!=null&&count2>=0&&this.size>=count2?size=this.size-count2:this.size!=null&&(this.size<count2||count2===void 0||count2<0)?size=0:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).skip(count2),size)}shuffle(bufferSize,seed,reshuffleEachIteration=!0){if(bufferSize==null||bufferSize<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let base2=this,random=seedrandom3.alea(seed||util_exports.now().toString());return datasetFromIteratorFn(async()=>{let seed2=random.int32();return reshuffleEachIteration&&(seed2+=random.int32()),(await base2.iterator()).shuffle(bufferSize,seed2.toString())},this.size)}take(count2){let base2=this,size;return this.size!=null&&this.size>count2?size=count2:this.size!=null&&this.size<=count2?size=this.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).take(count2),size)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Dataset.MAX_BUFFER_SIZE=1e4;function datasetFromIteratorFn(iteratorFn,size=null){return new class extends Dataset{constructor(){super(...arguments);this.size=size}async iterator(){return iteratorFn()}}}function array(items){return datasetFromIteratorFn(async()=>iteratorFromItems(items),items.length)}function zip(datasets){if(!isIterable2(datasets))throw new Error("The argument to zip() must be an object or array.");let size;if(Array.isArray(datasets))for(let i=0;i<datasets.length;i++)size=size==null?datasets[i].size:Math.min(size,datasets[i].size);else if(datasets instanceof Object)for(let ds in datasets)size=size==null?datasets[ds].size:Math.min(size,datasets[ds].size);return datasetFromIteratorFn(async()=>{let streams=await deepMapAndAwaitAll(datasets,d=>{if(d instanceof Dataset)return{value:d.iterator(),recurse:!1};if(isIterable2(d))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return iteratorFromZipped(streams,ZipMismatchMode.SHORTEST)},size)}function deepBatchConcat(rows){if(rows===null)return null;let exampleRow=rows[0];return canTensorify(exampleRow)?{value:batchConcat(rows),recurse:!1}:{value:null,recurse:!0}}function batchConcat(arrays){if(arrays.length===0)throw new Error("Can't make a batch of zero elements.");return arrays[0]instanceof Tensor?stack(arrays):tensor(arrays)}var TextLineDataset=class extends Dataset{constructor(input2){super();this.input=input2}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i<this.fullColumnNames.length;i++){let key=this.fullColumnNames[i],config=this.columnConfigs?this.columnConfigs[key]:null;if(!(this.configuredColumnsOnly&&!config)){let value=values[i],parsedValue=null;if(value==="")if(config&&config.default!==void 0)parsedValue=config.default;else{if(config&&(config.required||config.isLabel))throw new Error(`Required column ${key} is empty in this line: ${line}`);parsedValue=void 0}else{let valueAsNum=Number(value);if(isNaN(valueAsNum))config&&config.dtype==="bool"?parsedValue=this.getBoolean(value):parsedValue=value;else if(!config||!config.dtype)parsedValue=valueAsNum;else switch(config.dtype){case"float32":parsedValue=valueAsNum;break;case"int32":parsedValue=Math.floor(valueAsNum);break;case"bool":parsedValue=this.getBoolean(value);break;default:parsedValue=valueAsNum}}config&&config.isLabel?labels[key]=parsedValue:features[key]=parsedValue}}return Object.keys(labels).length===0?features:{xs:features,ys:labels}}getBoolean(value){return value==="1"||value.toLowerCase()==="true"?1:0}parseRow(line,validateElementCount=!0){let result=[],readOffset=0,readLength=line.length,currentState=STATE_OUT;for(let i=0;i<readLength;i++)switch(currentState){case STATE_OUT:switch(line.charAt(i)){case CODE_QUOTE:readOffset=i+1,currentState=STATE_QUOTE;break;case this.delimiter:if(readOffset=i+1,this.delimiter===" "&&this.delimWhitespace)break;result.push(""),currentState=STATE_OUT;break;default:currentState=STATE_FIELD,readOffset=i;break}break;case STATE_FIELD:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i)),currentState=STATE_OUT,readOffset=i+1;break;default:}break;case STATE_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE_AFTER_QUOTE;break;default:}break;case STATE_QUOTE_AFTER_QUOTE:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i-1)),currentState=STATE_OUT,readOffset=i+1;break;case CODE_QUOTE:currentState=STATE_QUOTE;break;default:currentState=STATE_WITHIN_QUOTE_IN_QUOTE;break}break;case STATE_WITHIN_QUOTE_IN_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE;break;default:}break;default:}if(currentState===STATE_QUOTE_AFTER_QUOTE?result.push(line.substring(readOffset,readLength-1)):result.push(line.substring(readOffset)),validateElementCount&&result.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);return result}},MicrophoneIterator=class extends LazyIterator{constructor(microphoneConfig){super();this.microphoneConfig=microphoneConfig,this.isClosed=!1,this.fftSize=microphoneConfig.fftSize||1024;let fftSizeLog2=Math.log2(this.fftSize);if(this.fftSize<0||fftSizeLog2<4||fftSizeLog2>14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data,i)=>freqData.set(data,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data=fileReader.result;if(data instanceof ArrayBuffer&&(data=new Uint8Array(data)),!(data instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version4="2.8.2";function assertNotComplex(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(`
+`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i<this.fullColumnNames.length;i++){let key=this.fullColumnNames[i],config=this.columnConfigs?this.columnConfigs[key]:null;if(!(this.configuredColumnsOnly&&!config)){let value=values[i],parsedValue=null;if(value==="")if(config&&config.default!==void 0)parsedValue=config.default;else{if(config&&(config.required||config.isLabel))throw new Error(`Required column ${key} is empty in this line: ${line}`);parsedValue=void 0}else{let valueAsNum=Number(value);if(isNaN(valueAsNum))config&&config.dtype==="bool"?parsedValue=this.getBoolean(value):parsedValue=value;else if(!config||!config.dtype)parsedValue=valueAsNum;else switch(config.dtype){case"float32":parsedValue=valueAsNum;break;case"int32":parsedValue=Math.floor(valueAsNum);break;case"bool":parsedValue=this.getBoolean(value);break;default:parsedValue=valueAsNum}}config&&config.isLabel?labels[key]=parsedValue:features[key]=parsedValue}}return Object.keys(labels).length===0?features:{xs:features,ys:labels}}getBoolean(value){return value==="1"||value.toLowerCase()==="true"?1:0}parseRow(line,validateElementCount=!0){let result=[],readOffset=0,readLength=line.length,currentState=STATE_OUT;for(let i=0;i<readLength;i++)switch(currentState){case STATE_OUT:switch(line.charAt(i)){case CODE_QUOTE:readOffset=i+1,currentState=STATE_QUOTE;break;case this.delimiter:if(readOffset=i+1,this.delimiter===" "&&this.delimWhitespace)break;result.push(""),currentState=STATE_OUT;break;default:currentState=STATE_FIELD,readOffset=i;break}break;case STATE_FIELD:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i)),currentState=STATE_OUT,readOffset=i+1;break;default:}break;case STATE_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE_AFTER_QUOTE;break;default:}break;case STATE_QUOTE_AFTER_QUOTE:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i-1)),currentState=STATE_OUT,readOffset=i+1;break;case CODE_QUOTE:currentState=STATE_QUOTE;break;default:currentState=STATE_WITHIN_QUOTE_IN_QUOTE;break}break;case STATE_WITHIN_QUOTE_IN_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE;break;default:}break;default:}if(currentState===STATE_QUOTE_AFTER_QUOTE?result.push(line.substring(readOffset,readLength-1)):result.push(line.substring(readOffset)),validateElementCount&&result.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);return result}},MicrophoneIterator=class extends LazyIterator{constructor(microphoneConfig){super();this.microphoneConfig=microphoneConfig,this.isClosed=!1,this.fftSize=microphoneConfig.fftSize||1024;let fftSizeLog2=Math.log2(this.fftSize);if(this.fftSize<0||fftSizeLog2<4||fftSizeLog2>14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data,i)=>freqData.set(data,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data=fileReader.result;if(data instanceof ArrayBuffer&&(data=new Uint8Array(data)),!(data instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version4="2.8.3";function assertNotComplex(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(`
 ============================
 Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
-============================`));let dataId={};return this.data.set(dataId,{values,dtype,refCount:1}),dataId}makeTensorInfo(shape,dtype,values){let outId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));outId=this.write(encodedValues,shape,dtype)}else outId=this.write(values,shape,dtype);return{dataId:outId,shape,dtype}}incRef(dataId){let tensorData=this.data.get(dataId);tensorData.refCount++}decRef(dataId){if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--}}move(dataId,values,shape,dtype){this.data.set(dataId,{values,dtype,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{dtype,complexTensorInfos}=this.data.get(dataId);if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);return backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}return this.data.get(dataId).values}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}makeOutput(values,shape,dtype){let dataId=this.write(values,shape,dtype);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}disposeData(dataId){if(this.data.has(dataId)){let{complexTensorInfos}=this.data.get(dataId);complexTensorInfos!=null&&(this.disposeData(complexTensorInfos.real.dataId),this.disposeData(complexTensorInfos.imag.dataId)),this.data.delete(dataId)}}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--,tensorData.refCount<1&&this.disposeData(dataId)}}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(condition){assertNotComplex([condition],"where");let condVals=this.readSync(condition.dataId);return whereImpl2(condition.shape,condVals)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},shared_exports={};__export2(shared_exports,{addImpl:()=>addImpl,bincountImpl:()=>bincountImpl,bincountReduceImpl:()=>bincountReduceImpl,ceilImpl:()=>ceilImpl,concatImpl:()=>concatImpl,expImpl:()=>expImpl,expm1Impl:()=>expm1Impl,floorImpl:()=>floorImpl,gatherV2Impl:()=>gatherV2Impl,greaterImpl:()=>greaterImpl,lessImpl:()=>lessImpl,linSpaceImpl:()=>linSpaceImpl,logImpl:()=>logImpl,maxImpl:()=>maxImpl,maximumImpl:()=>maximumImpl,minimumImpl:()=>minimumImpl,multiplyImpl:()=>multiplyImpl,negImpl:()=>negImpl,notEqualImpl:()=>notEqualImpl,prodImpl:()=>prodImpl,rangeImpl:()=>rangeImpl,rsqrtImpl:()=>rsqrtImpl,simpleAbsImpl:()=>simpleAbsImpl,sliceImpl:()=>sliceImpl,squaredDifferenceImpl:()=>squaredDifferenceImpl,stridedSliceImpl:()=>stridedSliceImpl,subImpl:()=>subImpl,tileImpl:()=>tileImpl,topKImpl:()=>topKImpl,transposeImpl:()=>transposeImpl,uniqueImpl:()=>uniqueImpl});function simpleAbsImpl(vals){let resultValues=new Float32Array(vals.length);for(let i=0;i<vals.length;++i)resultValues[i]=Math.abs(vals[i]);return resultValues}var abs2=args=>{let{x}=args.inputs,cpuBackend=args.backend;assertNotComplex(x,"abs");let resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),values=cpuBackend.data.get(x.dataId).values;return resultValues=simpleAbsImpl(values),cpuBackend.makeOutput(resultValues,x.shape,"float32")},absConfig={kernelName:Abs,backendName:"cpu",kernelFunc:abs2};function createSimpleBinaryKernelImpl(op2){return(aShape,bShape,aVals,bVals,dtype)=>{let newShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultRank=newShape.length,resultStrides=util_exports.computeStrides(newShape),resultSize=util_exports.sizeFromShape(newShape),result=util_exports.getTypedArrayFromDType(dtype,resultSize),aRank=aShape.length,bRank=bShape.length,aStrides=util_exports.computeStrides(aShape),bStrides=util_exports.computeStrides(bShape),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,newShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<result.length;++i)result[i]=op2(aVals[i%aVals.length],bVals[i%bVals.length]);else for(let i=0;i<result.length;++i){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides);result[i]=op2(aVals[aIndex],bVals[bIndex])}return[result,newShape]}}function complex2(args){let{inputs,backend:backend2}=args,{real:real4,imag:imag4}=inputs,realVals=backend2.data.get(real4.dataId).values,imagVals=backend2.data.get(imag4.dataId).values,complexInfo=backend2.makeTensorInfo(real4.shape,"complex64"),complex4=backend2.data.get(complexInfo.dataId);return complex4.complexTensorInfos={real:backend2.makeTensorInfo(real4.shape,"float32",realVals),imag:backend2.makeTensorInfo(imag4.shape,"float32",imagVals)},complexInfo}var complexConfig={kernelName:Complex,backendName:"cpu",kernelFunc:complex2};function zeros3(backend2,shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros3(backend2,shape,"float32"),imag4=zeros3(backend2,shape,"float32");return complex2({inputs:{real:real4,imag:imag4},backend:backend2})}let values=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape),dtype);return backend2.makeTensorInfo(shape,dtype,values)}function identity2(args){let{inputs,backend:backend2}=args,{x}=inputs;return backend2.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig={kernelName:Identity,backendName:"cpu",kernelFunc:identity2};function real2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,real4=backend2.data.get(input2.dataId).complexTensorInfos.real,realVal=backend2.data.get(real4.dataId).values;return backend2.makeTensorInfo(real4.shape,real4.dtype,realVal)}var realConfig={kernelName:Real,backendName:"cpu",kernelFunc:real2};function cast3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity2({inputs:{x},backend:backend2});let zerosTensorInfo=zeros3(backend2,x.shape,x.dtype),floatX=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex2({inputs:{real:floatX,imag:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),result=cast3({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity2({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32"){let values=backend2.data.get(x.dataId).values,resultValues=Int32Array.from(values);return backend2.makeTensorInfo(x.shape,"int32",resultValues)}if(dtype==="bool"){let xVals=backend2.data.get(x.dataId).values,zero=util_exports.toTypedArray([0],x.dtype),[resultData,resultShape]=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0)(x.shape,[],xVals,zero,"bool");return backend2.makeTensorInfo(resultShape,"bool",resultData)}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig={kernelName:Cast,backendName:"cpu",kernelFunc:cast3};function binaryKernelFunc(name,simpleImpl,complexImpl,dtype){return complexImpl==null?({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;assertNotComplex([a,b],name);let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}:({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;if(a.dtype==="complex64"||b.dtype==="complex64"){let $aComplex=cast3({inputs:{x:a},backend:cpuBackend,attrs:{dtype:"complex64"}}),$aComplexVals=cpuBackend.data.get($aComplex.dataId),aReal=$aComplexVals.complexTensorInfos.real,aImag=$aComplexVals.complexTensorInfos.imag,aRealVals=cpuBackend.data.get(aReal.dataId).values,aImagVals=cpuBackend.data.get(aImag.dataId).values,$bComplex=cast3({inputs:{x:b},backend:cpuBackend,attrs:{dtype:"complex64"}}),$bComplexVals=cpuBackend.data.get($bComplex.dataId),bReal=$bComplexVals.complexTensorInfos.real,bImag=$bComplexVals.complexTensorInfos.imag,bRealVals=cpuBackend.data.get(bReal.dataId).values,bImagVals=cpuBackend.data.get(bImag.dataId).values,[resultRealData,resultImagData,resultShape]=complexImpl(a.shape,b.shape,aRealVals,aImagVals,bRealVals,bImagVals),resultReal=cpuBackend.makeTensorInfo(resultShape,"float32",resultRealData),resultImag=cpuBackend.makeTensorInfo(resultShape,"float32",resultImagData),result=complex2({inputs:{real:resultReal,imag:resultImag},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($aComplex),cpuBackend.disposeIntermediateTensorInfo($bComplex),cpuBackend.disposeIntermediateTensorInfo(resultReal),cpuBackend.disposeIntermediateTensorInfo(resultImag),result}else{let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}}}function createComplexBinaryKernelImpl(op2){return(aShape,bShape,aRealVals,aImagVals,bRealVals,bImagVals)=>{let resultShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultSize=util_exports.sizeFromShape(resultShape),resultRank=resultShape.length,resultStrides=util_exports.computeStrides(resultShape),resultRealVals=util_exports.getTypedArrayFromDType("float32",resultSize),resultImagVals=util_exports.getTypedArrayFromDType("float32",resultSize),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,resultShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,resultShape),aVals=backend_util_exports.mergeRealAndImagArrays(aRealVals,aImagVals),bVals=backend_util_exports.mergeRealAndImagArrays(bRealVals,bImagVals),aRank=aShape.length,aStrides=util_exports.computeStrides(aShape),bRank=bShape.length,bStrides=util_exports.computeStrides(bShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resultRealVals.length;i++){let aIdx=i%aVals.length,bIdx=i%bVals.length,result=op2(aVals[aIdx*2],aVals[aIdx*2+1],bVals[bIdx*2],bVals[bIdx*2+1]);resultRealVals[i]=result.real,resultImagVals[i]=result.imag}else for(let i=0;i<resultRealVals.length;i++){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides),opResult=op2(aVals[aIndex*2],aVals[aIndex*2+1],bVals[bIndex*2],bVals[bIndex*2+1]);resultRealVals[i]=opResult.real,resultImagVals[i]=opResult.imag}return[resultRealVals,resultImagVals,resultShape]}}var addImpl=createSimpleBinaryKernelImpl((a,b)=>a+b),addComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal+bReal,imag:aImag+bImag})),add4=binaryKernelFunc(Add,addImpl,addComplexImpl),addConfig={kernelName:Add,backendName:"cpu",kernelFunc:add4};function bincountImpl(xVals,weightsVals,weightsDtype,weightsShape,size){let weightsSize=util_exports.sizeFromShape(weightsShape),outVals=util_exports.makeZerosTypedArray(size,weightsDtype);for(let i=0;i<xVals.length;i++){let value=xVals[i];if(value<0)throw new Error("Input x must be non-negative!");value>=size||(weightsSize>0?outVals[value]+=weightsVals[i]:outVals[value]+=1)}return outVals}function bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput=!1){let numRows=xBuf.shape[0],numCols=xBuf.shape[1],outBuf=buffer([numRows,size],weightsBuf.dtype);for(let i=0;i<numRows;i++)for(let j=0;j<numCols;j++){let value=xBuf.get(i,j);if(value<0)throw new Error("Input x must be non-negative!");value>=size||(binaryOutput?outBuf.set(1,i,value):weightsBuf.size>0?outBuf.set(outBuf.get(i,value)+weightsBuf.get(i,j),i,value):outBuf.set(outBuf.get(i,value)+1,i,value))}return outBuf}function createSimpleUnaryImpl(op2){return(values,dtype,attrs)=>{let newValues=util_exports.getTypedArrayFromDType(dtype,values.length);for(let i=0;i<values.length;++i)newValues[i]=op2(values[i],attrs);return newValues}}function unaryKernelFunc(name,op2,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),$dtype=dtype||x.dtype,newValues=util_exports.getArrayFromDType($dtype,xSize);for(let i=0;i<xSize;++i)newValues[i]=op2(values[i],attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}function unaryKernelFuncFromImpl(name,unaryImpl,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,$dtype=dtype||x.dtype,newValues=unaryImpl(values,$dtype,attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}var ceilImpl=createSimpleUnaryImpl(xi=>Math.ceil(xi)),ceil2=unaryKernelFuncFromImpl(Ceil,ceilImpl),ceilConfig={kernelName:Ceil,backendName:"cpu",kernelFunc:ceil2};function concatImpl(inputs,outShape,dtype,simplyConcat){let outVals=util_exports.getArrayFromDType(dtype,util_exports.sizeFromShape(outShape));if(simplyConcat&&dtype!=="string"){let offset=0;inputs.forEach(input2=>{let size=util_exports.sizeFromShape(input2.shape);outVals.set(input2.vals,offset),offset+=size})}else{let colOffset=0;inputs.forEach(input2=>{let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(input2.vals):input2.vals,tIdx=0;for(let row=0;row<input2.shape[0];++row){let resIdx=row*outShape[1]+colOffset;for(let col=0;col<input2.shape[1];++col)outVals[resIdx+col]=decodedData[tIdx++]}colOffset+=input2.shape[1]})}return outVals}var expImpl=createSimpleUnaryImpl(xi=>Math.exp(xi)),exp2=unaryKernelFuncFromImpl(Exp,expImpl),expConfig={kernelName:Exp,backendName:"cpu",kernelFunc:exp2},expm1Impl=createSimpleUnaryImpl(xi=>Math.expm1(xi)),expm12=unaryKernelFuncFromImpl(Expm1,expm1Impl),expm1Config={kernelName:Expm1,backendName:"cpu",kernelFunc:expm12},floorImpl=createSimpleUnaryImpl(xi=>Math.floor(xi)),floor2=unaryKernelFuncFromImpl(Floor,floorImpl),floorConfig={kernelName:Floor,backendName:"cpu",kernelFunc:floor2};function gatherV2Impl(xBuf,indicesBuf,flattenOutputShape){let outBuf=buffer(flattenOutputShape,xBuf.dtype);for(let i=0;i<outBuf.size;++i){let originalLoc=outBuf.indexToLoc(i).slice(),batchIdx=originalLoc[0],indicesIdx=originalLoc[2],indicesIndex=indicesBuf.locToIndex([batchIdx,indicesIdx]);originalLoc[2]=indicesBuf.values[indicesIndex];let originalIndex=xBuf.locToIndex(originalLoc);outBuf.values[i]=xBuf.values[originalIndex]}return outBuf}var greaterImpl=createSimpleBinaryKernelImpl((a,b)=>a>b?1:0),greater3=binaryKernelFunc(Greater,greaterImpl,null,"bool"),greaterConfig={kernelName:Greater,backendName:"cpu",kernelFunc:greater3},lessImpl=createSimpleBinaryKernelImpl((a,b)=>a<b?1:0),less3=binaryKernelFunc(Less,lessImpl,null,"bool"),lessConfig={kernelName:Less,backendName:"cpu",kernelFunc:less3};function linSpaceImpl(start,stop,num){let step4=(stop-start)/(num-1),values=util_exports.makeZerosTypedArray(num,"float32");values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var logImpl=createSimpleUnaryImpl(xi=>Math.log(xi)),log3=unaryKernelFuncFromImpl(Log,logImpl),logConfig={kernelName:Log,backendName:"cpu",kernelFunc:log3};function maxImpl(aVals,reduceSize,outShape,dtype){let vals=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(outShape));for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value)}vals[i]=max6}return vals}var maximumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.max(aValue,bValue)),maximum3=binaryKernelFunc(Maximum,maximumImpl),maximumConfig={kernelName:Maximum,backendName:"cpu",kernelFunc:maximum3},minimumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.min(aValue,bValue)),minimum3=binaryKernelFunc(Minimum,minimumImpl),minimumConfig={kernelName:Minimum,backendName:"cpu",kernelFunc:minimum3},multiplyImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue*bValue),multiplyComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal*bReal-aImag*bImag,imag:aReal*bImag+aImag*bReal})),multiply2=binaryKernelFunc(Multiply,multiplyImpl,multiplyComplexImpl),multiplyConfig={kernelName:Multiply,backendName:"cpu",kernelFunc:multiply2};function negImpl(xVals,xShape,xDtype){let minusOne=util_exports.createScalarValue(-1,xDtype);return multiplyImpl([],xShape,minusOne,xVals,xDtype)}function neg2(args){let{inputs,backend:backend2}=args,{x}=inputs;assertNotComplex(x,"neg");let xVals=backend2.data.get(x.dataId).values,[res,newShape]=negImpl(xVals,x.shape,x.dtype);return backend2.makeTensorInfo(newShape,x.dtype,res)}var negConfig={kernelName:Neg,backendName:"cpu",kernelFunc:neg2},notEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0),notEqual2=binaryKernelFunc(NotEqual,notEqualImpl,null,"bool"),notEqualConfig={kernelName:NotEqual,backendName:"cpu",kernelFunc:notEqual2};function transposeImpl(xVals,xShape,dtype,perm,newShape){let xRank=xShape.length,xSize=util_exports.sizeFromShape(xShape),xStrides=util_exports.computeStrides(xShape),newStrides=util_exports.computeStrides(newShape),result=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(newShape));for(let i=0;i<xSize;++i){let loc=util_exports.indexToLoc(i,xRank,xStrides),newLoc=new Array(loc.length);for(let i2=0;i2<newLoc.length;i2++)newLoc[i2]=loc[perm[i2]];let newIndex=util_exports.locToIndex(newLoc,xRank,newStrides);result[newIndex]=xVals[i]}return result}function transpose2(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{perm}=attrs;assertNotComplex(x,"transpose");let xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let values=backend2.data.get(x.dataId).values,result=transposeImpl(values,x.shape,x.dtype,perm,newShape);return{dataId:backend2.write(result,newShape,x.dtype),shape:newShape,dtype:x.dtype}}var transposeConfig={kernelName:Transpose,backendName:"cpu",kernelFunc:transpose2};function prodImpl(xShape,xDtype,xVals,reductionAxes){let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,reductionAxes),outDtype=upcastType(xDtype,"int32"),outVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),outDtype),reduceSize=util_exports.sizeFromShape(reduceShape);for(let i=0;i<outVals.length;++i){let offset=i*reduceSize,prod5=1;for(let j=0;j<reduceSize;++j)prod5*=xVals[offset+j];outVals[i]=prod5}return{outVals,outShape,outDtype}}function prod2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"prod");let xRank=x.shape.length,axes=util_exports.parseAxisParam(axis,x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=x,intermediateTensorInfos=[];permutation!=null&&(permutedX=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}),intermediateTensorInfos.push(permutedX),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank));let xVals=backend2.data.get(permutedX.dataId).values,{outVals,outShape,outDtype}=prodImpl(permutedX.shape,permutedX.dtype,xVals,reductionAxes),resultShape=outShape;return keepDims&&(resultShape=backend_util_exports.expandShapeToKeepDim(outShape,axes)),intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(resultShape,outDtype,outVals)}var prodConfig={kernelName:Prod,backendName:"cpu",kernelFunc:prod2};function rangeImpl(start,stop,step4,dtype){let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step4<0,decreasingRangePositiveStep=stop<start&&step4>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return util_exports.makeZerosTypedArray(0,dtype);let numElements=Math.abs(Math.ceil((stop-start)/step4)),values=util_exports.makeZerosTypedArray(numElements,dtype);stop<start&&step4===1&&(step4=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var rsqrtImpl=createSimpleUnaryImpl(xi=>1/Math.sqrt(xi)),rsqrt2=unaryKernelFuncFromImpl(Rsqrt,rsqrtImpl),rsqrtConfig={kernelName:Rsqrt,backendName:"cpu",kernelFunc:rsqrt2};function sliceImpl(vals,begin,size,shape,dtype){let isContinous=slice_util_exports.isSliceContinous(shape,begin,size),length=util_exports.sizeFromShape(size),xStrides=util_exports.computeStrides(shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin,xStrides);return dtype==="string"?vals.slice(flatOffset,flatOffset+length):vals.subarray(flatOffset,flatOffset+length)}let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(vals):vals,inBuf=buffer(shape,dtype,decodedData),outBuf=buffer(size,dtype);for(let i=0;i<outBuf.size;++i){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.map((idx,j)=>idx+begin[j]);outBuf.set(inBuf.get(...inLoc),...outLoc)}return dtype==="string"?backend_util_exports.fromStringArrayToUint8(outBuf.values):outBuf.values}function slice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs;assertNotComplex(x,"slice");let[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);slice_util_exports.assertParamsValid(x,$begin,$size);let vals=backend2.data.get(x.dataId).values,outVals=sliceImpl(vals,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outVals)}var sliceConfig={kernelName:Slice,backendName:"cpu",kernelFunc:slice2},squaredDifferenceImpl=createSimpleBinaryKernelImpl((a,b)=>{let diff=a-b;return diff*diff}),squaredDifference2=binaryKernelFunc(SquaredDifference,squaredDifferenceImpl),squaredDifferenceConfig={kernelName:SquaredDifference,backendName:"cpu",kernelFunc:squaredDifference2};function stridedSliceImpl(outShape,xBuf,strides,begin){let outBuf=buffer(outShape,xBuf.dtype);for(let i=0;i<outBuf.size;i++){let loc=outBuf.indexToLoc(i),newLoc=new Array(loc.length);for(let j=0;j<newLoc.length;j++)newLoc[j]=loc[j]*strides[j]+begin[j];outBuf.set(xBuf.get(...newLoc),...loc)}return outBuf}var subImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue-bValue),subComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal-bReal,imag:aImag-bImag})),sub2=binaryKernelFunc(Sub,subImpl,subComplexImpl),subConfig={kernelName:Sub,backendName:"cpu",kernelFunc:sub2};function tileImpl(xBuf,reps){let newShape=new Array(xBuf.rank);for(let i=0;i<newShape.length;i++)newShape[i]=xBuf.shape[i]*reps[i];let result=buffer(newShape,xBuf.dtype);for(let i=0;i<result.values.length;++i){let newLoc=result.indexToLoc(i),originalLoc=new Array(xBuf.rank);for(let j=0;j<originalLoc.length;j++)originalLoc[j]=newLoc[j]%xBuf.shape[j];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result}function topKImpl(x,xShape,xDtype,k,sorted){let lastDim=xShape[xShape.length-1],[batch,size]=[x.length/lastDim,lastDim],allTopKVals=util_exports.getTypedArrayFromDType(xDtype,batch*k),allTopKIndices=util_exports.getTypedArrayFromDType("int32",batch*k);for(let b=0;b<batch;b++){let offset=b*size,vals=x.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value);let outOffset=b*k,topKVals=allTopKVals.subarray(outOffset,outOffset+k),topKIndices=allTopKIndices.subarray(outOffset,outOffset+k);for(let i=0;i<k;i++)topKVals[i]=valAndInd[i].value,topKIndices[i]=valAndInd[i].index}let outputShape=xShape.slice();return outputShape[outputShape.length-1]=k,[buffer(outputShape,xDtype,allTopKVals),buffer(outputShape,"int32",allTopKIndices)]}function uniqueImpl(values,axis,shape,dtype){let $axis=util_exports.parseAxisParam(axis,shape)[0],newShape=[1,shape[0],1];for(let i=0;i<$axis;i++)newShape[0]*=shape[i];newShape[1]=shape[$axis];for(let i=$axis+1;i<shape.length;i++)newShape[2]*=shape[i];let uniqueElements={},indices=new Int32Array(shape[$axis]),inputBuffer=new TensorBuffer(newShape,dtype,values),uniqueIndices=[],is1DTensor=newShape[0]===1&&newShape[2]===1;for(let i=0;i<shape[$axis];i++){let element;if(is1DTensor)element=values[i].toString();else{let axisValues=[];for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)axisValues.push(inputBuffer.get(m,i,n));element=axisValues.join(",")}if(uniqueElements[element]!==void 0)indices[i]=uniqueElements[element];else{let uniqueIndex=Object.keys(uniqueElements).length;uniqueElements[element]=uniqueIndex,indices[i]=uniqueIndex,uniqueIndices.push(i)}}let outputTmpShape=newShape.slice();outputTmpShape[1]=Object.keys(uniqueElements).length;let outputBuffer=new TensorBuffer(outputTmpShape,dtype);uniqueIndices.forEach((uniqueElementIndex,i)=>{for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)outputBuffer.set(inputBuffer.get(m,uniqueElementIndex,n),m,i,n)});let outputShape=shape.slice();return outputShape[$axis]=outputTmpShape[1],{outputValues:outputBuffer.values,outputShape,indices}}var version5="2.8.2";registerBackend("cpu",()=>new MathBackendCPU,1);var elu4=unaryKernelFunc(Elu,xi=>xi>=0?xi:Math.exp(xi)-1),eluConfig={kernelName:Elu,backendName:"cpu",kernelFunc:elu4};function leakyRelu2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{alpha}=attrs;assertNotComplex([x],"leakyRelu");let xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outVals=util_exports.getTypedArrayFromDType("float32",xSize);for(let i=0;i<xVals.length;i++)outVals[i]=xVals[i]<0?alpha*xVals[i]:xVals[i];return backend2.makeTensorInfo(x.shape,"float32",outVals)}var leakyReluConfig={kernelName:LeakyRelu,backendName:"cpu",kernelFunc:leakyRelu2},preluImpl=createSimpleBinaryKernelImpl((xValue,aValue)=>xValue<0?aValue*xValue:xValue);function prelu3(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs;assertNotComplex([x,alpha],"prelu");let aVals=backend2.data.get(x.dataId).values,bVals=backend2.data.get(alpha.dataId).values,[resultData,resultShape]=preluImpl(x.shape,alpha.shape,aVals,bVals,x.dtype);return backend2.makeTensorInfo(resultShape,x.dtype,resultData)}var preluConfig={kernelName:Prelu,backendName:"cpu",kernelFunc:prelu3},relu2=unaryKernelFunc(Relu,xi=>Math.max(0,xi)),reluConfig={kernelName:Relu,backendName:"cpu",kernelFunc:relu2},relu62=unaryKernelFunc(Relu6,xi=>Math.min(Math.max(0,xi),6)),relu6Config={kernelName:Relu6,backendName:"cpu",kernelFunc:relu62};function applyActivation2(backend2,x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return identity2({inputs:{x},backend:backend2});if(activation2==="relu")return relu2({inputs:{x},backend:backend2});if(activation2==="elu")return elu4({inputs:{x},backend:backend2});if(activation2==="relu6")return relu62({inputs:{x},backend:backend2});if(activation2==="prelu")return prelu3({inputs:{x,alpha:preluActivationWeights},backend:backend2});if(activation2==="leakyrelu")return leakyRelu2({inputs:{x},backend:backend2,attrs:{alpha:leakyreluAlpha}});throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`)}function reshape3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`),backend2.incRef(x.dataId);let xData=backend2.data.get(x.dataId);if(xData.complexTensorInfos!=null){let real4=xData.complexTensorInfos.real,imag4=xData.complexTensorInfos.imag;real4.shape=$shape,imag4.shape=$shape}return{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig={kernelName:Reshape,backendName:"cpu",kernelFunc:reshape3};function batchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;assertNotComplex([a,b],"matMul");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape3({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape3({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),sharedDim=transposeA?a3d.shape[1]:a3d.shape[2],leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),a3dValues=backend2.data.get(a3d.dataId).values,b3dValues=backend2.data.get(b3d.dataId).values,a3dStrides=util_exports.computeStrides(a3d.shape),b3dStrides=util_exports.computeStrides(b3d.shape),[aBatch,aOuterStep,aInnerStep]=transposeA?[a3dStrides[0],1,a3dStrides[1]]:[a3dStrides[0],a3dStrides[1],1],[bInnerStep,bOuterStep,bBatch]=transposeB?[1,b3dStrides[1],b3dStrides[0]]:[b3dStrides[1],1,b3dStrides[0]],size=leftDim*rightDim,result=buffer([batchDim,leftDim,rightDim],a3d.dtype),resVals=result.values,blockSize=backend2.blockSize;for(let bi=0;bi<batchDim;bi++)for(let i0=0;i0<leftDim;i0+=blockSize)for(let j0=0;j0<rightDim;j0+=blockSize)for(let k0=0;k0<sharedDim;k0+=blockSize){let iBlock=Math.min(i0+blockSize,leftDim),jBlock=Math.min(j0+blockSize,rightDim),kBlock=Math.min(k0+blockSize,sharedDim);for(let i=i0;i<iBlock;i++)for(let j=j0;j<jBlock;j++){let sum6=0;for(let k=k0;k<kBlock;k++){let batchOffsetA=Math.min(bi,batchDimA-1)*aBatch,batchOffsetB=Math.min(bi,batchDimB-1)*bBatch,aVal=a3dValues[batchOffsetA+i*aOuterStep+k*aInnerStep],bVal=b3dValues[k*bInnerStep+j*bOuterStep+batchOffsetB];sum6+=aVal*bVal}resVals[bi*size+(i*rightDim+j)]+=sum6}}return backend2.disposeIntermediateTensorInfo(a3d),backend2.disposeIntermediateTensorInfo(b3d),backend2.makeTensorInfo(outShape,result.dtype,result.values)}var batchMatMulConfig={kernelName:BatchMatMul,backendName:"cpu",kernelFunc:batchMatMul};function _fusedMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs,{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,current,addRes,activationRes,intermediates=[];current=batchMatMul({inputs:{a,b},attrs:{transposeA,transposeB},backend:backend2}),bias&&(addRes=add4({inputs:{a:current,b:bias},backend:backend2}),intermediates.push(current),current=addRes),activation2&&(activationRes=applyActivation2(backend2,current,activation2,preluActivationWeights,leakyreluAlpha),intermediates.push(current),current=activationRes);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return current}var _fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"cpu",kernelFunc:_fusedMatMul},acos2=unaryKernelFunc(Acos,xi=>Math.acos(xi)),acosConfig={kernelName:Acos,backendName:"cpu",kernelFunc:acos2},acosh2=unaryKernelFunc(Acosh,xi=>Math.acosh(xi)),acoshConfig={kernelName:Acosh,backendName:"cpu",kernelFunc:acosh2};function addN2(args){let{inputs,backend:backend2}=args,tensors=inputs;assertNotComplex(inputs,"addN");let vals=tensors.map(t=>backend2.data.get(t.dataId).values),outBuf=buffer(tensors[0].shape,tensors[0].dtype),outVals=outBuf.values;for(let i=0;i<tensors.length;i++){let currVals=vals[i];for(let j=0;j<outVals.length;j++)outVals[j]+=currVals[j]}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var addNConfig={kernelName:AddN,backendName:"cpu",kernelFunc:addN2};function all2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"all");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("all",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,all4=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];all4=all4&&value}vals[i]=all4}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var allConfig={kernelName:All,backendName:"cpu",kernelFunc:all2};function any2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"any");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("any",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,anyVal=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];anyVal=anyVal||value}vals[i]=anyVal}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var anyConfig={kernelName:Any,backendName:"cpu",kernelFunc:any2};function argMax2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMax");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMax",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset],maxIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value,maxIndex=j)}vals[i]=maxIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMaxConfig={kernelName:ArgMax,backendName:"cpu",kernelFunc:argMax2};function argMin2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMin");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMin",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset],minIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value,minIndex=j)}vals[i]=minIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMinConfig={kernelName:ArgMin,backendName:"cpu",kernelFunc:argMin2},asin2=unaryKernelFunc(Asin,xi=>Math.asin(xi)),asinConfig={kernelName:Asin,backendName:"cpu",kernelFunc:asin2},asinh2=unaryKernelFunc(Asinh,xi=>Math.asinh(xi)),asinhConfig={kernelName:Asinh,backendName:"cpu",kernelFunc:asinh2},atan3=unaryKernelFunc(Atan,xi=>Math.atan(xi)),atanConfig={kernelName:Atan,backendName:"cpu",kernelFunc:atan3},atan2Impl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.atan2(aValue,bValue)),atan22=binaryKernelFunc(Atan2,atan2Impl),atan2Config={kernelName:Atan2,backendName:"cpu",kernelFunc:atan22},atanh2=unaryKernelFunc(Atanh,xi=>Math.atanh(xi)),atanhConfig={kernelName:Atanh,backendName:"cpu",kernelFunc:atanh2};function pool2(xValues,xShape,dtype,strides,convInfo,poolType){let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3],outputRowStrides=convInfo.outShape[2]*convInfo.outShape[3],outputColStrides=convInfo.outShape[3];for(let b=0;b<convInfo.batchSize;++b){let outputBatchOffset=b*outputBatchStrides,inputBatchOffset=b*strides[0];for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=Math.max(0,xRCorner),xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner),outputRowOffset=outputBatchOffset+yR*outputRowStrides;for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=Math.max(0,xCCorner),xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),minMaxValue=initialValue,avgValue=0,count2=0;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let xROffset=inputBatchOffset+xR*strides[1];for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let xCOffset=xROffset+xC*strides[2],pixel=xValues[xCOffset+d];poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++)}if(isNaN(minMaxValue))break}let outputOffset=outputRowOffset+yC*outputColStrides+d;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}return output}function maxPoolPositions(xValues,xShape,dtype,convInfo,flattenPositions=!1,includeBatchInIndex=!1){let maxPositions=buffer(convInfo.outShape,"int32"),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=buffer(xShape,dtype,xValues);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=xRCorner;for(;xRMin<0;)xRMin+=dilationHeight;let xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner);for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=xCCorner;for(;xCMin<0;)xCMin+=dilationWidth;let xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let wR=xR-xRCorner;for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let wC=xC-xCCorner,pixel=xBuf.get(b,xR,xC,d);pixel>maxValue&&(maxValue=pixel,flattenPositions?maxPosition=includeBatchInIndex?((b*convInfo.inHeight+xR)*convInfo.inWidth+xC)*convInfo.inChannels+d:(xR*convInfo.inWidth+xC)*convInfo.inChannels+d:maxPosition=wR*effectiveFilterWidth+wC)}}maxPositions.set(maxPosition,b,yR,yC,d)}}return maxPositions}function pool3d2(xValues,xShape,dtype,strides,convInfo,poolType){let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputDepthStrides=convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputRowStrides=convInfo.outShape[3]*convInfo.outShape[4],outputColStrides=convInfo.outShape[4];for(let batch=0;batch<convInfo.batchSize;++batch){let outputBatchOffset=batch*outputBatchStrides,inputBatchOffset=batch*strides[0];for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner),outputDepthOffset=outputBatchOffset+yDepth*outputDepthStrides;for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner),outputRowOffset=outputDepthOffset+yRow*outputRowStrides;for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),outputColOffset=outputRowOffset+yCol*outputColStrides,minMaxValue=initialValue,avgValue=0,count2=0;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let xDepthOffset=inputBatchOffset+xDepth*strides[1];for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let xRowOffset=xDepthOffset+xRow*strides[2];for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let xColOffset=xRowOffset+xCol*strides[3],pixel=xValues[xColOffset+channel];if(poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++),isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}let outputOffset=outputColOffset+channel;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}}return output}function maxPool3dPositions(xBuf,convInfo){let maxPositions=buffer(convInfo.outShape,"int32"),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner);for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner);for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let wDepth=xDepth-xDepthCorner;for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let wRow=xRow-xRowCorner;for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let wCol=xCol-xColCorner,pixel=xBuf.get(batch,xDepth,xRow,xCol,channel);pixel>=maxValue&&(maxValue=pixel,maxPosition=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterHeight+wCol)}}}maxPositions.set(maxPosition,batch,yDepth,yRow,yCol,channel)}}}return maxPositions}function avgPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"avgPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"avg");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var avgPoolConfig={kernelName:AvgPool,backendName:"cpu",kernelFunc:avgPool2};function avgPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"avgPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"avg");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var avgPool3DConfig={kernelName:AvgPool3D,backendName:"cpu",kernelFunc:avgPool3D};function avgPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"avgPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),avgMultiplier=1/(filterDepth*filterHeight*filterWidth),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol||(dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel))}}}dx.set(dotProd*avgMultiplier,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPool3DGradConfig2={kernelName:AvgPool3DGrad,backendName:"cpu",kernelFunc:avgPool3DGrad};function avgPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex([dy,input2],"avgPoolGrad");let{filterSize,strides,pad:pad3}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterHeight*filterWidth),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC||(dotProd+=dyBuf.get(b,dyR,dyC,d))}}dx.set(dotProd*avgMultiplier,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPoolGradConfig2={kernelName:AvgPoolGrad,backendName:"cpu",kernelFunc:avgPoolGrad2};function batchNorm2(args){let{inputs,backend:backend2,attrs}=args,{x,scale:scale22,offset,mean:mean4,variance}=inputs;util_exports.assert(mean4.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean4.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale22==null||mean4.shape.length===scale22.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),assertNotComplex([x,mean4,variance,scale22,offset],"batchNorm");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let xVals=backend2.data.get(x.dataId).values,mVals=backend2.data.get(mean4.dataId).values,varVals=backend2.data.get(variance.dataId).values,sVals=scale22?backend2.data.get(scale22.dataId).values:new Float32Array([1]),offVals=offset?backend2.data.get(offset.dataId).values:new Float32Array([0]),outVals=new Float32Array(xVals.length),offValsLength=offVals.length,sValsLength=sVals.length,varValsLength=varVals.length,mValsLength=mVals.length,offi=0,mi=0,si=0,vi=0;for(let i=0;i<xVals.length;++i)outVals[i]=offVals[offi++]+(xVals[i]-mVals[mi++])*sVals[si++]/Math.sqrt(varVals[vi++]+varianceEpsilon),offi>=offValsLength&&(offi=0),mi>=mValsLength&&(mi=0),si>=sValsLength&&(si=0),vi>=varValsLength&&(vi=0);return backend2.makeTensorInfo(x.shape,x.dtype,outVals)}var batchNormConfig={kernelName:FusedBatchNorm,backendName:"cpu",kernelFunc:batchNorm2};function batchToSpaceND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;assertNotComplex([x],"batchToSpaceND");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),xReshaped=reshape3({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),xTransposed=transpose2({inputs:{x:xReshaped},backend:backend2,attrs:{perm:permuted}}),xTransposedReshaped=reshape3({inputs:{x:xTransposed},backend:backend2,attrs:{shape:reshapedPermuted}}),result=slice2({inputs:{x:xTransposedReshaped},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return backend2.disposeIntermediateTensorInfo(xReshaped),backend2.disposeIntermediateTensorInfo(xTransposed),backend2.disposeIntermediateTensorInfo(xTransposedReshaped),result}var batchToSpaceNDConfig={kernelName:BatchToSpaceND,backendName:"cpu",kernelFunc:batchToSpaceND2};function bincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig={kernelName:Bincount,backendName:"cpu",kernelFunc:bincount2},clip=unaryKernelFunc(ClipByValue,(xi,attrs)=>{let clipAttrs=attrs;return xi>clipAttrs.clipValueMax?clipAttrs.clipValueMax:xi<clipAttrs.clipValueMin?clipAttrs.clipValueMin:xi}),clipConfig={kernelName:ClipByValue,backendName:"cpu",kernelFunc:clip},complexAbs=args=>{let{x}=args.inputs,cpuBackend=args.backend,resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),complexVals=cpuBackend.data.get(x.dataId),real4=complexVals.complexTensorInfos.real,imag4=complexVals.complexTensorInfos.imag,realVals=cpuBackend.data.get(real4.dataId).values,imagVals=cpuBackend.data.get(imag4.dataId).values;for(let i=0;i<realVals.length;i++){let real5=realVals[i],imag5=imagVals[i];resultValues[i]=Math.hypot(real5,imag5)}return cpuBackend.makeOutput(resultValues,x.shape,"float32")},complexAbsConfig={kernelName:ComplexAbs,backendName:"cpu",kernelFunc:complexAbs};function imag2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,imag4=backend2.data.get(input2.dataId).complexTensorInfos.imag,imagVal=backend2.data.get(imag4.dataId).values;return backend2.makeTensorInfo(imag4.shape,imag4.dtype,imagVal)}var imagConfig={kernelName:Imag,backendName:"cpu",kernelFunc:imag2};function concat2(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity2({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,$axis),$inputs[0].dtype==="complex64"){let reals=$inputs.map(t=>real2({inputs:{input:t},backend:backend2})),imags=$inputs.map(t=>imag2({inputs:{input:t},backend:backend2})),realConcated=concat2({inputs:reals,backend:backend2,attrs:{axis:$axis}}),imagConcated=concat2({inputs:imags,backend:backend2,attrs:{axis:$axis}}),result=complex2({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result}let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice($axis));return reshape3({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.data.get(t.dataId).values,shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),$axis),outInfo=backend2.makeTensorInfo(finalOutShape,inputs[0].dtype,outVals);return inputs2D.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}var concatConfig={kernelName:Concat,backendName:"cpu",kernelFunc:concat2};function conv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"conv2d");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,padLeft=convInfo.padInfo.left,padTop=convInfo.padInfo.top,isChannelsLast=convInfo.dataFormat==="channelsLast",y=new TensorBuffer(convInfo.outShape,x.dtype),xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),xBatchStride=xStrides[0],xRowStride=isChannelsLast?xStrides[1]:xStrides[2],xColStride=isChannelsLast?xStrides[2]:1,xChannelStride=isChannelsLast?1:xStrides[1],yBatchStride=y.strides[0],yRowStride=isChannelsLast?y.strides[1]:y.strides[2],yColStride=isChannelsLast?y.strides[2]:1,yChannelStride=isChannelsLast?1:y.strides[1],xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xBatchStride,yOffset1=b*yBatchStride;for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*yRowStride,xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(!(xR<0||xR>=convInfo.inHeight)){let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xRowStride;for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*yColStride,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(!(xC<0||xC>=convInfo.inWidth)){let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*xColStride,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1*xChannelStride];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset3+d2*yChannelStride]+=xVal*wVals[wOffset3+d2];wOffset3+=convInfo.outChannels}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,yVals)}var conv2DConfig={kernelName:Conv2D,backendName:"cpu",kernelFunc:conv2D};function conv2DBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"conv2dBackpropFilter");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,isChannelsLast=convInfo.dataFormat==="channelsLast",dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,xVals=backend2.data.get(x.dataId).values,dyVals=backend2.data.get(dy.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d1=0;d1<convInfo.inChannels;++d1)for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;isChannelsLast?dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2):dotProd+=xBuf.get(b,d1,xR,xC)*dyBuf.get(b,d2,yR,yC)}}dW.set(dotProd,wR,wC,d1,d2)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var conv2DBackpropFilterConfig={kernelName:Conv2DBackpropFilter,backendName:"cpu",kernelFunc:conv2DBackpropFilter2};function conv2DBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{inputShape,strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;assertNotComplex([dy,filter],"conv2dBackpropInput");let filterStrides=util_exports.computeStrides(filter.shape),dyStrides=util_exports.computeStrides(dy.shape),$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,dyValues=backend2.data.get(dy.dataId).values,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo;$dataFormat=convInfo.dataFormat;let topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=$dataFormat==="channelsLast",xBatchStride=dx.strides[0],xRowStride=isChannelsLast?dx.strides[1]:dx.strides[2],xColStride=isChannelsLast?dx.strides[2]:1,xChannelStride=isChannelsLast?1:dx.strides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1];for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=yBatchStride*b+yRowStride*yR+yColStride*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+yChannelStride*d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}let dxOffset=xBatchStride*b+xRowStride*xR+xColStride*xC+xChannelStride*d1;dxValues[dxOffset]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv2DBackpropInputConfig={kernelName:Conv2DBackpropInput,backendName:"cpu",kernelFunc:conv2DBackpropInput2};function conv3D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs;assertNotComplex([x,filter],"conv3d");let convInfo=backend_util_exports.computeConv3DInfo(x.shape,filter.shape,strides,dilations,pad3),{filterDepth,filterHeight,filterWidth,dilationDepth,dilationHeight,dilationWidth,padInfo}=convInfo,padFront=padInfo.front,padLeft=padInfo.left,padTop=padInfo.top,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values,xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yF=0;yF<convInfo.outDepth;++yF){let yOffset2=yOffset1+yF*y.strides[1],xFCorner=yF*convInfo.strideDepth-padFront;for(let wF=0;wF<filterDepth;++wF){let xF=xFCorner+wF*dilationDepth;if(!(xF<0||xF>=convInfo.inDepth)){let wOffset1=wF*filterStrides[0],xOffset2=xOffset1+xF*xStrides[1];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset3=yOffset2+yR*y.strides[2],xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(!(xR<0||xR>=convInfo.inHeight)){let wOffset2=wOffset1+wR*filterStrides[1],xOffset3=xOffset2+xR*xStrides[2];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset4=yOffset3+yC*convInfo.outChannels,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(!(xC<0||xC>=convInfo.inWidth)){let wOffset3=wOffset2+wC*filterStrides[2],xOffset4=xOffset3+xC*convInfo.inChannels,wOffset4=wOffset3;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset4+d1];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset4+d2]+=xVal*wVals[wOffset4+d2];wOffset4+=convInfo.outChannels}}}}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var conv3DConfig={kernelName:Conv3D,backendName:"cpu",kernelFunc:conv3D};function conv3DBackpropFilterV2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,filterShape}=attrs;assertNotComplex([x,dy],"conv3dBackpropFilterV2");let xStrides=util_exports.computeStrides(x.shape),dyStrides=util_exports.computeStrides(dy.shape),convInfo=backend_util_exports.computeConv3DInfo(x.shape,filterShape,strides,1,pad3),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dw=new TensorBuffer(convInfo.filterShape,"float32"),dwValues=dw.values,[dwS0,dwS1,dwS2,dwS3]=dw.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,xValues=backend2.data.get(x.dataId).values,[xS0,xS1,xS2,xS3]=xStrides,frontPad=convInfo.padInfo.front,leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top;for(let wF=0;wF<filterDepth;++wF){let yFMin=Math.max(0,Math.ceil((frontPad-wF)/strideDepth)),yFMax=Math.min(convInfo.outDepth,(convInfo.inDepth+frontPad-wF)/strideDepth),wOffset1=wF*dwS0;for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight),wOffset2=wR*dwS1+wOffset1;for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth),wOffset3=wC*dwS2+wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let wOffset4=d1*dwS3+wOffset3;for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xS0,yOffset1=b*dyS0;for(let yF=yFMin;yF<yFMax;++yF){let xOffset2=(wF+yF*strideDepth-frontPad)*xS1+xOffset1,yOffset2=yF*dyS1+yOffset1;for(let yR=yRMin;yR<yRMax;++yR){let xOffset3=(wR+yR*strideHeight-topPad)*xS2+xOffset2,yOffset3=yR*dyS2+yOffset2;for(let yC=yCMin;yC<yCMax;++yC){let xOffset4=(wC+yC*strideWidth-leftPad)*xS3+xOffset3,yOffset4=yC*dyS3+yOffset3;dotProd+=xValues[xOffset4+d1]*dyValues[yOffset4+d2]}}}}dwValues[wOffset4+d2]=dotProd}}}}}return backend2.makeTensorInfo(dw.shape,dw.dtype,dw.values)}var conv3DBackpropFilterV2Config={kernelName:Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:conv3DBackpropFilterV2};function conv3DBackpropInputV2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{pad:pad3,strides,inputShape}=attrs;assertNotComplex([dy],"conv3dBackpropInputV2");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv3DInfo(inputShape,filter.shape,strides,1,pad3),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2,dxS3]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2,fltS3]=filterStrides,{batchSize,filterDepth,filterHeight,filterWidth,inChannels,inDepth,inHeight,inWidth,outChannels,outDepth,outHeight,outWidth,strideDepth,strideHeight,strideWidth}=convInfo,frontPad=filterDepth-1-convInfo.padInfo.front,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xF=0;xF<inDepth;++xF){let xFCorner=xF-frontPad,xFMin=Math.max(0,Math.ceil(xFCorner/strideDepth)),yFMax=Math.min(outDepth,(filterDepth+xFCorner)/strideDepth);for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yF=xFMin;yF<yFMax;++yF){let wF=yF*strideDepth-xFCorner;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yF+dyS2*yR+dyS3*yC,fltOffset=fltS0*(filterDepth-1-wF)+fltS1*(filterHeight-1-wR)+fltS2*(filterWidth-1-wC)+fltS3*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}}dxValues[dxS0*b+dxS1*xF+dxS2*xR+dxS3*xC+d1]=dotProd}}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv3DBackpropInputV2Config={kernelName:Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:conv3DBackpropInputV2},cos2=unaryKernelFunc(Cos,xi=>Math.cos(xi)),cosConfig={kernelName:Cos,backendName:"cpu",kernelFunc:cos2},cosh2=unaryKernelFunc(Cosh,xi=>Math.cosh(xi)),coshConfig={kernelName:Cosh,backendName:"cpu",kernelFunc:cosh2};function cropAndResize2(args){let{inputs,backend:backend2,attrs}=args,{image:image3,boxes,boxInd}=inputs,{cropSize,method,extrapolationValue}=attrs,[batch,imageHeight,imageWidth,numChannels]=image3.shape,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,output=buffer([numBoxes,cropHeight,cropWidth,numChannels],"float32"),boxVals=backend2.data.get(boxes.dataId).values,boxIndVals=backend2.data.get(boxInd.dataId).values,imageVals=backend2.data.get(image3.dataId).values,inStride=util_exports.computeStrides(image3.shape),outStride=util_exports.computeStrides(output.shape);for(let b=0;b<numBoxes;b++){let startInd=b*4,y1=boxVals[startInd],x1=boxVals[startInd+1],y2=boxVals[startInd+2],x2=boxVals[startInd+3],bInd=boxIndVals[b];if(!(bInd>=batch)){let heightScale=cropHeight>1?(y2-y1)*(imageHeight-1)/(cropHeight-1):0,widthScale=cropWidth>1?(x2-x1)*(imageWidth-1)/(cropWidth-1):0;for(let y=0;y<cropHeight;y++){let yInd=cropHeight>1?y1*(imageHeight-1)+y*heightScale:.5*(y1+y2)*(imageHeight-1);if(yInd<0||yInd>imageHeight-1){for(let x=0;x<cropWidth;x++)for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}if(method==="bilinear"){let topInd=Math.floor(yInd),bottomInd=Math.ceil(yInd),yLerp=yInd-topInd;for(let x=0;x<cropWidth;x++){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let leftInd=Math.floor(xInd),rightInd=Math.ceil(xInd),xLerp=xInd-leftInd;for(let c=0;c<numChannels;c++){let ind=c+leftInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0],topLeft=imageVals[ind];ind=c+rightInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];let topRight=imageVals[ind];ind=c+leftInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomLeft=imageVals[ind];ind=c+rightInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomRight=imageVals[ind],top=topLeft+(topRight-topLeft)*xLerp,bottom=bottomLeft+(bottomRight-bottomLeft)*xLerp;ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0],output.values[ind]=top+(bottom-top)*yLerp}}}else for(let x=0;x<cropWidth;++x){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let closestX=Math.round(xInd),closestY=Math.round(yInd);for(let c=0;c<numChannels;c++){let inInd=c+closestX*inStride[2]+closestY*inStride[1]+bInd*inStride[0],outInd=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[outInd]=imageVals[inInd]}}}}}return backend2.makeTensorInfo(output.shape,output.dtype,output.values)}var cropAndResizeConfig={kernelName:CropAndResize,backendName:"cpu",kernelFunc:cropAndResize2};function cumsum2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs;assertNotComplex(x,"cumsum");let permutation=backend_util_exports.getAxesPermutation([axis],x.shape.length),$x=x;permutation!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,x.shape.length)[0];if(permutedAxis!==$x.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length-1} but got axis=${permutedAxis}`);let resultDtype=upcastType($x.dtype,"int32"),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape),resultDtype),aVals=backend2.data.get($x.dataId).values,finalDim=$x.shape[$x.shape.length-1],indexAdjuster=reverse5?(i,j)=>i+finalDim-j-1:(i,j)=>i+j;for(let i=0;i<aVals.length;i+=finalDim)for(let j=0;j<finalDim;j++){let idx=indexAdjuster(i,j);if(j===0)vals[idx]=exclusive?0:aVals[idx];else{let prevIdx=indexAdjuster(i,j-1);vals[idx]=exclusive?aVals[prevIdx]+vals[prevIdx]:aVals[idx]+vals[prevIdx]}}let result=backend2.makeTensorInfo($x.shape,resultDtype,vals);if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose2({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo($x),reverseTransposedResult}return result}var cumsumConfig={kernelName:Cumsum,backendName:"cpu",kernelFunc:cumsum2};function denseBincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig={kernelName:DenseBincount,backendName:"cpu",kernelFunc:denseBincount2};function depthToSpace2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(dataFormat==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`),util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=x.shape[1],inputWidth=x.shape[2],inputDepth=x.shape[3],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),xValues=backend2.data.get(x.dataId).values,result=new Float32Array(batchSize*outputHeight*outputWidth*outputDepth),outputIdx=0;for(let b=0;b<batchSize;++b)for(let h=0;h<outputHeight;++h){let inH=Math.floor(h/blockSize),offsetH=h%blockSize;for(let w=0;w<outputWidth;++w){let inW=Math.floor(w/blockSize),offsetW=w%blockSize,offsetD=(offsetH*blockSize+offsetW)*outputDepth;for(let d=0;d<outputDepth;++d){let inputIdx=d+offsetD+inputDepth*(inW+inputWidth*(inH+inputHeight*b));result[outputIdx++]=xValues[inputIdx]}}}return backend2.makeTensorInfo([batchSize,outputHeight,outputWidth,outputDepth],x.dtype,result)}var depthToSpaceConfig={kernelName:DepthToSpace,backendName:"cpu",kernelFunc:depthToSpace2};function depthwiseConv2dNative(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"depthwiseConv2DNative");let xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),{filterHeight,filterWidth,dilationHeight,dilationWidth,padInfo}=convInfo,padLeft=padInfo.left,padTop=padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*y.strides[1],xRCorner=yR*convInfo.strideHeight-padLeft;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(!(xR<0||xR>=convInfo.inHeight)){let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xStrides[1];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*y.strides[2],xCCorner=yC*convInfo.strideWidth-padTop;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(!(xC<0||xC>=convInfo.inWidth)){let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*convInfo.inChannels,yOffset4=yOffset3,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1];for(let q=0;q<chMul;++q)yVals[yOffset4+q]+=xVal*wVals[wOffset3+q];yOffset4+=chMul,wOffset3+=chMul}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var depthwiseConv2dNativeConfig={kernelName:DepthwiseConv2dNative,backendName:"cpu",kernelFunc:depthwiseConv2dNative};function depthwiseConv2dNativeBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"depthwiseConv2dNativeBackpropFilter");let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,dilations,pad3,dimRoundingMode,!0),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,xVals=backend2.data.get(x.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyVals=backend2.data.get(dy.dataId).values,dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d2=0;d2<convInfo.outChannels;++d2){let d1=Math.trunc(d2/chMul),dm=d2%chMul,dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2)}}dW.set(dotProd,wR,wC,d1,dm)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var depthwiseConv2dNativeBackpropFilterConfig={kernelName:DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropFilter2};function depthwiseConv2dNativeBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,inputShape}=attrs;assertNotComplex([dy,filter],"depthwiseConv2DNativeBackpropInput");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,chMul=outChannels/inChannels;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yR+dyS2*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let dm=0;dm<chMul;++dm){let d2=d1*chMul+dm,pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+dm];dotProd+=pixel*weight}}}dxValues[dxS0*b+dxS1*xR+dxS2*xC+d1]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var depthwiseConv2dNativeBackpropInputConfig={kernelName:DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropInput2};function diag2(args){let{inputs,backend:backend2}=args,{x}=inputs,xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outBuf=buffer([xSize,xSize],x.dtype),vals=outBuf.values;for(let i=0;i<xVals.length;i++)vals[i*xSize+i]=xVals[i];let outShape=[...x.shape,...x.shape];return backend2.makeTensorInfo(outShape,outBuf.dtype,outBuf.values)}var diagConfig={kernelName:Diag,backendName:"cpu",kernelFunc:diag2},dilation2dConfig={kernelName:Dilation2D,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,xVals=cpuBackend.data.get(x.dataId).values,xRank=x.shape.length,filterVals=cpuBackend.data.get(filter.dataId).values,filterRank=filter.shape.length,{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations),outSize=util_exports.sizeFromShape(outShape),outRank=outShape.length,outputVals=util_exports.getArrayFromDType(x.dtype,outSize);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let xIndex=util_exports.locToIndex([b,hIn,wIn,d],xRank,util_exports.computeStrides(x.shape)),filterIndex=util_exports.locToIndex([h,w,d],filterRank,util_exports.computeStrides(filter.shape)),val=xVals[xIndex]+filterVals[filterIndex];val>curVal&&(curVal=val)}}}let outputIndex=util_exports.locToIndex([b,hOut,wOut,d],outRank,util_exports.computeStrides(outShape));outputVals[outputIndex]=curVal}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(outputVals,x.dtype),outShape,x.dtype),shape:outShape,dtype:x.dtype}}},dilation2dBackpropFilterConfig={kernelName:Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(filter.shape,filter.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hMax=0,wMax=0;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hMax=h,wMax=w)}}}gradients[hMax][wMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),filter.shape,filter.dtype),shape:filter.shape,dtype:filter.dtype}}},dilation2dBackpropInputConfig={kernelName:Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(x.shape,x.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hInMax=hBeg<0?0:hBeg,wInMax=wBeg<0?0:wBeg;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hInMax=hIn,wInMax=wIn)}}}gradients[b][hInMax][wInMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}};function eluGrad(args){let{inputs,backend:backend2}=args,{dy,y}=inputs;assertNotComplex([dy,y],"eluGrad");let resultValues=new Float32Array(util_exports.sizeFromShape(y.shape)),values=backend2.data.get(y.dataId).values,dyValues=backend2.data.get(dy.dataId).values;for(let i=0;i<values.length;++i){let v=values[i];v>=1?resultValues[i]=dyValues[i]:resultValues[i]=dyValues[i]*(v+1)}return backend2.makeTensorInfo(y.shape,"float32",resultValues)}var eluGradConfig2={kernelName:EluGrad,backendName:"cpu",kernelFunc:eluGrad},equalImpl=createSimpleBinaryKernelImpl((a,b)=>a===b?1:0),equal2=binaryKernelFunc(Equal,equalImpl,null,"bool"),equalConfig={kernelName:Equal,backendName:"cpu",kernelFunc:equal2},p=backend_util_exports.ERF_P,a1=backend_util_exports.ERF_A1,a2=backend_util_exports.ERF_A2,a3=backend_util_exports.ERF_A3,a4=backend_util_exports.ERF_A4,a5=backend_util_exports.ERF_A5,erf2=unaryKernelFunc(Erf,xi=>{let sign4=Math.sign(xi),v=Math.abs(xi),t=1/(1+p*v);return sign4*(1-((((a5*t+a4)*t+a3)*t+a2)*t+a1)*t*Math.exp(-v*v))}),erfConfig={kernelName:Erf,backendName:"cpu",kernelFunc:erf2};function expandDims3(args){let{inputs,backend:backend2,attrs}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig={kernelName:ExpandDims,backendName:"cpu",kernelFunc:expandDims3},realDivImpl=createSimpleBinaryKernelImpl((a,b)=>a/b),div2=binaryKernelFunc(RealDiv,realDivImpl),realDivConfig={kernelName:RealDiv,backendName:"cpu",kernelFunc:div2};function fftBatch(input2,inverse,cpuBackend){let inputShape=input2.shape,batch=inputShape[0],innerDim=inputShape[1],inputVals=cpuBackend.data.get(input2.dataId),real2D=inputVals.complexTensorInfos.real,imag2D=inputVals.complexTensorInfos.imag,resultShape=[batch,innerDim],resultSize=util_exports.sizeFromShape(resultShape),resultReal=util_exports.getTypedArrayFromDType("float32",resultSize),resultImag=util_exports.getTypedArrayFromDType("float32",resultSize);for(let b=0;b<batch;b++){let r=slice2({inputs:{x:real2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),i=slice2({inputs:{x:imag2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),input3=complex2({inputs:{real:r,imag:i},backend:cpuBackend}),{real:real4,imag:imag4}=fftImpl(input3,inverse,cpuBackend),res=backend_util_exports.mergeRealAndImagArrays(real4,imag4);for(let d=0;d<innerDim;d++){let c=backend_util_exports.getComplexWithIndex(res,d);resultReal[b*innerDim+d]=c.real,resultImag[b*innerDim+d]=c.imag}cpuBackend.disposeIntermediateTensorInfo(r),cpuBackend.disposeIntermediateTensorInfo(i),cpuBackend.disposeIntermediateTensorInfo(input3)}let $realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultReal),$imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultImag),result=complex2({inputs:{real:$realInfo,imag:$imagInfo},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($realInfo),cpuBackend.disposeIntermediateTensorInfo($imagInfo),result}function fftImpl(input2,inverse,cpuBackend){let inputSize=util_exports.sizeFromShape(input2.shape),inputVals=cpuBackend.data.get(input2.dataId),realVals=cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values,imagVals=cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;if(isExponentOf2(inputSize)){let result=fftRadix2(realVals,imagVals,inputSize,inverse,cpuBackend),resultShape=[input2.shape[0],input2.shape[1]];if(inverse){let realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.real),imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.imag),sizeInfo=cpuBackend.makeTensorInfo([],"float32",util_exports.createScalarValue(inputSize,"float32")),sizeInfoCopy=identity2({inputs:{x:sizeInfo},backend:cpuBackend}),divRealInfo=realDivConfig.kernelFunc({inputs:{a:realInfo,b:sizeInfo},backend:cpuBackend}),divImagInfo=realDivConfig.kernelFunc({inputs:{a:imagInfo,b:sizeInfoCopy},backend:cpuBackend}),divRealVals=cpuBackend.data.get(divRealInfo.dataId).values,divImagVals=cpuBackend.data.get(divImagInfo.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(realInfo),cpuBackend.disposeIntermediateTensorInfo(imagInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy),cpuBackend.disposeIntermediateTensorInfo(divRealInfo),cpuBackend.disposeIntermediateTensorInfo(divImagInfo),{real:divRealVals,imag:divImagVals}}return result}else{let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),rawOutput=fourierTransformByMatmul(data,inputSize,inverse);return backend_util_exports.splitRealAndImagArrays(rawOutput)}}function isExponentOf2(size){return(size&size-1)===0}function fftRadix2(realVals,imagVals,size,inverse,cpuBackend){if(size===1)return{real:realVals,imag:imagVals};let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),half=size/2,evenComplex=backend_util_exports.complexWithEvenIndex(data),evenRealVals=evenComplex.real,evenImagVals=evenComplex.imag,evenShape=[evenRealVals.length],evenRealInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenRealVals),evenImagInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenImagVals),evenTensorInfo=complex2({inputs:{real:evenRealInfo,imag:evenImagInfo},backend:cpuBackend}),oddComplex=backend_util_exports.complexWithOddIndex(data),oddRealVals=oddComplex.real,oddImagVals=oddComplex.imag,oddShape=[oddRealVals.length],oddRealInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddRealVals),oddImagInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddImagVals),oddTensorInfo=complex2({inputs:{real:oddRealInfo,imag:oddImagInfo},backend:cpuBackend}),$evenComplex=fftRadix2(evenRealVals,evenImagVals,half,inverse,cpuBackend),$evenRealVals=$evenComplex.real,$evenImagVals=$evenComplex.imag,$evenShape=[$evenRealVals.length],$evenRealInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenRealVals),$evenImagInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenImagVals),$evenTensorInfo=complex2({inputs:{real:$evenRealInfo,imag:$evenImagInfo},backend:cpuBackend}),$oddComplex=fftRadix2(oddRealVals,oddImagVals,half,inverse,cpuBackend),$oddRealVals=$oddComplex.real,$oddImagVals=$oddComplex.imag,$oddShape=[$oddRealVals.length],$oddRealInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddRealVals),$oddImagInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddImagVals),$oddTensorInfo=complex2({inputs:{real:$oddRealInfo,imag:$oddImagInfo},backend:cpuBackend}),e=backend_util_exports.exponents(size,inverse),eShape=[e.real.length],eRealInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.real),eImagInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.imag),complexInfo=complex2({inputs:{real:eRealInfo,imag:eImagInfo},backend:cpuBackend}),exponentInfo=multiply2({inputs:{a:complexInfo,b:$oddTensorInfo},backend:cpuBackend}),addPart=add4({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),subPart=sub2({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),addPartReal=real2({inputs:{input:addPart},backend:cpuBackend}),subPartReal=real2({inputs:{input:subPart},backend:cpuBackend}),addPartImag=imag2({inputs:{input:addPart},backend:cpuBackend}),subPartImag=imag2({inputs:{input:subPart},backend:cpuBackend}),$real=concat2({inputs:[addPartReal,subPartReal],backend:cpuBackend,attrs:{axis:0}}),$imag=concat2({inputs:[addPartImag,subPartImag],backend:cpuBackend,attrs:{axis:0}}),$realVals=cpuBackend.data.get($real.dataId).values,$imagVals=cpuBackend.data.get($imag.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(evenRealInfo),cpuBackend.disposeIntermediateTensorInfo(evenImagInfo),cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo(oddRealInfo),cpuBackend.disposeIntermediateTensorInfo(oddImagInfo),cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo($evenRealInfo),cpuBackend.disposeIntermediateTensorInfo($evenImagInfo),cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo($oddRealInfo),cpuBackend.disposeIntermediateTensorInfo($oddImagInfo),cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo(eRealInfo),cpuBackend.disposeIntermediateTensorInfo(eImagInfo),cpuBackend.disposeIntermediateTensorInfo(complexInfo),cpuBackend.disposeIntermediateTensorInfo(exponentInfo),cpuBackend.disposeIntermediateTensorInfo(addPart),cpuBackend.disposeIntermediateTensorInfo(subPart),cpuBackend.disposeIntermediateTensorInfo(addPartReal),cpuBackend.disposeIntermediateTensorInfo(addPartImag),cpuBackend.disposeIntermediateTensorInfo(subPartReal),cpuBackend.disposeIntermediateTensorInfo(subPartImag),cpuBackend.disposeIntermediateTensorInfo($real),cpuBackend.disposeIntermediateTensorInfo($imag),{real:$realVals,imag:$imagVals}}function fourierTransformByMatmul(data,size,inverse){let ret=new Float32Array(size*2);for(let r=0;r<size;r++){let real4=0,imag4=0;for(let c=0;c<size;c++){let e=backend_util_exports.exponent(r*c,size,inverse),term=backend_util_exports.getComplexWithIndex(data,c);real4+=term.real*e.real-term.imag*e.imag,imag4+=term.real*e.imag+term.imag*e.real}inverse&&(real4/=size,imag4/=size),backend_util_exports.assignToTypedArray(ret,real4,imag4,r)}return ret}function fft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!1,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var fftConfig={kernelName:FFT,backendName:"cpu",kernelFunc:fft2};function fill2(args){let{backend:backend2,attrs}=args,{shape,value,dtype}=attrs,$dtype=dtype||util_exports.inferDtype(value),values=util_exports.getArrayFromDType($dtype,util_exports.sizeFromShape(shape));return fillValues(values,value,$dtype),backend2.makeTensorInfo(shape,$dtype,values)}var fillConfig={kernelName:Fill,backendName:"cpu",kernelFunc:fill2};function fillValues(values,value,dtype){values.fill(value)}var flipLeftRightConfig={kernelName:FlipLeftRight,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let x=[batch,row,col,channel][2],coordX=Math.round(imageWidth-x),outIdx=batchOffset+rowOffset+colOffset+channel,outputValue=imageVals[outIdx];if(coordX>=0&&coordX<imageWidth){let rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}},floorDivImpl=createSimpleBinaryKernelImpl((a,b)=>Math.floor(a/b)),floorDiv2=binaryKernelFunc(FloorDiv,floorDivImpl,null,"int32"),floorDivConfig={kernelName:FloorDiv,backendName:"cpu",kernelFunc:floorDiv2};function fusedConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=conv2D({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let resultOld=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(resultOld)}if(activation2){let resultOld=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(resultOld)}return result}var fusedConv2DConfig={kernelName:FusedConv2D,backendName:"cpu",kernelFunc:fusedConv2D};function fusedDepthwiseConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=depthwiseConv2dNative({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let oldResult=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(oldResult)}if(activation2){let oldResult=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(oldResult)}return result}var fusedDepthwiseConv2DConfig={kernelName:FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:fusedDepthwiseConv2D};function gatherNd(args){let{inputs,backend:backend2}=args,{params,indices}=inputs,paramsSize=util_exports.sizeFromShape(params.shape),indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(params,indices);if(numSlices===0)return backend2.makeTensorInfo(resultShape,params.dtype,[]);let outBuf=buffer([numSlices,sliceSize],params.dtype),indicesData=backend2.data.get(indices.dataId).values,paramsData=backend2.data.get(params.dataId).values;for(let i=0;i<numSlices;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];flattenIndex+=dim*strides[j],index.push(dim)}if(flattenIndex<0||flattenIndex>=paramsSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${params.shape}`);for(let k=0;k<sliceSize;k++)outBuf.values[i*sliceSize+k]=paramsData[flattenIndex*sliceSize+k]}return backend2.makeTensorInfo(resultShape,outBuf.dtype,outBuf.values)}var gatherNdConfig={kernelName:GatherNd,backendName:"cpu",kernelFunc:gatherNd};function gatherV2(args){let{inputs,backend:backend2,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs;assertNotComplex([x,indices],"gatherV2");let $batchDims=batchDims;batchDims==null&&($batchDims=0);let indicesSize=util_exports.sizeFromShape(indices.shape),parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,$batchDims),flattenX=reshape3({inputs:{x},backend:backend2,attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]}}),flattenIndex=reshape3({inputs:{x:indices},backend:backend2,attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]}}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],indicesBuf=backend2.bufferSync(flattenIndex),xBuf=backend2.bufferSync(flattenX),outBuf=gatherV2Impl(xBuf,indicesBuf,flattenOutputShape);return backend2.disposeIntermediateTensorInfo(flattenX),backend2.disposeIntermediateTensorInfo(flattenIndex),backend2.makeTensorInfo(shapeInfo.outputShape,outBuf.dtype,outBuf.values)}var gatherV2Config={kernelName:GatherV2,backendName:"cpu",kernelFunc:gatherV2},greaterEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a>=b?1:0),greaterEqual2=binaryKernelFunc(GreaterEqual,greaterEqualImpl,null,"bool"),greaterEqualConfig={kernelName:GreaterEqual,backendName:"cpu",kernelFunc:greaterEqual2};function ifft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!0,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var ifftConfig={kernelName:IFFT,backendName:"cpu",kernelFunc:ifft2},isFinite3=unaryKernelFunc(IsFinite,xi=>Number.isFinite(xi)?1:0,"bool"),isFiniteConfig={kernelName:IsFinite,backendName:"cpu",kernelFunc:isFinite3},isInf2=unaryKernelFunc(IsInf,xi=>Math.abs(xi)===Infinity?1:0,"bool"),isInfConfig={kernelName:IsInf,backendName:"cpu",kernelFunc:isInf2},isNaN3=unaryKernelFunc(IsNan,xi=>Number.isNaN(xi)?1:0,"bool"),isNaNConfig={kernelName:IsNan,backendName:"cpu",kernelFunc:isNaN3},lessEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a<=b?1:0),lessEqual2=binaryKernelFunc(LessEqual,lessEqualImpl,null,"bool"),lessEqualConfig={kernelName:LessEqual,backendName:"cpu",kernelFunc:lessEqual2};function linSpace(args){let{backend:backend2,attrs}=args,{start,stop,num}=attrs,outVals=linSpaceImpl(start,stop,num);return backend2.makeTensorInfo([outVals.length],"float32",outVals)}var linSpaceConfig={kernelName:LinSpace,backendName:"cpu",kernelFunc:linSpace},log1p2=unaryKernelFunc(Log1p,xi=>Math.log1p(xi)),log1pConfig={kernelName:Log1p,backendName:"cpu",kernelFunc:log1p2},logicalAndImpl=createSimpleBinaryKernelImpl((a,b)=>a&&b),logicalAnd2=binaryKernelFunc(LogicalAnd,logicalAndImpl,null,"bool"),logicalAndConfig={kernelName:LogicalAnd,backendName:"cpu",kernelFunc:logicalAnd2},logicalNot2=unaryKernelFunc(LogicalNot,xi=>xi?0:1,"bool"),logicalNotConfig={kernelName:LogicalNot,backendName:"cpu",kernelFunc:logicalNot2},logicalOrImpl=createSimpleBinaryKernelImpl((a,b)=>a||b),logicalOr2=binaryKernelFunc(LogicalOr,logicalOrImpl,null,"bool"),logicalOrConfig={kernelName:LogicalOr,backendName:"cpu",kernelFunc:logicalOr2};function lRN(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(x,"LRN");let channels=x.shape[3],maxD=channels-1,xValues=backend2.data.get(x.dataId).values,size=util_exports.sizeFromShape(x.shape),result=new Float32Array(size);function sumAcrossChannels(offset){let currentChannel=offset%channels,beginSumOffset=offset-currentChannel+Math.max(0,currentChannel-depthRadius),endSumOffset=offset-currentChannel+Math.min(currentChannel+depthRadius,maxD),sum6=0;for(;beginSumOffset<=endSumOffset;beginSumOffset++){let z=xValues[beginSumOffset];sum6+=z*z}return sum6}for(let offset=0;offset<size;offset++){let sum6=sumAcrossChannels(offset),val=xValues[offset]*Math.pow(bias+alpha*sum6,-beta);result[offset]=val}return backend2.makeTensorInfo(x.shape,x.dtype,result)}var lRNConfig={kernelName:LRN,backendName:"cpu",kernelFunc:lRN};function lRNGrad(args){let{inputs,backend:backend2,attrs}=args,{x,y,dy}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(dy,"LRNGrad");let dySize=util_exports.sizeFromShape(dy.shape),channels=dy.shape[3],dyValues=backend2.data.get(dy.dataId).values,xValues=backend2.data.get(x.dataId).values,yValues=backend2.data.get(y.dataId).values,result=new Float32Array(dySize),size=dySize;for(let offset=0;offset<size;offset++){let currentChannel=offset%channels,depthBegin=offset-currentChannel+Math.max(0,currentChannel-depthRadius),depthEnd=offset-currentChannel+Math.min(channels,currentChannel+depthRadius+1),norm2=0;for(let k=depthBegin;k<depthEnd;k++)norm2+=Math.pow(xValues[k],2);norm2=alpha*norm2+bias;for(let k=depthBegin;k<depthEnd;k++){let dyi=-2*alpha*beta*xValues[k]*yValues[offset]/norm2;offset===k&&(dyi+=Math.pow(norm2,-beta)),dyi*=dyValues[offset],result[k]+=dyi}}return backend2.makeTensorInfo(dy.shape,x.dtype,result)}var lRNGradConfig={kernelName:LRNGrad,backendName:"cpu",kernelFunc:lRNGrad};function max3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reductionIndices,keepDims}=attrs,cpuBackend=backend2,xShape=x.shape,xRank=xShape.length,origAxes=util_exports.parseAxisParam(reductionIndices,xShape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xVals=cpuBackend.data.get(x.dataId).values;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];xVals=transposeImpl(xVals,xShape,x.dtype,permutedAxes,newShape),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xShape=newShape}assertNotComplex(x,"max"),backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),result=maxImpl(xVals,reduceSize,maxOutShape,x.dtype),dataId=cpuBackend.write(result,maxOutShape,x.dtype),outShape=maxOutShape;return keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes)),{dataId,shape:outShape,dtype:x.dtype}}var maxConfig={kernelName:Max,backendName:"cpu",kernelFunc:max3};function maxPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"maxPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"max");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var maxPoolConfig={kernelName:MaxPool,backendName:"cpu",kernelFunc:maxPool2};function maxPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"maxPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"max");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var maxPool3DConfig={kernelName:MaxPool3D,backendName:"cpu",kernelFunc:maxPool3D};function maxPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"maxPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),inputBuf=backend2.bufferSync(input2),maxPosBuf=maxPool3dPositions(inputBuf,convInfo),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(!(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)){let maxPos=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(batch,dyDepth,dyRow,dyCol,channel),curPos=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterWidth+wCol,mask=maxPos===curPos?1:0;mask!==0&&(dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel)*mask)}}}}dx.set(dotProd,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPool3DGradConfig2={kernelName:MaxPool3DGrad,backendName:"cpu",kernelFunc:maxPool3DGrad};function maxPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex([input2,output],"maxPoolGrad");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),xValues=backend2.data.get(x.dataId).values,maxPosBuf=buffer(convInfo.outShape,x.dtype,maxPoolPositions(xValues,x.shape,x.dtype,convInfo).values),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(!(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)){let maxPos=effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(b,dyR,dyC,d),curPos=wR*effectiveFilterWidth+wC,mask=maxPos===curPos?1:0;mask!==0&&(dotProd+=dyBuf.get(b,dyR,dyC,d)*mask)}}}dx.set(dotProd,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPoolGradConfig2={kernelName:MaxPoolGrad,backendName:"cpu",kernelFunc:maxPoolGrad2};function maxPoolWithArgmaxImpl(xValues,xShape,dtype,includeBatchInIndex,convInfo){let strides=util_exports.computeStrides(xShape),maxPools=pool2(xValues,xShape,dtype,strides,convInfo,"max"),maxPositions=maxPoolPositions(xValues,xShape,dtype,convInfo,!0,includeBatchInIndex);return[maxPools.values,maxPositions.values]}var maxPoolWithArgmaxConfig={kernelName:MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{x}=inputs,{filterSize,strides,pad:pad3,includeBatchInIndex}=attrs,cpuBackend=backend2;assertNotComplex(x,"MaxPoolWithArgmax");let values=cpuBackend.data.get(x.dataId).values,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,[1,1],pad3),[pooled,indexes]=maxPoolWithArgmaxImpl(values,x.shape,x.dtype,includeBatchInIndex,convInfo),pooledDataId=cpuBackend.write(pooled,convInfo.outShape,x.dtype),indexesDataId=cpuBackend.write(indexes,convInfo.outShape,x.dtype);return[{dataId:pooledDataId,shape:convInfo.outShape,dtype:x.dtype},{dataId:indexesDataId,shape:convInfo.outShape,dtype:"int32"}]}};function sum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"sum");let $x;x.dtype==="bool"?$x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"int32"}}):$x=identity2({inputs:{x},backend:backend2});let xRank=$x.shape.length,axes=util_exports.parseAxisParam(axis,$x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose2({inputs:{x:$x},backend:backend2,attrs:{perm:permutation}}),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank)),backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,permutedX.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(permutedX.shape,reductionAxes),resultDtype=backend_util_exports.upcastType(permutedX.dtype,"int32"),result=zeros3(backend2,outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=backend2.data.get(result.dataId).values,aVals=backend2.data.get(permutedX.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,sum6=0;for(let j=0;j<reduceSize;++j)sum6+=aVals[offset+j];vals[i]=sum6}if(keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(result.shape,axes),oldResult=result;result=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:newShape}}),backend2.disposeIntermediateTensorInfo(oldResult)}return backend2.disposeIntermediateTensorInfo($x),permutation!=null&&backend2.disposeIntermediateTensorInfo(permutedX),result}var sumConfig={kernelName:Sum,backendName:"cpu",kernelFunc:sum3};function mean2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs,axes=util_exports.parseAxisParam(axis,x.shape),reduceShape=backend_util_exports.computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=util_exports.sizeFromShape(reduceShape),toDispose=[],reduceSizeScalar=backend2.makeTensorInfo([],"float32",new Float32Array([reduceSize]));toDispose.push(reduceSizeScalar);let $x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}});toDispose.push($x);let res=div2({inputs:{a:$x,b:reduceSizeScalar},backend:backend2});toDispose.push(res);let result=sum3({inputs:{x:res},backend:backend2,attrs:{axis,keepDims}});return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var meanConfig={kernelName:Mean,backendName:"cpu",kernelFunc:mean2};function min3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"min");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("min",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value)}vals[i]=min6}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var minConfig={kernelName:Min,backendName:"cpu",kernelFunc:min3};function mirrorPad2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,mode}=attrs;assertNotComplex(x,"mirrorPad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),end=paddings.map((p2,i)=>p2[0]+x.shape[i]),offset=mode==="reflect"?0:1,xVals=backend2.data.get(x.dataId).values,xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);for(let i=0;i<resultSize;i++){let coords2=util_exports.indexToLoc(i,resultRank,resultStrides);for(let i2=0;i2<resultRank;i2++)coords2[i2]<start[i2]?coords2[i2]=start[i2]*2-coords2[i2]-offset:coords2[i2]>=end[i2]&&(coords2[i2]=(end[i2]-1)*2-coords2[i2]+offset);coords2=coords2.map((c,i2)=>c-start[i2]);let inIndex=util_exports.locToIndex(coords2,xRank,xStrides);resVals[i]=xVals[inIndex]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var mirrorPadConfig={kernelName:MirrorPad,backendName:"cpu",kernelFunc:mirrorPad2},modImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>{let rem=aValue%bValue;return aValue<0&&bValue<0||aValue>=0&&bValue>=0?rem:(rem+bValue)%bValue}),mod2=binaryKernelFunc(Mod,modImpl),modConfig={kernelName:Mod,backendName:"cpu",kernelFunc:mod2},seedrandom4=__toModule2(require_seedrandom2());function softmax3(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{dim}=attrs,logitsRank=logits.shape.length,$dim=dim;if($dim===-1&&($dim=logitsRank-1),$dim!==logitsRank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);let axes=util_exports.parseAxisParam([$dim],logits.shape),maxLogit=max3({inputs:{x:logits},backend:backend2,attrs:{reductionIndices:axes,keepDims:!1}}),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),maxLogitReshaped=reshape3({inputs:{x:maxLogit},backend:backend2,attrs:{shape:expandedShape}}),a=sub2({inputs:{a:logits,b:maxLogitReshaped},backend:backend2}),b=exp2({inputs:{x:a},backend:backend2}),sumExp=sum3({inputs:{x:b},backend:backend2,attrs:{axis:axes,keepDims:!1}}),sumReshaped=reshape3({inputs:{x:sumExp},backend:backend2,attrs:{shape:expandedShape}}),result=div2({inputs:{a:b,b:sumReshaped},backend:backend2});return backend2.disposeIntermediateTensorInfo(maxLogit),backend2.disposeIntermediateTensorInfo(maxLogitReshaped),backend2.disposeIntermediateTensorInfo(a),backend2.disposeIntermediateTensorInfo(b),backend2.disposeIntermediateTensorInfo(sumExp),backend2.disposeIntermediateTensorInfo(sumReshaped),result}var softmaxConfig={kernelName:Softmax,backendName:"cpu",kernelFunc:softmax3};function multinomial2(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{numSamples,seed,normalized}=attrs;assertNotComplex(logits,"multinomial");let probabilities=normalized?logits:softmax3({inputs:{logits},backend:backend2,attrs:{dim:-1}}),batchSize=probabilities.shape[0],numEvents=probabilities.shape[1],probVals=backend2.data.get(probabilities.dataId).values,resShape=[batchSize,numSamples],resVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape),"int32");for(let b=0;b<batchSize;++b){let offset=b*numEvents,cdf=new Float32Array(numEvents-1);cdf[0]=probVals[offset];for(let event=1;event<cdf.length;++event)cdf[event]=cdf[event-1]+probVals[offset+event];let random=seedrandom4.alea(seed.toString()),outOffset=b*numSamples;for(let sampleId=0;sampleId<numSamples;++sampleId){let r=random();resVals[outOffset+sampleId]=cdf.length;for(let event=0;event<cdf.length;event++)if(r<cdf[event]){resVals[outOffset+sampleId]=event;break}}}return normalized||backend2.disposeIntermediateTensorInfo(probabilities),backend2.makeTensorInfo(resShape,"int32",resVals)}var multinomialConfig={kernelName:Multinomial,backendName:"cpu",kernelFunc:multinomial2},nonMaxSuppressionV3Impl2=kernel_impls_exports.nonMaxSuppressionV3Impl;function nonMaxSuppressionV3(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold}=attrs;assertNotComplex(boxes,"NonMaxSuppression");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices}=nonMaxSuppressionV3Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices))}var nonMaxSuppressionV3Config={kernelName:NonMaxSuppressionV3,backendName:"cpu",kernelFunc:nonMaxSuppressionV3},nonMaxSuppressionV4Impl2=kernel_impls_exports.nonMaxSuppressionV4Impl;function nonMaxSuppressionV4(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs;assertNotComplex(boxes,"NonMaxSuppressionPadded");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([],"int32",new Int32Array([validOutputs]))]}var nonMaxSuppressionV4Config={kernelName:NonMaxSuppressionV4,backendName:"cpu",kernelFunc:nonMaxSuppressionV4},nonMaxSuppressionV5Impl2=kernel_impls_exports.nonMaxSuppressionV5Impl;function nonMaxSuppressionV5(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs;assertNotComplex(boxes,"NonMaxSuppressionWithScore");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl2(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([selectedScores.length],"float32",new Float32Array(selectedScores))]}var nonMaxSuppressionV5Config={kernelName:NonMaxSuppressionV5,backendName:"cpu",kernelFunc:nonMaxSuppressionV5};function oneHot2(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs;assertNotComplex(indices,"oneHot");let indicesSize=util_exports.sizeFromShape(indices.shape),res=new Float32Array(indicesSize*depth);res.fill(offValue);let indicesVal=backend2.data.get(indices.dataId).values;for(let event=0;event<indicesSize;++event)indicesVal[event]>=0&&indicesVal[event]<depth&&(res[event*depth+indicesVal[event]]=onValue);return backend2.makeTensorInfo([...indices.shape,depth],"int32",res)}var oneHotConfig={kernelName:OneHot,backendName:"cpu",kernelFunc:oneHot2};function zerosLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=zerosLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:0,dtype:x.dtype}})}var zerosLikeConfig={kernelName:ZerosLike,backendName:"cpu",kernelFunc:zerosLike2};function onesLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=onesLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:1,dtype:x.dtype}})}var onesLikeConfig={kernelName:OnesLike,backendName:"cpu",kernelFunc:onesLike2};function pack(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims3({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let intermediateTensorInfos=[],expandedTensors=inputs.map(t=>{let expandedT=expandDims3({inputs:{input:t},backend:backend2,attrs:{dim:axis}});return intermediateTensorInfos.push(expandedT),expandedT}),result=concat2({inputs:expandedTensors,backend:backend2,attrs:{axis}});return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var packConfig={kernelName:Pack,backendName:"cpu",kernelFunc:pack};function padV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,constantValue}=attrs;assertNotComplex(x,"pad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),xVals=backend2.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);constantValue!==0&&resVals.fill(constantValue);for(let i=0;i<xSize;i++){let outCoords=util_exports.indexToLoc(i,xRank,xStrides).map((c,i2)=>c+start[i2]),outIndex=util_exports.locToIndex(outCoords,resultRank,resultStrides);resVals[outIndex]=xVals[i]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var padV2Config={kernelName:PadV2,backendName:"cpu",kernelFunc:padV2},powImpl=createSimpleBinaryKernelImpl((a,b)=>Math.pow(a,b)),pow2=binaryKernelFunc(Pow,powImpl),powConfig={kernelName:Pow,backendName:"cpu",kernelFunc:pow2};function range3(args){let{backend:backend2,attrs}=args,{start,stop,dtype,step:step4}=attrs,values=rangeImpl(start,stop,step4,dtype);return backend2.makeTensorInfo([values.length],dtype,values)}var rangeConfig={kernelName:Range,backendName:"cpu",kernelFunc:range3},reciprocal2=unaryKernelFunc(Reciprocal,xi=>1/xi),reciprocalConfig={kernelName:Reciprocal,backendName:"cpu",kernelFunc:reciprocal2};function resizeBilinear2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeBilinear");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,result=new Float32Array(util_exports.sizeFromShape([batch,newHeight,newWidth,numChannels])),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],outputIdx=0,effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];for(let b=0;b<batch;b++)for(let r=0;r<newHeight;r++){let sourceFracRow;halfPixelCenters?sourceFracRow=effectiveRowSizeRatio*(r+.5)-.5:sourceFracRow=effectiveRowSizeRatio*r;let sourceRowFloor=Math.max(0,Math.floor(sourceFracRow)),rowFrac=sourceFracRow-sourceRowFloor,sourceRowCeil=Math.min(oldHeight-1,Math.ceil(sourceFracRow)),topRowOffset=b*imagesStrides[0]+sourceRowFloor*imagesStrides[1],botRowOffset=b*imagesStrides[0]+sourceRowCeil*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol;halfPixelCenters?sourceFracCol=effectiveColSizeRatio*(c+.5)-.5:sourceFracCol=effectiveColSizeRatio*c;let sourceColFloor=Math.max(0,Math.floor(sourceFracCol)),colFrac=sourceFracCol-sourceColFloor,sourceColCeil=Math.min(oldWidth-1,Math.ceil(sourceFracCol)),topLeftOffest=topRowOffset+sourceColFloor*imagesStrides[2],botLeftOffset=botRowOffset+sourceColFloor*imagesStrides[2],topRightOffset=topRowOffset+sourceColCeil*imagesStrides[2],botRightOffest=botRowOffset+sourceColCeil*imagesStrides[2];for(let d=0;d<numChannels;d++){let topLeft=xValues[topLeftOffest+d],bottomLeft=xValues[botLeftOffset+d],topRight=xValues[topRightOffset+d],bottomRight=xValues[botRightOffest+d],top=topLeft+(topRight-topLeft)*colFrac,bottom=bottomLeft+(bottomRight-bottomLeft)*colFrac,newValue=top+(bottom-top)*rowFrac;result[outputIdx++]=newValue}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],"float32",result)}var resizeBilinearConfig={kernelName:ResizeBilinear,backendName:"cpu",kernelFunc:resizeBilinear2};function resizeBilinearGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeBilinearGrad");let imagesStrides=util_exports.computeStrides(images.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],dyValues=backend2.data.get(dy.dataId).values,offset=0;for(let b=0;b<batch;b++){let bOffset=b*imagesStrides[0];for(let r=0;r<yHeight;r++){let dxR=r*heightScale,topDxRIndex=Math.floor(dxR),bottomDxRIndex=Math.min(Math.ceil(dxR),xHeight-1),topDxROffset=bOffset+topDxRIndex*imagesStrides[1],bottomDxROffset=bOffset+bottomDxRIndex*imagesStrides[1],dxRLerp=dxR-topDxRIndex,inverseDxRLerp=1-dxRLerp;for(let c=0;c<yWidth;c++){let dxC=c*widthScale,leftDxCIndex=Math.floor(dxC),rightDxCIndex=Math.min(Math.ceil(dxC),xWidth-1),dxCLerp=dxC-leftDxCIndex,inverseDxCLerp=1-dxCLerp,topLeftRCOffset=topDxROffset+leftDxCIndex*imagesStrides[2],topRightRCOffset=topDxROffset+rightDxCIndex*imagesStrides[2],bottomLeftRCOffset=bottomDxROffset+leftDxCIndex*imagesStrides[2],bottomRightRCOffset=bottomDxROffset+rightDxCIndex*imagesStrides[2],inverseDxRLerpTimesInverseDxCLerp=inverseDxRLerp*inverseDxCLerp,inverseDxRLerpTimesDxCLerp=inverseDxRLerp*dxCLerp,dxRLerpTimesInverseDxCLerp=dxRLerp*inverseDxCLerp,dxRLerpTimesDxCLerp=dxRLerp*dxCLerp;for(let d=0;d<depth;d++){let dyVal=dyValues[offset++];output[topLeftRCOffset+d]+=dyVal*inverseDxRLerpTimesInverseDxCLerp,output[topRightRCOffset+d]+=dyVal*inverseDxRLerpTimesDxCLerp,output[bottomLeftRCOffset+d]+=dyVal*dxRLerpTimesInverseDxCLerp,output[bottomRightRCOffset+d]+=dyVal*dxRLerpTimesDxCLerp}}}}return backend2.makeTensorInfo([batch,xWidth,xHeight,depth],"float32",output)}var resizeBilinearGradConfig2={kernelName:ResizeBilinearGrad,backendName:"cpu",kernelFunc:resizeBilinearGrad};function resizeNearestNeighbor2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeNearestNeighbor");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,output=new Float32Array(batch*newHeight*newWidth*numChannels),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1],outputOffset=0;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<newHeight;r++){let sourceFracRow=halfPixelCenters?effectiveRowSizeRatio*(r+.5):effectiveRowSizeRatio*r,sourceNearestRow=Math.min(oldHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));halfPixelCenters&&(sourceNearestRow=Math.max(0,sourceNearestRow));let rowOffset=batchOffset+sourceNearestRow*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=halfPixelCenters?effectiveColSizeRatio*(c+.5):effectiveColSizeRatio*c,sourceNearestCol=Math.min(oldWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));halfPixelCenters&&(sourceNearestCol=Math.max(0,sourceNearestCol));let colOffset=rowOffset+sourceNearestCol*imagesStrides[2];for(let d=0;d<numChannels;d++){let newVal=xValues[colOffset+d];output[outputOffset++]=newVal}}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],images.dtype,output)}var resizeNearestNeighborConfig={kernelName:ResizeNearestNeighbor,backendName:"cpu",kernelFunc:resizeNearestNeighbor2};function resizeNearestNeighborGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeNearestNeighborGrad");let imagesStrides=util_exports.computeStrides(images.shape),dyStrides=util_exports.computeStrides(dy.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),dyValues=backend2.data.get(dy.dataId).values,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<xHeight;r++){let rowOffset=batchOffset+r*imagesStrides[1],startRLerp=Math.floor(r*invHeightScale),startDyR=Math.floor(startRLerp-winHeight/2);for(let c=0;c<xWidth;c++){let colOffset=rowOffset+c*imagesStrides[2],startCLerp=Math.floor(c*invWidthScale),startDyC=Math.floor(startCLerp-winWidth/2);for(let d=0;d<depth;d++){let accum=0;for(let dyRIndex=0;dyRIndex<winHeight;dyRIndex++){let dyR=dyRIndex+startDyR;if(!(dyR<0||dyR>=yHeight)){let dyROffset=batchOffset+dyR*dyStrides[1],sourceFracRow=dyR*heightScale,sourceNearestRow=Math.min(xHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));if(r===sourceNearestRow)for(let dyCIndex=0;dyCIndex<winWidth;dyCIndex++){let dyC=dyCIndex+startDyC;if(!(dyC<0||dyC>=yWidth)){let dyCOffset=dyROffset+dyC*dyStrides[2],sourceFracCol=dyC*widthScale,sourceNearestCol=Math.min(xWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));c===sourceNearestCol&&(accum+=dyValues[dyCOffset+d])}}}}output[colOffset+d]=accum}}}}return backend2.makeTensorInfo(images.shape,images.dtype,output)}var resizeNearestNeighborGradConfig2={kernelName:ResizeNearestNeighborGrad,backendName:"cpu",kernelFunc:resizeNearestNeighborGrad};function reverse2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs;assertNotComplex(x,"reverse");let xRank=x.shape.length,$dims=util_exports.parseAxisParam(dims,x.shape);if(xRank===0)return identity2({inputs:{x},backend:backend2});let outBuf=new TensorBuffer(x.shape,x.dtype),xBuf=backend2.bufferSync(x);for(let i=0;i<outBuf.size;i++){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.slice();$dims.forEach(d=>inLoc[d]=x.shape[d]-1-inLoc[d]),outBuf.set(xBuf.get(...inLoc),...outLoc)}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var reverseConfig={kernelName:Reverse,backendName:"cpu",kernelFunc:reverse2},rotateWithOffsetConfig={kernelName:RotateWithOffset,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fullOpacityValue=255,sinFactor=Math.sin(radians),cosFactor=Math.cos(radians),imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],y=coords2[1],coordX=(x-centerX)*cosFactor-(y-centerY)*sinFactor,coordY=(x-centerX)*sinFactor+(y-centerY)*cosFactor;coordX=Math.round(coordX+centerX),coordY=Math.round(coordY+centerY);let outputValue=fillValue;if(typeof fillValue!="number"&&(channel===3?outputValue=fullOpacityValue:outputValue=fillValue[channel]),coordX>=0&&coordX<imageWidth&&coordY>=0&&coordY<imageHeight){let rotatedRowOffset=coordY*(imageWidth*numChannels),rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rotatedRowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}let outIdx=batchOffset+rowOffset+colOffset+channel;output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}},round3=unaryKernelFunc(Round,xi=>{let base2=Math.floor(xi);return xi-base2<.5?Math.floor(xi):xi-base2>.5?Math.ceil(xi):base2%2===0?base2:base2+1}),roundConfig={kernelName:Round,backendName:"cpu",kernelFunc:round3};function scatterImpl(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices){let flattenShape=[outputSize/sliceSize,sliceSize],indicesData=indices.values,updatesData=updates.values;if(outputSize===0)return buffer(shape,updates.dtype);let outBuf=buffer(flattenShape,updates.dtype);outBuf.values.fill(defaultValue);for(let i=0;i<numUpdates;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];index.push(dim),flattenIndex+=dim*strides[j]}if(flattenIndex<0||flattenIndex>=outputSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${shape}`);for(let k=0;k<sliceSize;k++)sumDupeIndices?outBuf.values[flattenIndex*sliceSize+k]+=updatesData[i*sliceSize+k]:outBuf.values[flattenIndex*sliceSize+k]=updates.rank===0?updatesData[0]:updatesData[i*sliceSize+k]}return outBuf}function scatterNd(args){let{inputs,backend:backend2,attrs}=args,{indices,updates}=inputs,{shape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),sumDupeIndices=!0,indicesBuf=backend2.bufferSync(indices),updatesBuf=backend2.bufferSync(updates),outBuf=scatterImpl(indicesBuf,updatesBuf,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,0,sumDupeIndices);return backend2.makeTensorInfo(shape,outBuf.dtype,outBuf.values)}var scatterNdConfig={kernelName:ScatterNd,backendName:"cpu",kernelFunc:scatterNd};function select(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs;assertNotComplex([condition,t,e],"select");let conditionRank=condition.shape.length,values=backend2.data.get(condition.dataId).values,tValues=backend2.data.get(t.dataId).values,eValues=backend2.data.get(e.dataId).values,resultDtype=upcastType(t.dtype,e.dtype),newValues=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape),resultDtype),index=0,offset=conditionRank===0||conditionRank>1||t.shape.length===1?1:util_exports.sizeFromShape(t.shape.slice(1));for(let i=0;i<values.length;i++)for(let j=0;j<offset;j++)values[i]===1?newValues[index++]=tValues[i]:newValues[index++]=eValues[i];return backend2.makeTensorInfo(t.shape,resultDtype,newValues)}var selectConfig={kernelName:Select,backendName:"cpu",kernelFunc:select},scaleAlpha=backend_util_exports.SELU_SCALEALPHA,scale=backend_util_exports.SELU_SCALE,selu2=unaryKernelFunc(Selu,xi=>xi>=0?scale*xi:scaleAlpha*(Math.exp(xi)-1)),seluConfig={kernelName:Selu,backendName:"cpu",kernelFunc:selu2},sigmoid2=unaryKernelFunc(Sigmoid,xi=>1/(1+Math.exp(-xi))),sigmoidConfig={kernelName:Sigmoid,backendName:"cpu",kernelFunc:sigmoid2},sign2=unaryKernelFunc(Sign,xi=>xi<0?-1:xi>0?1:0),signConfig={kernelName:Sign,backendName:"cpu",kernelFunc:sign2},sin2=unaryKernelFunc(Sin,xi=>Math.sin(xi)),sinConfig={kernelName:Sin,backendName:"cpu",kernelFunc:sin2},sinh2=unaryKernelFunc(Sinh,xi=>Math.sinh(xi)),sinhConfig={kernelName:Sinh,backendName:"cpu",kernelFunc:sinh2},epsilon2=11920928955078125e-23,threshold=Math.log(epsilon2)+2,softplus2=unaryKernelFunc(Softplus,xi=>{let tooLarge=xi>-threshold,tooSmall=xi<threshold,expX=Math.exp(xi),result;return tooSmall?result=expX:tooLarge?result=xi:result=Math.log(1+expX),result}),softplusConfig={kernelName:Softplus,backendName:"cpu",kernelFunc:softplus2};function spaceToBatchND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;assertNotComplex([x],"spaceToBatchND");let prod5=util_exports.sizeFromShape(blockShape),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=padV2Config.kernelFunc({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),paddedXReshaped=reshape3({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose2({inputs:{x:paddedXReshaped},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape3({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return backend2.disposeIntermediateTensorInfo(paddedX),backend2.disposeIntermediateTensorInfo(paddedXReshaped),backend2.disposeIntermediateTensorInfo(paddedXT),result}var spaceToBatchNDConfig={kernelName:SpaceToBatchND,backendName:"cpu",kernelFunc:spaceToBatchND2};function sparseToDense2(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,indicesBuf=backend2.bufferSync(sparseIndices),updatesBuf=backend2.bufferSync(sparseValues),$defaultValue=backend2.data.get(defaultValue.dataId).values[0],outBuf=scatterImpl(indicesBuf,updatesBuf,outputShape,outputSize,sliceSize,numUpdates,sliceRank,strides,$defaultValue,sumDupeIndices);return backend2.makeTensorInfo(outputShape,outBuf.dtype,outBuf.values)}var sparseToDenseConfig={kernelName:SparseToDense,backendName:"cpu",kernelFunc:sparseToDense2};function splitV(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[$axis]=s;let sliceT=slice2({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[$axis]+=s,sliceT})}var splitVConfig={kernelName:SplitV,backendName:"cpu",kernelFunc:splitV},sqrt2=unaryKernelFunc(Sqrt,xi=>Math.sqrt(xi)),sqrtConfig={kernelName:Sqrt,backendName:"cpu",kernelFunc:sqrt2},squareConfig={kernelName:Square,backendName:"cpu",kernelFunc:({inputs,backend:backend2})=>{let{x}=inputs,cpuBackend=backend2;assertNotComplex(x,"square");let values=cpuBackend.data.get(x.dataId).values,newValues=new Float32Array(values.length);for(let i=0;i<values.length;++i){let value=values[i];newValues[i]=value*value}return{dataId:cpuBackend.write(newValues,x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}},step2=unaryKernelFunc(Step,(xi,attrs)=>{let stepAttrs=attrs;return isNaN(xi)?NaN:xi>0?1:stepAttrs.alpha}),stepConfig={kernelName:Step,backendName:"cpu",kernelFunc:step2};function stridedSlice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs;assertNotComplex(x,"stridedSlice");let{nonStrided,$begin,$strides,size,newShape,outShape}=slice_util_exports.sliceInfo(x.shape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask),$x=reshape3({inputs:{x},backend:backend2,attrs:{shape:newShape}}),result;if(nonStrided){let sliced=slice2({inputs:{x:$x},backend:backend2,attrs:{begin:$begin,size}});result=reshape3({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(sliced)}else if(outShape.some(axis=>axis===0))result=backend2.makeTensorInfo(outShape,x.dtype,[]);else{let xBuf=backend2.bufferSync($x),outBuf=stridedSliceImpl(outShape,xBuf,$strides,$begin);result=backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:outShape}});return backend2.disposeIntermediateTensorInfo($x),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var stridedSliceConfig={kernelName:StridedSlice,backendName:"cpu",kernelFunc:stridedSlice2},tan2=unaryKernelFunc(Tan,xi=>Math.tan(xi)),tanConfig={kernelName:Tan,backendName:"cpu",kernelFunc:tan2},tanh3=unaryKernelFunc(Tanh,xi=>Math.tanh(xi)),tanhConfig={kernelName:Tanh,backendName:"cpu",kernelFunc:tanh3};function tile3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reps}=attrs;assertNotComplex(x,"tile");let outBuf=tileImpl(backend2.bufferSync(x),reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var tileConfig={kernelName:Tile,backendName:"cpu",kernelFunc:tile3};function topK(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs;assertNotComplex(x,"topk");let xVals=backend2.data.get(x.dataId).values,[allTopKVals,allTopKIndices]=topKImpl(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig={kernelName:TopK,backendName:"cpu",kernelFunc:topK};function unique3(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex(x,"unique");let values=backend2.data.get(x.dataId).values,{outputValues,outputShape,indices}=uniqueImpl(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig={kernelName:Unique,backendName:"cpu",kernelFunc:unique3};function unpack(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let valueRank=value.shape.length,num=value.shape[axis],outShape=new Array(valueRank-1),outIndex=0;for(let i=0;i<valueRank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let begin=new Array(valueRank).fill(0),size=value.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let tempRes=slice2({inputs:{x:value},backend:backend2,attrs:{begin,size}});res[i]=reshape3({inputs:{x:tempRes},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(tempRes)}return res}var unpackConfig={kernelName:Unpack,backendName:"cpu",kernelFunc:unpack};function unsortedSegmentSum2(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs;assertNotComplex(x,"unsortedSegmentSum");let xRank=x.shape.length,segmentIdsRank=segmentIds.shape.length,res=[],intermediates=[],numIters=xRank-segmentIdsRank,$segmentIds=segmentIds;for(let i=0;i<numIters;++i){let expanded=expandDims3({inputs:{input:$segmentIds},backend:backend2,attrs:{dim:i+1}});$segmentIds=expanded,intermediates.push(expanded)}for(let i=0;i<numSegments;++i){let scalarValue=util_exports.createScalarValue(i,"int32"),segmentId=backend2.makeTensorInfo([],"int32",scalarValue),mask=equal2({inputs:{a:segmentId,b:$segmentIds},backend:backend2}),maskCasted=cast3({inputs:{x:mask},backend:backend2,attrs:{dtype:"float32"}}),mul2=multiply2({inputs:{a:maskCasted,b:x},backend:backend2}),sumTensorInfo=sum3({inputs:{x:mul2},backend:backend2,attrs:{axis:0,keepDims:!1}});res.push(sumTensorInfo),intermediates.push(segmentId),intermediates.push(mask),intermediates.push(maskCasted),intermediates.push(mul2),intermediates.push(sumTensorInfo)}let result=pack({inputs:res,backend:backend2,attrs:{axis:0}});return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig={kernelName:UnsortedSegmentSum,backendName:"cpu",kernelFunc:unsortedSegmentSum2},kernelConfigs=[_fusedMatMulConfig,absConfig,acosConfig,acoshConfig,addConfig,addNConfig,allConfig,anyConfig,argMaxConfig,argMinConfig,asinConfig,asinhConfig,atanConfig,atan2Config,atanhConfig,avgPoolConfig,avgPool3DConfig,avgPool3DGradConfig2,avgPoolGradConfig2,batchMatMulConfig,batchNormConfig,batchToSpaceNDConfig,bincountConfig,castConfig,ceilConfig,clipConfig,complexConfig,complexAbsConfig,concatConfig,conv2DBackpropFilterConfig,conv2DBackpropInputConfig,conv2DConfig,conv3DBackpropFilterV2Config,conv3DBackpropInputV2Config,conv3DConfig,cosConfig,coshConfig,cropAndResizeConfig,cumsumConfig,denseBincountConfig,depthToSpaceConfig,depthwiseConv2dNativeConfig,depthwiseConv2dNativeBackpropFilterConfig,depthwiseConv2dNativeBackpropInputConfig,diagConfig,dilation2dConfig,dilation2dBackpropInputConfig,dilation2dBackpropFilterConfig,realDivConfig,eluConfig,eluGradConfig2,equalConfig,erfConfig,expConfig,expandDimsConfig,expm1Config,fftConfig,fillConfig,flipLeftRightConfig,floorConfig,floorDivConfig,fusedConv2DConfig,fusedDepthwiseConv2DConfig,gatherNdConfig,gatherV2Config,greaterConfig,greaterEqualConfig,identityConfig,ifftConfig,imagConfig,isFiniteConfig,isInfConfig,isNaNConfig,leakyReluConfig,lessConfig,lessEqualConfig,linSpaceConfig,logConfig,log1pConfig,logicalAndConfig,logicalNotConfig,logicalOrConfig,lRNConfig,lRNGradConfig,maximumConfig,maxPoolConfig,maxPool3DConfig,maxPool3DGradConfig2,maxPoolGradConfig2,maxPoolWithArgmaxConfig,maxConfig,meanConfig,minConfig,minimumConfig,mirrorPadConfig,modConfig,multinomialConfig,multiplyConfig,negConfig,nonMaxSuppressionV3Config,nonMaxSuppressionV4Config,nonMaxSuppressionV5Config,notEqualConfig,oneHotConfig,onesLikeConfig,packConfig,padV2Config,powConfig,preluConfig,prodConfig,rangeConfig,realConfig,reciprocalConfig,reluConfig,relu6Config,reshapeConfig,resizeBilinearConfig,resizeBilinearGradConfig2,resizeNearestNeighborConfig,resizeNearestNeighborGradConfig2,reverseConfig,rotateWithOffsetConfig,roundConfig,rsqrtConfig,scatterNdConfig,selectConfig,seluConfig,sigmoidConfig,signConfig,sinConfig,sinhConfig,sliceConfig,softmaxConfig,softplusConfig,spaceToBatchNDConfig,sparseToDenseConfig,splitVConfig,sqrtConfig,squareConfig,squaredDifferenceConfig,stepConfig,stridedSliceConfig,subConfig,sumConfig,tanConfig,tanhConfig,tileConfig,topKConfig,transposeConfig,uniqueConfig,unpackConfig,unsortedSegmentSumConfig,zerosLikeConfig];for(let kernelConfig of kernelConfigs)registerKernel(kernelConfig);var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(webGLVersion,gl){contexts[webGLVersion]=gl}function getWebGLContext(webGLVersion){if(!(webGLVersion in contexts)){let newCtx=getWebGLRenderingContext(webGLVersion);if(newCtx!==null)contexts[webGLVersion]=newCtx;else return console.log("Could not get context for WebGL version",webGLVersion),null}let gl=contexts[webGLVersion];return gl.isContextLost()?(delete contexts[webGLVersion],getWebGLContext(webGLVersion)):(gl.disable(gl.DEPTH_TEST),gl.disable(gl.STENCIL_TEST),gl.disable(gl.BLEND),gl.disable(gl.DITHER),gl.disable(gl.POLYGON_OFFSET_FILL),gl.disable(gl.SAMPLE_COVERAGE),gl.enable(gl.SCISSOR_TEST),gl.enable(gl.CULL_FACE),gl.cullFace(gl.BACK),contexts[webGLVersion])}function createCanvas(webGLVersion){if(typeof OffscreenCanvas!="undefined"&&webGLVersion===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function getWebGLRenderingContext(webGLVersion){if(webGLVersion!==1&&webGLVersion!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let canvas=createCanvas(webGLVersion);return canvas.addEventListener("webglcontextlost",ev=>{ev.preventDefault(),delete contexts[webGLVersion]},!1),webGLVersion===1?canvas.getContext("webgl",WEBGL_ATTRIBUTES)||canvas.getContext("experimental-webgl",WEBGL_ATTRIBUTES):canvas.getContext("webgl2",WEBGL_ATTRIBUTES)}var PackingScheme;(function(PackingScheme2){PackingScheme2[PackingScheme2.DENSE=0]="DENSE",PackingScheme2[PackingScheme2.SHARED_BATCH=1]="SHARED_BATCH"})(PackingScheme||(PackingScheme={}));var TextureUsage;(function(TextureUsage2){TextureUsage2[TextureUsage2.RENDER=0]="RENDER",TextureUsage2[TextureUsage2.UPLOAD=1]="UPLOAD",TextureUsage2[TextureUsage2.PIXELS=2]="PIXELS",TextureUsage2[TextureUsage2.DOWNLOAD=3]="DOWNLOAD"})(TextureUsage||(TextureUsage={}));var PhysicalTextureType;(function(PhysicalTextureType2){PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(PhysicalTextureType||(PhysicalTextureType={}));function getUnpackedMatrixTextureShapeWidthHeight(rows,columns){return[columns,rows]}function getUnpackedArraySizeFromMatrixSize(matrixSize,channelsPerTexture){return matrixSize*channelsPerTexture}function getDenseTexShape(shape){let size=util_exports.sizeFromShape(shape),texelsNeeded=Math.ceil(size/4);return util_exports.sizeToSquarishShape(texelsNeeded)}function getPackedMatrixTextureShapeWidthHeight(rows,columns){return[Math.max(1,Math.ceil(columns/2)),Math.max(1,Math.ceil(rows/2))]}function getPackedRGBAArraySizeFromMatrixShape(rows,columns){let[w,h]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return w*h*4}function getTextureConfig(gl,textureHalfFloatExtension){let glany=gl,internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat;return env().getNumber("WEBGL_VERSION")===2?(internalFormatFloat=glany.R32F,internalFormatHalfFloat=glany.R16F,internalFormatPackedHalfFloat=glany.RGBA16F,internalFormatPackedFloat=glany.RGBA32F,textureFormatFloat=glany.RED,downloadUnpackNumChannels=4,defaultNumChannels=1,textureTypeHalfFloat=glany.HALF_FLOAT,textureTypeFloat=glany.FLOAT):(internalFormatFloat=gl.RGBA,internalFormatHalfFloat=gl.RGBA,internalFormatPackedHalfFloat=gl.RGBA,internalFormatPackedFloat=glany.RGBA,textureFormatFloat=gl.RGBA,downloadUnpackNumChannels=4,defaultNumChannels=4,textureTypeHalfFloat=textureHalfFloatExtension!=null?textureHalfFloatExtension.HALF_FLOAT_OES:null,textureTypeFloat=gl.FLOAT),downloadTextureFormat=gl.RGBA,{internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat}}function callAndCheck(gl,func2){let returnValue=func2();return env().getBool("DEBUG")&&checkWebGLError(gl),returnValue}function checkWebGLError(gl){let error=gl.getError();if(error!==gl.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(gl,error))}var MIN_FLOAT16=596e-10,MAX_FLOAT16=65504;function canBeRepresented(num){return!!(env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||num===0||MIN_FLOAT16<Math.abs(num)&&Math.abs(num)<MAX_FLOAT16)}function getWebGLErrorMessage(gl,status){switch(status){case gl.NO_ERROR:return"NO_ERROR";case gl.INVALID_ENUM:return"INVALID_ENUM";case gl.INVALID_VALUE:return"INVALID_VALUE";case gl.INVALID_OPERATION:return"INVALID_OPERATION";case gl.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case gl.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case gl.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${status}`}}function getExtensionOrThrow(gl,extensionName){return throwIfNull(gl,()=>gl.getExtension(extensionName),'Extension "'+extensionName+'" not supported on this browser.')}function createVertexShader(gl,vertexShaderSource){let vertexShader=throwIfNull(gl,()=>gl.createShader(gl.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(vertexShader,vertexShaderSource)),callAndCheck(gl,()=>gl.compileShader(vertexShader)),gl.getShaderParameter(vertexShader,gl.COMPILE_STATUS)===!1)throw console.log(gl.getShaderInfoLog(vertexShader)),new Error("Failed to compile vertex shader.");return vertexShader}function createFragmentShader(gl,fragmentShaderSource){let fragmentShader=throwIfNull(gl,()=>gl.createShader(gl.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(fragmentShader,fragmentShaderSource)),callAndCheck(gl,()=>gl.compileShader(fragmentShader)),gl.getShaderParameter(fragmentShader,gl.COMPILE_STATUS)===!1)throw logShaderSourceAndInfoLog(fragmentShaderSource,gl.getShaderInfoLog(fragmentShader)),new Error("Failed to compile fragment shader.");return fragmentShader}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(shaderSource,shaderInfoLog){let lineNumberRegexResult=lineNumberRegex.exec(shaderInfoLog);if(lineNumberRegexResult==null){console.log(`Couldn't parse line number in error: ${shaderInfoLog}`),console.log(shaderSource);return}let lineNumber=+lineNumberRegexResult[1],shaderLines=shaderSource.split(`
+============================`));let dataId={};return this.data.set(dataId,{values,dtype,refCount:1}),dataId}makeTensorInfo(shape,dtype,values){let outId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));outId=this.write(encodedValues,shape,dtype)}else outId=this.write(values,shape,dtype);return{dataId:outId,shape,dtype}}incRef(dataId){let tensorData=this.data.get(dataId);tensorData.refCount++}decRef(dataId){if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--}}move(dataId,values,shape,dtype){this.data.set(dataId,{values,dtype,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{dtype,complexTensorInfos}=this.data.get(dataId);if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);return backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}return this.data.get(dataId).values}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}makeOutput(values,shape,dtype){let dataId=this.write(values,shape,dtype);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}disposeData(dataId){if(this.data.has(dataId)){let{complexTensorInfos}=this.data.get(dataId);complexTensorInfos!=null&&(this.disposeData(complexTensorInfos.real.dataId),this.disposeData(complexTensorInfos.imag.dataId)),this.data.delete(dataId)}}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--,tensorData.refCount<1&&this.disposeData(dataId)}}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(condition){assertNotComplex([condition],"where");let condVals=this.readSync(condition.dataId);return whereImpl2(condition.shape,condVals)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},shared_exports={};__export2(shared_exports,{addImpl:()=>addImpl,bincountImpl:()=>bincountImpl,bincountReduceImpl:()=>bincountReduceImpl,ceilImpl:()=>ceilImpl,concatImpl:()=>concatImpl,expImpl:()=>expImpl,expm1Impl:()=>expm1Impl,floorImpl:()=>floorImpl,gatherV2Impl:()=>gatherV2Impl,greaterImpl:()=>greaterImpl,lessImpl:()=>lessImpl,linSpaceImpl:()=>linSpaceImpl,logImpl:()=>logImpl,maxImpl:()=>maxImpl,maximumImpl:()=>maximumImpl,minimumImpl:()=>minimumImpl,multiplyImpl:()=>multiplyImpl,negImpl:()=>negImpl,notEqualImpl:()=>notEqualImpl,prodImpl:()=>prodImpl,rangeImpl:()=>rangeImpl,rsqrtImpl:()=>rsqrtImpl,simpleAbsImpl:()=>simpleAbsImpl,sliceImpl:()=>sliceImpl,squaredDifferenceImpl:()=>squaredDifferenceImpl,stridedSliceImpl:()=>stridedSliceImpl,subImpl:()=>subImpl,tileImpl:()=>tileImpl,topKImpl:()=>topKImpl,transposeImpl:()=>transposeImpl,uniqueImpl:()=>uniqueImpl});function simpleAbsImpl(vals){let resultValues=new Float32Array(vals.length);for(let i=0;i<vals.length;++i)resultValues[i]=Math.abs(vals[i]);return resultValues}var abs2=args=>{let{x}=args.inputs,cpuBackend=args.backend;assertNotComplex(x,"abs");let resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),values=cpuBackend.data.get(x.dataId).values;return resultValues=simpleAbsImpl(values),cpuBackend.makeOutput(resultValues,x.shape,"float32")},absConfig={kernelName:Abs,backendName:"cpu",kernelFunc:abs2};function createSimpleBinaryKernelImpl(op2){return(aShape,bShape,aVals,bVals,dtype)=>{let newShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultRank=newShape.length,resultStrides=util_exports.computeStrides(newShape),resultSize=util_exports.sizeFromShape(newShape),result=util_exports.getTypedArrayFromDType(dtype,resultSize),aRank=aShape.length,bRank=bShape.length,aStrides=util_exports.computeStrides(aShape),bStrides=util_exports.computeStrides(bShape),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,newShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<result.length;++i)result[i]=op2(aVals[i%aVals.length],bVals[i%bVals.length]);else for(let i=0;i<result.length;++i){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides);result[i]=op2(aVals[aIndex],bVals[bIndex])}return[result,newShape]}}function complex2(args){let{inputs,backend:backend2}=args,{real:real4,imag:imag4}=inputs,realVals=backend2.data.get(real4.dataId).values,imagVals=backend2.data.get(imag4.dataId).values,complexInfo=backend2.makeTensorInfo(real4.shape,"complex64"),complex4=backend2.data.get(complexInfo.dataId);return complex4.complexTensorInfos={real:backend2.makeTensorInfo(real4.shape,"float32",realVals),imag:backend2.makeTensorInfo(imag4.shape,"float32",imagVals)},complexInfo}var complexConfig={kernelName:Complex,backendName:"cpu",kernelFunc:complex2};function zeros3(backend2,shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros3(backend2,shape,"float32"),imag4=zeros3(backend2,shape,"float32");return complex2({inputs:{real:real4,imag:imag4},backend:backend2})}let values=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape),dtype);return backend2.makeTensorInfo(shape,dtype,values)}function identity2(args){let{inputs,backend:backend2}=args,{x}=inputs;return backend2.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig={kernelName:Identity,backendName:"cpu",kernelFunc:identity2};function real2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,real4=backend2.data.get(input2.dataId).complexTensorInfos.real,realVal=backend2.data.get(real4.dataId).values;return backend2.makeTensorInfo(real4.shape,real4.dtype,realVal)}var realConfig={kernelName:Real,backendName:"cpu",kernelFunc:real2};function cast3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity2({inputs:{x},backend:backend2});let zerosTensorInfo=zeros3(backend2,x.shape,x.dtype),floatX=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex2({inputs:{real:floatX,imag:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),result=cast3({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity2({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32"){let values=backend2.data.get(x.dataId).values,resultValues=Int32Array.from(values);return backend2.makeTensorInfo(x.shape,"int32",resultValues)}if(dtype==="bool"){let xVals=backend2.data.get(x.dataId).values,zero=util_exports.toTypedArray([0],x.dtype),[resultData,resultShape]=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0)(x.shape,[],xVals,zero,"bool");return backend2.makeTensorInfo(resultShape,"bool",resultData)}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig={kernelName:Cast,backendName:"cpu",kernelFunc:cast3};function binaryKernelFunc(name,simpleImpl,complexImpl,dtype){return complexImpl==null?({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;assertNotComplex([a,b],name);let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}:({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;if(a.dtype==="complex64"||b.dtype==="complex64"){let $aComplex=cast3({inputs:{x:a},backend:cpuBackend,attrs:{dtype:"complex64"}}),$aComplexVals=cpuBackend.data.get($aComplex.dataId),aReal=$aComplexVals.complexTensorInfos.real,aImag=$aComplexVals.complexTensorInfos.imag,aRealVals=cpuBackend.data.get(aReal.dataId).values,aImagVals=cpuBackend.data.get(aImag.dataId).values,$bComplex=cast3({inputs:{x:b},backend:cpuBackend,attrs:{dtype:"complex64"}}),$bComplexVals=cpuBackend.data.get($bComplex.dataId),bReal=$bComplexVals.complexTensorInfos.real,bImag=$bComplexVals.complexTensorInfos.imag,bRealVals=cpuBackend.data.get(bReal.dataId).values,bImagVals=cpuBackend.data.get(bImag.dataId).values,[resultRealData,resultImagData,resultShape]=complexImpl(a.shape,b.shape,aRealVals,aImagVals,bRealVals,bImagVals),resultReal=cpuBackend.makeTensorInfo(resultShape,"float32",resultRealData),resultImag=cpuBackend.makeTensorInfo(resultShape,"float32",resultImagData),result=complex2({inputs:{real:resultReal,imag:resultImag},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($aComplex),cpuBackend.disposeIntermediateTensorInfo($bComplex),cpuBackend.disposeIntermediateTensorInfo(resultReal),cpuBackend.disposeIntermediateTensorInfo(resultImag),result}else{let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}}}function createComplexBinaryKernelImpl(op2){return(aShape,bShape,aRealVals,aImagVals,bRealVals,bImagVals)=>{let resultShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultSize=util_exports.sizeFromShape(resultShape),resultRank=resultShape.length,resultStrides=util_exports.computeStrides(resultShape),resultRealVals=util_exports.getTypedArrayFromDType("float32",resultSize),resultImagVals=util_exports.getTypedArrayFromDType("float32",resultSize),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,resultShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,resultShape),aVals=backend_util_exports.mergeRealAndImagArrays(aRealVals,aImagVals),bVals=backend_util_exports.mergeRealAndImagArrays(bRealVals,bImagVals),aRank=aShape.length,aStrides=util_exports.computeStrides(aShape),bRank=bShape.length,bStrides=util_exports.computeStrides(bShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resultRealVals.length;i++){let aIdx=i%aVals.length,bIdx=i%bVals.length,result=op2(aVals[aIdx*2],aVals[aIdx*2+1],bVals[bIdx*2],bVals[bIdx*2+1]);resultRealVals[i]=result.real,resultImagVals[i]=result.imag}else for(let i=0;i<resultRealVals.length;i++){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides),opResult=op2(aVals[aIndex*2],aVals[aIndex*2+1],bVals[bIndex*2],bVals[bIndex*2+1]);resultRealVals[i]=opResult.real,resultImagVals[i]=opResult.imag}return[resultRealVals,resultImagVals,resultShape]}}var addImpl=createSimpleBinaryKernelImpl((a,b)=>a+b),addComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal+bReal,imag:aImag+bImag})),add4=binaryKernelFunc(Add,addImpl,addComplexImpl),addConfig={kernelName:Add,backendName:"cpu",kernelFunc:add4};function bincountImpl(xVals,weightsVals,weightsDtype,weightsShape,size){let weightsSize=util_exports.sizeFromShape(weightsShape),outVals=util_exports.makeZerosTypedArray(size,weightsDtype);for(let i=0;i<xVals.length;i++){let value=xVals[i];if(value<0)throw new Error("Input x must be non-negative!");value>=size||(weightsSize>0?outVals[value]+=weightsVals[i]:outVals[value]+=1)}return outVals}function bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput=!1){let numRows=xBuf.shape[0],numCols=xBuf.shape[1],outBuf=buffer([numRows,size],weightsBuf.dtype);for(let i=0;i<numRows;i++)for(let j=0;j<numCols;j++){let value=xBuf.get(i,j);if(value<0)throw new Error("Input x must be non-negative!");value>=size||(binaryOutput?outBuf.set(1,i,value):weightsBuf.size>0?outBuf.set(outBuf.get(i,value)+weightsBuf.get(i,j),i,value):outBuf.set(outBuf.get(i,value)+1,i,value))}return outBuf}function createSimpleUnaryImpl(op2){return(values,dtype,attrs)=>{let newValues=util_exports.getTypedArrayFromDType(dtype,values.length);for(let i=0;i<values.length;++i)newValues[i]=op2(values[i],attrs);return newValues}}function unaryKernelFunc(name,op2,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),$dtype=dtype||x.dtype,newValues=util_exports.getArrayFromDType($dtype,xSize);for(let i=0;i<xSize;++i)newValues[i]=op2(values[i],attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}function unaryKernelFuncFromImpl(name,unaryImpl,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,$dtype=dtype||x.dtype,newValues=unaryImpl(values,$dtype,attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}var ceilImpl=createSimpleUnaryImpl(xi=>Math.ceil(xi)),ceil2=unaryKernelFuncFromImpl(Ceil,ceilImpl),ceilConfig={kernelName:Ceil,backendName:"cpu",kernelFunc:ceil2};function concatImpl(inputs,outShape,dtype,simplyConcat){let outVals=util_exports.getArrayFromDType(dtype,util_exports.sizeFromShape(outShape));if(simplyConcat&&dtype!=="string"){let offset=0;inputs.forEach(input2=>{let size=util_exports.sizeFromShape(input2.shape);outVals.set(input2.vals,offset),offset+=size})}else{let colOffset=0;inputs.forEach(input2=>{let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(input2.vals):input2.vals,tIdx=0;for(let row=0;row<input2.shape[0];++row){let resIdx=row*outShape[1]+colOffset;for(let col=0;col<input2.shape[1];++col)outVals[resIdx+col]=decodedData[tIdx++]}colOffset+=input2.shape[1]})}return outVals}var expImpl=createSimpleUnaryImpl(xi=>Math.exp(xi)),exp2=unaryKernelFuncFromImpl(Exp,expImpl),expConfig={kernelName:Exp,backendName:"cpu",kernelFunc:exp2},expm1Impl=createSimpleUnaryImpl(xi=>Math.expm1(xi)),expm12=unaryKernelFuncFromImpl(Expm1,expm1Impl),expm1Config={kernelName:Expm1,backendName:"cpu",kernelFunc:expm12},floorImpl=createSimpleUnaryImpl(xi=>Math.floor(xi)),floor2=unaryKernelFuncFromImpl(Floor,floorImpl),floorConfig={kernelName:Floor,backendName:"cpu",kernelFunc:floor2};function gatherV2Impl(xBuf,indicesBuf,flattenOutputShape){let outBuf=buffer(flattenOutputShape,xBuf.dtype);for(let i=0;i<outBuf.size;++i){let originalLoc=outBuf.indexToLoc(i).slice(),batchIdx=originalLoc[0],indicesIdx=originalLoc[2],indicesIndex=indicesBuf.locToIndex([batchIdx,indicesIdx]);originalLoc[2]=indicesBuf.values[indicesIndex];let originalIndex=xBuf.locToIndex(originalLoc);outBuf.values[i]=xBuf.values[originalIndex]}return outBuf}var greaterImpl=createSimpleBinaryKernelImpl((a,b)=>a>b?1:0),greater3=binaryKernelFunc(Greater,greaterImpl,null,"bool"),greaterConfig={kernelName:Greater,backendName:"cpu",kernelFunc:greater3},lessImpl=createSimpleBinaryKernelImpl((a,b)=>a<b?1:0),less3=binaryKernelFunc(Less,lessImpl,null,"bool"),lessConfig={kernelName:Less,backendName:"cpu",kernelFunc:less3};function linSpaceImpl(start,stop,num){let step4=(stop-start)/(num-1),values=util_exports.makeZerosTypedArray(num,"float32");values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var logImpl=createSimpleUnaryImpl(xi=>Math.log(xi)),log3=unaryKernelFuncFromImpl(Log,logImpl),logConfig={kernelName:Log,backendName:"cpu",kernelFunc:log3};function maxImpl(aVals,reduceSize,outShape,dtype){let vals=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(outShape));for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value)}vals[i]=max6}return vals}var maximumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.max(aValue,bValue)),maximum3=binaryKernelFunc(Maximum,maximumImpl),maximumConfig={kernelName:Maximum,backendName:"cpu",kernelFunc:maximum3},minimumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.min(aValue,bValue)),minimum3=binaryKernelFunc(Minimum,minimumImpl),minimumConfig={kernelName:Minimum,backendName:"cpu",kernelFunc:minimum3},multiplyImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue*bValue),multiplyComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal*bReal-aImag*bImag,imag:aReal*bImag+aImag*bReal})),multiply2=binaryKernelFunc(Multiply,multiplyImpl,multiplyComplexImpl),multiplyConfig={kernelName:Multiply,backendName:"cpu",kernelFunc:multiply2};function negImpl(xVals,xShape,xDtype){let minusOne=util_exports.createScalarValue(-1,xDtype);return multiplyImpl([],xShape,minusOne,xVals,xDtype)}function neg2(args){let{inputs,backend:backend2}=args,{x}=inputs;assertNotComplex(x,"neg");let xVals=backend2.data.get(x.dataId).values,[res,newShape]=negImpl(xVals,x.shape,x.dtype);return backend2.makeTensorInfo(newShape,x.dtype,res)}var negConfig={kernelName:Neg,backendName:"cpu",kernelFunc:neg2},notEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0),notEqual2=binaryKernelFunc(NotEqual,notEqualImpl,null,"bool"),notEqualConfig={kernelName:NotEqual,backendName:"cpu",kernelFunc:notEqual2};function transposeImpl(xVals,xShape,dtype,perm,newShape){let xRank=xShape.length,xSize=util_exports.sizeFromShape(xShape),xStrides=util_exports.computeStrides(xShape),newStrides=util_exports.computeStrides(newShape),result=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(newShape));for(let i=0;i<xSize;++i){let loc=util_exports.indexToLoc(i,xRank,xStrides),newLoc=new Array(loc.length);for(let i2=0;i2<newLoc.length;i2++)newLoc[i2]=loc[perm[i2]];let newIndex=util_exports.locToIndex(newLoc,xRank,newStrides);result[newIndex]=xVals[i]}return result}function transpose2(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{perm}=attrs;assertNotComplex(x,"transpose");let xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let values=backend2.data.get(x.dataId).values,result=transposeImpl(values,x.shape,x.dtype,perm,newShape);return{dataId:backend2.write(result,newShape,x.dtype),shape:newShape,dtype:x.dtype}}var transposeConfig={kernelName:Transpose,backendName:"cpu",kernelFunc:transpose2};function prodImpl(xShape,xDtype,xVals,reductionAxes){let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,reductionAxes),outDtype=upcastType(xDtype,"int32"),outVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),outDtype),reduceSize=util_exports.sizeFromShape(reduceShape);for(let i=0;i<outVals.length;++i){let offset=i*reduceSize,prod5=1;for(let j=0;j<reduceSize;++j)prod5*=xVals[offset+j];outVals[i]=prod5}return{outVals,outShape,outDtype}}function prod2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"prod");let xRank=x.shape.length,axes=util_exports.parseAxisParam(axis,x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=x,intermediateTensorInfos=[];permutation!=null&&(permutedX=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}),intermediateTensorInfos.push(permutedX),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank));let xVals=backend2.data.get(permutedX.dataId).values,{outVals,outShape,outDtype}=prodImpl(permutedX.shape,permutedX.dtype,xVals,reductionAxes),resultShape=outShape;return keepDims&&(resultShape=backend_util_exports.expandShapeToKeepDim(outShape,axes)),intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(resultShape,outDtype,outVals)}var prodConfig={kernelName:Prod,backendName:"cpu",kernelFunc:prod2};function rangeImpl(start,stop,step4,dtype){let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step4<0,decreasingRangePositiveStep=stop<start&&step4>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return util_exports.makeZerosTypedArray(0,dtype);let numElements=Math.abs(Math.ceil((stop-start)/step4)),values=util_exports.makeZerosTypedArray(numElements,dtype);stop<start&&step4===1&&(step4=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var rsqrtImpl=createSimpleUnaryImpl(xi=>1/Math.sqrt(xi)),rsqrt2=unaryKernelFuncFromImpl(Rsqrt,rsqrtImpl),rsqrtConfig={kernelName:Rsqrt,backendName:"cpu",kernelFunc:rsqrt2};function sliceImpl(vals,begin,size,shape,dtype){let isContinous=slice_util_exports.isSliceContinous(shape,begin,size),length=util_exports.sizeFromShape(size),xStrides=util_exports.computeStrides(shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin,xStrides);return dtype==="string"?vals.slice(flatOffset,flatOffset+length):vals.subarray(flatOffset,flatOffset+length)}let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(vals):vals,inBuf=buffer(shape,dtype,decodedData),outBuf=buffer(size,dtype);for(let i=0;i<outBuf.size;++i){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.map((idx,j)=>idx+begin[j]);outBuf.set(inBuf.get(...inLoc),...outLoc)}return dtype==="string"?backend_util_exports.fromStringArrayToUint8(outBuf.values):outBuf.values}function slice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs;assertNotComplex(x,"slice");let[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);slice_util_exports.assertParamsValid(x,$begin,$size);let vals=backend2.data.get(x.dataId).values,outVals=sliceImpl(vals,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outVals)}var sliceConfig={kernelName:Slice,backendName:"cpu",kernelFunc:slice2},squaredDifferenceImpl=createSimpleBinaryKernelImpl((a,b)=>{let diff=a-b;return diff*diff}),squaredDifference2=binaryKernelFunc(SquaredDifference,squaredDifferenceImpl),squaredDifferenceConfig={kernelName:SquaredDifference,backendName:"cpu",kernelFunc:squaredDifference2};function stridedSliceImpl(outShape,xBuf,strides,begin){let outBuf=buffer(outShape,xBuf.dtype);for(let i=0;i<outBuf.size;i++){let loc=outBuf.indexToLoc(i),newLoc=new Array(loc.length);for(let j=0;j<newLoc.length;j++)newLoc[j]=loc[j]*strides[j]+begin[j];outBuf.set(xBuf.get(...newLoc),...loc)}return outBuf}var subImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue-bValue),subComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal-bReal,imag:aImag-bImag})),sub2=binaryKernelFunc(Sub,subImpl,subComplexImpl),subConfig={kernelName:Sub,backendName:"cpu",kernelFunc:sub2};function tileImpl(xBuf,reps){let newShape=new Array(xBuf.rank);for(let i=0;i<newShape.length;i++)newShape[i]=xBuf.shape[i]*reps[i];let result=buffer(newShape,xBuf.dtype);for(let i=0;i<result.values.length;++i){let newLoc=result.indexToLoc(i),originalLoc=new Array(xBuf.rank);for(let j=0;j<originalLoc.length;j++)originalLoc[j]=newLoc[j]%xBuf.shape[j];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result}function topKImpl(x,xShape,xDtype,k,sorted){let lastDim=xShape[xShape.length-1],[batch,size]=[x.length/lastDim,lastDim],allTopKVals=util_exports.getTypedArrayFromDType(xDtype,batch*k),allTopKIndices=util_exports.getTypedArrayFromDType("int32",batch*k);for(let b=0;b<batch;b++){let offset=b*size,vals=x.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value);let outOffset=b*k,topKVals=allTopKVals.subarray(outOffset,outOffset+k),topKIndices=allTopKIndices.subarray(outOffset,outOffset+k);for(let i=0;i<k;i++)topKVals[i]=valAndInd[i].value,topKIndices[i]=valAndInd[i].index}let outputShape=xShape.slice();return outputShape[outputShape.length-1]=k,[buffer(outputShape,xDtype,allTopKVals),buffer(outputShape,"int32",allTopKIndices)]}function uniqueImpl(values,axis,shape,dtype){let $axis=util_exports.parseAxisParam(axis,shape)[0],newShape=[1,shape[0],1];for(let i=0;i<$axis;i++)newShape[0]*=shape[i];newShape[1]=shape[$axis];for(let i=$axis+1;i<shape.length;i++)newShape[2]*=shape[i];let uniqueElements={},indices=new Int32Array(shape[$axis]),inputBuffer=new TensorBuffer(newShape,dtype,values),uniqueIndices=[],is1DTensor=newShape[0]===1&&newShape[2]===1;for(let i=0;i<shape[$axis];i++){let element;if(is1DTensor)element=values[i].toString();else{let axisValues=[];for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)axisValues.push(inputBuffer.get(m,i,n));element=axisValues.join(",")}if(uniqueElements[element]!==void 0)indices[i]=uniqueElements[element];else{let uniqueIndex=Object.keys(uniqueElements).length;uniqueElements[element]=uniqueIndex,indices[i]=uniqueIndex,uniqueIndices.push(i)}}let outputTmpShape=newShape.slice();outputTmpShape[1]=Object.keys(uniqueElements).length;let outputBuffer=new TensorBuffer(outputTmpShape,dtype);uniqueIndices.forEach((uniqueElementIndex,i)=>{for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)outputBuffer.set(inputBuffer.get(m,uniqueElementIndex,n),m,i,n)});let outputShape=shape.slice();return outputShape[$axis]=outputTmpShape[1],{outputValues:outputBuffer.values,outputShape,indices}}var version5="2.8.3";registerBackend("cpu",()=>new MathBackendCPU,1);var elu4=unaryKernelFunc(Elu,xi=>xi>=0?xi:Math.exp(xi)-1),eluConfig={kernelName:Elu,backendName:"cpu",kernelFunc:elu4};function leakyRelu2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{alpha}=attrs;assertNotComplex([x],"leakyRelu");let xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outVals=util_exports.getTypedArrayFromDType("float32",xSize);for(let i=0;i<xVals.length;i++)outVals[i]=xVals[i]<0?alpha*xVals[i]:xVals[i];return backend2.makeTensorInfo(x.shape,"float32",outVals)}var leakyReluConfig={kernelName:LeakyRelu,backendName:"cpu",kernelFunc:leakyRelu2},preluImpl=createSimpleBinaryKernelImpl((xValue,aValue)=>xValue<0?aValue*xValue:xValue);function prelu3(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs;assertNotComplex([x,alpha],"prelu");let aVals=backend2.data.get(x.dataId).values,bVals=backend2.data.get(alpha.dataId).values,[resultData,resultShape]=preluImpl(x.shape,alpha.shape,aVals,bVals,x.dtype);return backend2.makeTensorInfo(resultShape,x.dtype,resultData)}var preluConfig={kernelName:Prelu,backendName:"cpu",kernelFunc:prelu3},relu2=unaryKernelFunc(Relu,xi=>Math.max(0,xi)),reluConfig={kernelName:Relu,backendName:"cpu",kernelFunc:relu2},relu62=unaryKernelFunc(Relu6,xi=>Math.min(Math.max(0,xi),6)),relu6Config={kernelName:Relu6,backendName:"cpu",kernelFunc:relu62};function applyActivation2(backend2,x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return identity2({inputs:{x},backend:backend2});if(activation2==="relu")return relu2({inputs:{x},backend:backend2});if(activation2==="elu")return elu4({inputs:{x},backend:backend2});if(activation2==="relu6")return relu62({inputs:{x},backend:backend2});if(activation2==="prelu")return prelu3({inputs:{x,alpha:preluActivationWeights},backend:backend2});if(activation2==="leakyrelu")return leakyRelu2({inputs:{x},backend:backend2,attrs:{alpha:leakyreluAlpha}});throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`)}function reshape3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`),backend2.incRef(x.dataId);let xData=backend2.data.get(x.dataId);if(xData.complexTensorInfos!=null){let real4=xData.complexTensorInfos.real,imag4=xData.complexTensorInfos.imag;real4.shape=$shape,imag4.shape=$shape}return{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig={kernelName:Reshape,backendName:"cpu",kernelFunc:reshape3};function batchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;assertNotComplex([a,b],"matMul");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape3({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape3({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),sharedDim=transposeA?a3d.shape[1]:a3d.shape[2],leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),a3dValues=backend2.data.get(a3d.dataId).values,b3dValues=backend2.data.get(b3d.dataId).values,a3dStrides=util_exports.computeStrides(a3d.shape),b3dStrides=util_exports.computeStrides(b3d.shape),[aBatch,aOuterStep,aInnerStep]=transposeA?[a3dStrides[0],1,a3dStrides[1]]:[a3dStrides[0],a3dStrides[1],1],[bInnerStep,bOuterStep,bBatch]=transposeB?[1,b3dStrides[1],b3dStrides[0]]:[b3dStrides[1],1,b3dStrides[0]],size=leftDim*rightDim,result=buffer([batchDim,leftDim,rightDim],a3d.dtype),resVals=result.values,blockSize=backend2.blockSize;for(let bi=0;bi<batchDim;bi++)for(let i0=0;i0<leftDim;i0+=blockSize)for(let j0=0;j0<rightDim;j0+=blockSize)for(let k0=0;k0<sharedDim;k0+=blockSize){let iBlock=Math.min(i0+blockSize,leftDim),jBlock=Math.min(j0+blockSize,rightDim),kBlock=Math.min(k0+blockSize,sharedDim);for(let i=i0;i<iBlock;i++)for(let j=j0;j<jBlock;j++){let sum6=0;for(let k=k0;k<kBlock;k++){let batchOffsetA=Math.min(bi,batchDimA-1)*aBatch,batchOffsetB=Math.min(bi,batchDimB-1)*bBatch,aVal=a3dValues[batchOffsetA+i*aOuterStep+k*aInnerStep],bVal=b3dValues[k*bInnerStep+j*bOuterStep+batchOffsetB];sum6+=aVal*bVal}resVals[bi*size+(i*rightDim+j)]+=sum6}}return backend2.disposeIntermediateTensorInfo(a3d),backend2.disposeIntermediateTensorInfo(b3d),backend2.makeTensorInfo(outShape,result.dtype,result.values)}var batchMatMulConfig={kernelName:BatchMatMul,backendName:"cpu",kernelFunc:batchMatMul};function _fusedMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs,{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,current,addRes,activationRes,intermediates=[];current=batchMatMul({inputs:{a,b},attrs:{transposeA,transposeB},backend:backend2}),bias&&(addRes=add4({inputs:{a:current,b:bias},backend:backend2}),intermediates.push(current),current=addRes),activation2&&(activationRes=applyActivation2(backend2,current,activation2,preluActivationWeights,leakyreluAlpha),intermediates.push(current),current=activationRes);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return current}var _fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"cpu",kernelFunc:_fusedMatMul},acos2=unaryKernelFunc(Acos,xi=>Math.acos(xi)),acosConfig={kernelName:Acos,backendName:"cpu",kernelFunc:acos2},acosh2=unaryKernelFunc(Acosh,xi=>Math.acosh(xi)),acoshConfig={kernelName:Acosh,backendName:"cpu",kernelFunc:acosh2};function addN2(args){let{inputs,backend:backend2}=args,tensors=inputs;assertNotComplex(inputs,"addN");let vals=tensors.map(t=>backend2.data.get(t.dataId).values),outBuf=buffer(tensors[0].shape,tensors[0].dtype),outVals=outBuf.values;for(let i=0;i<tensors.length;i++){let currVals=vals[i];for(let j=0;j<outVals.length;j++)outVals[j]+=currVals[j]}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var addNConfig={kernelName:AddN,backendName:"cpu",kernelFunc:addN2};function all2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"all");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("all",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,all4=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];all4=all4&&value}vals[i]=all4}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var allConfig={kernelName:All,backendName:"cpu",kernelFunc:all2};function any2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"any");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("any",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,anyVal=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];anyVal=anyVal||value}vals[i]=anyVal}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var anyConfig={kernelName:Any,backendName:"cpu",kernelFunc:any2};function argMax2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMax");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMax",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset],maxIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value,maxIndex=j)}vals[i]=maxIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMaxConfig={kernelName:ArgMax,backendName:"cpu",kernelFunc:argMax2};function argMin2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMin");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMin",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset],minIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value,minIndex=j)}vals[i]=minIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMinConfig={kernelName:ArgMin,backendName:"cpu",kernelFunc:argMin2},asin2=unaryKernelFunc(Asin,xi=>Math.asin(xi)),asinConfig={kernelName:Asin,backendName:"cpu",kernelFunc:asin2},asinh2=unaryKernelFunc(Asinh,xi=>Math.asinh(xi)),asinhConfig={kernelName:Asinh,backendName:"cpu",kernelFunc:asinh2},atan3=unaryKernelFunc(Atan,xi=>Math.atan(xi)),atanConfig={kernelName:Atan,backendName:"cpu",kernelFunc:atan3},atan2Impl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.atan2(aValue,bValue)),atan22=binaryKernelFunc(Atan2,atan2Impl),atan2Config={kernelName:Atan2,backendName:"cpu",kernelFunc:atan22},atanh2=unaryKernelFunc(Atanh,xi=>Math.atanh(xi)),atanhConfig={kernelName:Atanh,backendName:"cpu",kernelFunc:atanh2};function pool2(xValues,xShape,dtype,strides,convInfo,poolType){let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3],outputRowStrides=convInfo.outShape[2]*convInfo.outShape[3],outputColStrides=convInfo.outShape[3];for(let b=0;b<convInfo.batchSize;++b){let outputBatchOffset=b*outputBatchStrides,inputBatchOffset=b*strides[0];for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=Math.max(0,xRCorner),xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner),outputRowOffset=outputBatchOffset+yR*outputRowStrides;for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=Math.max(0,xCCorner),xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),minMaxValue=initialValue,avgValue=0,count2=0;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let xROffset=inputBatchOffset+xR*strides[1];for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let xCOffset=xROffset+xC*strides[2],pixel=xValues[xCOffset+d];poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++)}if(isNaN(minMaxValue))break}let outputOffset=outputRowOffset+yC*outputColStrides+d;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}return output}function maxPoolPositions(xValues,xShape,dtype,convInfo,flattenPositions=!1,includeBatchInIndex=!1){let maxPositions=buffer(convInfo.outShape,"int32"),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=buffer(xShape,dtype,xValues);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=xRCorner;for(;xRMin<0;)xRMin+=dilationHeight;let xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner);for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=xCCorner;for(;xCMin<0;)xCMin+=dilationWidth;let xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let wR=xR-xRCorner;for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let wC=xC-xCCorner,pixel=xBuf.get(b,xR,xC,d);pixel>maxValue&&(maxValue=pixel,flattenPositions?maxPosition=includeBatchInIndex?((b*convInfo.inHeight+xR)*convInfo.inWidth+xC)*convInfo.inChannels+d:(xR*convInfo.inWidth+xC)*convInfo.inChannels+d:maxPosition=wR*effectiveFilterWidth+wC)}}maxPositions.set(maxPosition,b,yR,yC,d)}}return maxPositions}function pool3d2(xValues,xShape,dtype,strides,convInfo,poolType){let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputDepthStrides=convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputRowStrides=convInfo.outShape[3]*convInfo.outShape[4],outputColStrides=convInfo.outShape[4];for(let batch=0;batch<convInfo.batchSize;++batch){let outputBatchOffset=batch*outputBatchStrides,inputBatchOffset=batch*strides[0];for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner),outputDepthOffset=outputBatchOffset+yDepth*outputDepthStrides;for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner),outputRowOffset=outputDepthOffset+yRow*outputRowStrides;for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),outputColOffset=outputRowOffset+yCol*outputColStrides,minMaxValue=initialValue,avgValue=0,count2=0;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let xDepthOffset=inputBatchOffset+xDepth*strides[1];for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let xRowOffset=xDepthOffset+xRow*strides[2];for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let xColOffset=xRowOffset+xCol*strides[3],pixel=xValues[xColOffset+channel];if(poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++),isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}let outputOffset=outputColOffset+channel;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}}return output}function maxPool3dPositions(xBuf,convInfo){let maxPositions=buffer(convInfo.outShape,"int32"),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner);for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner);for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let wDepth=xDepth-xDepthCorner;for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let wRow=xRow-xRowCorner;for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let wCol=xCol-xColCorner,pixel=xBuf.get(batch,xDepth,xRow,xCol,channel);pixel>=maxValue&&(maxValue=pixel,maxPosition=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterHeight+wCol)}}}maxPositions.set(maxPosition,batch,yDepth,yRow,yCol,channel)}}}return maxPositions}function avgPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"avgPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"avg");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var avgPoolConfig={kernelName:AvgPool,backendName:"cpu",kernelFunc:avgPool2};function avgPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"avgPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"avg");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var avgPool3DConfig={kernelName:AvgPool3D,backendName:"cpu",kernelFunc:avgPool3D};function avgPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"avgPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),avgMultiplier=1/(filterDepth*filterHeight*filterWidth),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol||(dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel))}}}dx.set(dotProd*avgMultiplier,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPool3DGradConfig2={kernelName:AvgPool3DGrad,backendName:"cpu",kernelFunc:avgPool3DGrad};function avgPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex([dy,input2],"avgPoolGrad");let{filterSize,strides,pad:pad3}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterHeight*filterWidth),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC||(dotProd+=dyBuf.get(b,dyR,dyC,d))}}dx.set(dotProd*avgMultiplier,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPoolGradConfig2={kernelName:AvgPoolGrad,backendName:"cpu",kernelFunc:avgPoolGrad2};function batchNorm2(args){let{inputs,backend:backend2,attrs}=args,{x,scale:scale22,offset,mean:mean4,variance}=inputs;util_exports.assert(mean4.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean4.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale22==null||mean4.shape.length===scale22.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),assertNotComplex([x,mean4,variance,scale22,offset],"batchNorm");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let xVals=backend2.data.get(x.dataId).values,mVals=backend2.data.get(mean4.dataId).values,varVals=backend2.data.get(variance.dataId).values,sVals=scale22?backend2.data.get(scale22.dataId).values:new Float32Array([1]),offVals=offset?backend2.data.get(offset.dataId).values:new Float32Array([0]),outVals=new Float32Array(xVals.length),offValsLength=offVals.length,sValsLength=sVals.length,varValsLength=varVals.length,mValsLength=mVals.length,offi=0,mi=0,si=0,vi=0;for(let i=0;i<xVals.length;++i)outVals[i]=offVals[offi++]+(xVals[i]-mVals[mi++])*sVals[si++]/Math.sqrt(varVals[vi++]+varianceEpsilon),offi>=offValsLength&&(offi=0),mi>=mValsLength&&(mi=0),si>=sValsLength&&(si=0),vi>=varValsLength&&(vi=0);return backend2.makeTensorInfo(x.shape,x.dtype,outVals)}var batchNormConfig={kernelName:FusedBatchNorm,backendName:"cpu",kernelFunc:batchNorm2};function batchToSpaceND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;assertNotComplex([x],"batchToSpaceND");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),xReshaped=reshape3({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),xTransposed=transpose2({inputs:{x:xReshaped},backend:backend2,attrs:{perm:permuted}}),xTransposedReshaped=reshape3({inputs:{x:xTransposed},backend:backend2,attrs:{shape:reshapedPermuted}}),result=slice2({inputs:{x:xTransposedReshaped},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return backend2.disposeIntermediateTensorInfo(xReshaped),backend2.disposeIntermediateTensorInfo(xTransposed),backend2.disposeIntermediateTensorInfo(xTransposedReshaped),result}var batchToSpaceNDConfig={kernelName:BatchToSpaceND,backendName:"cpu",kernelFunc:batchToSpaceND2};function bincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig={kernelName:Bincount,backendName:"cpu",kernelFunc:bincount2},clip=unaryKernelFunc(ClipByValue,(xi,attrs)=>{let clipAttrs=attrs;return xi>clipAttrs.clipValueMax?clipAttrs.clipValueMax:xi<clipAttrs.clipValueMin?clipAttrs.clipValueMin:xi}),clipConfig={kernelName:ClipByValue,backendName:"cpu",kernelFunc:clip},complexAbs=args=>{let{x}=args.inputs,cpuBackend=args.backend,resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),complexVals=cpuBackend.data.get(x.dataId),real4=complexVals.complexTensorInfos.real,imag4=complexVals.complexTensorInfos.imag,realVals=cpuBackend.data.get(real4.dataId).values,imagVals=cpuBackend.data.get(imag4.dataId).values;for(let i=0;i<realVals.length;i++){let real5=realVals[i],imag5=imagVals[i];resultValues[i]=Math.hypot(real5,imag5)}return cpuBackend.makeOutput(resultValues,x.shape,"float32")},complexAbsConfig={kernelName:ComplexAbs,backendName:"cpu",kernelFunc:complexAbs};function imag2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,imag4=backend2.data.get(input2.dataId).complexTensorInfos.imag,imagVal=backend2.data.get(imag4.dataId).values;return backend2.makeTensorInfo(imag4.shape,imag4.dtype,imagVal)}var imagConfig={kernelName:Imag,backendName:"cpu",kernelFunc:imag2};function concat2(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity2({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,$axis),$inputs[0].dtype==="complex64"){let reals=$inputs.map(t=>real2({inputs:{input:t},backend:backend2})),imags=$inputs.map(t=>imag2({inputs:{input:t},backend:backend2})),realConcated=concat2({inputs:reals,backend:backend2,attrs:{axis:$axis}}),imagConcated=concat2({inputs:imags,backend:backend2,attrs:{axis:$axis}}),result=complex2({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result}let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice($axis));return reshape3({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.data.get(t.dataId).values,shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),$axis),outInfo=backend2.makeTensorInfo(finalOutShape,inputs[0].dtype,outVals);return inputs2D.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}var concatConfig={kernelName:Concat,backendName:"cpu",kernelFunc:concat2};function conv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"conv2d");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,padLeft=convInfo.padInfo.left,padTop=convInfo.padInfo.top,isChannelsLast=convInfo.dataFormat==="channelsLast",y=new TensorBuffer(convInfo.outShape,x.dtype),xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),xBatchStride=xStrides[0],xRowStride=isChannelsLast?xStrides[1]:xStrides[2],xColStride=isChannelsLast?xStrides[2]:1,xChannelStride=isChannelsLast?1:xStrides[1],yBatchStride=y.strides[0],yRowStride=isChannelsLast?y.strides[1]:y.strides[2],yColStride=isChannelsLast?y.strides[2]:1,yChannelStride=isChannelsLast?1:y.strides[1],xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xBatchStride,yOffset1=b*yBatchStride;for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*yRowStride,xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xRowStride;for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*yColStride,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*xColStride,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1*xChannelStride];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset3+d2*yChannelStride]+=xVal*wVals[wOffset3+d2];wOffset3+=convInfo.outChannels}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,yVals)}var conv2DConfig={kernelName:Conv2D,backendName:"cpu",kernelFunc:conv2D};function conv2DBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"conv2dBackpropFilter");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,isChannelsLast=convInfo.dataFormat==="channelsLast",dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,xVals=backend2.data.get(x.dataId).values,dyVals=backend2.data.get(dy.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d1=0;d1<convInfo.inChannels;++d1)for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;isChannelsLast?dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2):dotProd+=xBuf.get(b,d1,xR,xC)*dyBuf.get(b,d2,yR,yC)}}dW.set(dotProd,wR,wC,d1,d2)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var conv2DBackpropFilterConfig={kernelName:Conv2DBackpropFilter,backendName:"cpu",kernelFunc:conv2DBackpropFilter2};function conv2DBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{inputShape,strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;assertNotComplex([dy,filter],"conv2dBackpropInput");let filterStrides=util_exports.computeStrides(filter.shape),dyStrides=util_exports.computeStrides(dy.shape),$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,dyValues=backend2.data.get(dy.dataId).values,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo;$dataFormat=convInfo.dataFormat;let topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=$dataFormat==="channelsLast",xBatchStride=dx.strides[0],xRowStride=isChannelsLast?dx.strides[1]:dx.strides[2],xColStride=isChannelsLast?dx.strides[2]:1,xChannelStride=isChannelsLast?1:dx.strides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1];for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=yBatchStride*b+yRowStride*yR+yColStride*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+yChannelStride*d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}let dxOffset=xBatchStride*b+xRowStride*xR+xColStride*xC+xChannelStride*d1;dxValues[dxOffset]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv2DBackpropInputConfig={kernelName:Conv2DBackpropInput,backendName:"cpu",kernelFunc:conv2DBackpropInput2};function conv3D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs;assertNotComplex([x,filter],"conv3d");let convInfo=backend_util_exports.computeConv3DInfo(x.shape,filter.shape,strides,dilations,pad3),{filterDepth,filterHeight,filterWidth,dilationDepth,dilationHeight,dilationWidth,padInfo}=convInfo,padFront=padInfo.front,padLeft=padInfo.left,padTop=padInfo.top,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values,xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yF=0;yF<convInfo.outDepth;++yF){let yOffset2=yOffset1+yF*y.strides[1],xFCorner=yF*convInfo.strideDepth-padFront;for(let wF=0;wF<filterDepth;++wF){let xF=xFCorner+wF*dilationDepth;if(xF<0||xF>=convInfo.inDepth)continue;let wOffset1=wF*filterStrides[0],xOffset2=xOffset1+xF*xStrides[1];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset3=yOffset2+yR*y.strides[2],xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset2=wOffset1+wR*filterStrides[1],xOffset3=xOffset2+xR*xStrides[2];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset4=yOffset3+yC*convInfo.outChannels,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset3=wOffset2+wC*filterStrides[2],xOffset4=xOffset3+xC*convInfo.inChannels,wOffset4=wOffset3;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset4+d1];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset4+d2]+=xVal*wVals[wOffset4+d2];wOffset4+=convInfo.outChannels}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var conv3DConfig={kernelName:Conv3D,backendName:"cpu",kernelFunc:conv3D};function conv3DBackpropFilterV2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,filterShape}=attrs;assertNotComplex([x,dy],"conv3dBackpropFilterV2");let xStrides=util_exports.computeStrides(x.shape),dyStrides=util_exports.computeStrides(dy.shape),convInfo=backend_util_exports.computeConv3DInfo(x.shape,filterShape,strides,1,pad3),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dw=new TensorBuffer(convInfo.filterShape,"float32"),dwValues=dw.values,[dwS0,dwS1,dwS2,dwS3]=dw.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,xValues=backend2.data.get(x.dataId).values,[xS0,xS1,xS2,xS3]=xStrides,frontPad=convInfo.padInfo.front,leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top;for(let wF=0;wF<filterDepth;++wF){let yFMin=Math.max(0,Math.ceil((frontPad-wF)/strideDepth)),yFMax=Math.min(convInfo.outDepth,(convInfo.inDepth+frontPad-wF)/strideDepth),wOffset1=wF*dwS0;for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight),wOffset2=wR*dwS1+wOffset1;for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth),wOffset3=wC*dwS2+wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let wOffset4=d1*dwS3+wOffset3;for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xS0,yOffset1=b*dyS0;for(let yF=yFMin;yF<yFMax;++yF){let xOffset2=(wF+yF*strideDepth-frontPad)*xS1+xOffset1,yOffset2=yF*dyS1+yOffset1;for(let yR=yRMin;yR<yRMax;++yR){let xOffset3=(wR+yR*strideHeight-topPad)*xS2+xOffset2,yOffset3=yR*dyS2+yOffset2;for(let yC=yCMin;yC<yCMax;++yC){let xOffset4=(wC+yC*strideWidth-leftPad)*xS3+xOffset3,yOffset4=yC*dyS3+yOffset3;dotProd+=xValues[xOffset4+d1]*dyValues[yOffset4+d2]}}}}dwValues[wOffset4+d2]=dotProd}}}}}return backend2.makeTensorInfo(dw.shape,dw.dtype,dw.values)}var conv3DBackpropFilterV2Config={kernelName:Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:conv3DBackpropFilterV2};function conv3DBackpropInputV2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{pad:pad3,strides,inputShape}=attrs;assertNotComplex([dy],"conv3dBackpropInputV2");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv3DInfo(inputShape,filter.shape,strides,1,pad3),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2,dxS3]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2,fltS3]=filterStrides,{batchSize,filterDepth,filterHeight,filterWidth,inChannels,inDepth,inHeight,inWidth,outChannels,outDepth,outHeight,outWidth,strideDepth,strideHeight,strideWidth}=convInfo,frontPad=filterDepth-1-convInfo.padInfo.front,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xF=0;xF<inDepth;++xF){let xFCorner=xF-frontPad,xFMin=Math.max(0,Math.ceil(xFCorner/strideDepth)),yFMax=Math.min(outDepth,(filterDepth+xFCorner)/strideDepth);for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yF=xFMin;yF<yFMax;++yF){let wF=yF*strideDepth-xFCorner;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yF+dyS2*yR+dyS3*yC,fltOffset=fltS0*(filterDepth-1-wF)+fltS1*(filterHeight-1-wR)+fltS2*(filterWidth-1-wC)+fltS3*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}}dxValues[dxS0*b+dxS1*xF+dxS2*xR+dxS3*xC+d1]=dotProd}}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv3DBackpropInputV2Config={kernelName:Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:conv3DBackpropInputV2},cos2=unaryKernelFunc(Cos,xi=>Math.cos(xi)),cosConfig={kernelName:Cos,backendName:"cpu",kernelFunc:cos2},cosh2=unaryKernelFunc(Cosh,xi=>Math.cosh(xi)),coshConfig={kernelName:Cosh,backendName:"cpu",kernelFunc:cosh2};function cropAndResize2(args){let{inputs,backend:backend2,attrs}=args,{image:image3,boxes,boxInd}=inputs,{cropSize,method,extrapolationValue}=attrs,[batch,imageHeight,imageWidth,numChannels]=image3.shape,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,output=buffer([numBoxes,cropHeight,cropWidth,numChannels],"float32"),boxVals=backend2.data.get(boxes.dataId).values,boxIndVals=backend2.data.get(boxInd.dataId).values,imageVals=backend2.data.get(image3.dataId).values,inStride=util_exports.computeStrides(image3.shape),outStride=util_exports.computeStrides(output.shape);for(let b=0;b<numBoxes;b++){let startInd=b*4,y1=boxVals[startInd],x1=boxVals[startInd+1],y2=boxVals[startInd+2],x2=boxVals[startInd+3],bInd=boxIndVals[b];if(bInd>=batch)continue;let heightScale=cropHeight>1?(y2-y1)*(imageHeight-1)/(cropHeight-1):0,widthScale=cropWidth>1?(x2-x1)*(imageWidth-1)/(cropWidth-1):0;for(let y=0;y<cropHeight;y++){let yInd=cropHeight>1?y1*(imageHeight-1)+y*heightScale:.5*(y1+y2)*(imageHeight-1);if(yInd<0||yInd>imageHeight-1){for(let x=0;x<cropWidth;x++)for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}if(method==="bilinear"){let topInd=Math.floor(yInd),bottomInd=Math.ceil(yInd),yLerp=yInd-topInd;for(let x=0;x<cropWidth;x++){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let leftInd=Math.floor(xInd),rightInd=Math.ceil(xInd),xLerp=xInd-leftInd;for(let c=0;c<numChannels;c++){let ind=c+leftInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0],topLeft=imageVals[ind];ind=c+rightInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];let topRight=imageVals[ind];ind=c+leftInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomLeft=imageVals[ind];ind=c+rightInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomRight=imageVals[ind],top=topLeft+(topRight-topLeft)*xLerp,bottom=bottomLeft+(bottomRight-bottomLeft)*xLerp;ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0],output.values[ind]=top+(bottom-top)*yLerp}}}else for(let x=0;x<cropWidth;++x){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let closestX=Math.round(xInd),closestY=Math.round(yInd);for(let c=0;c<numChannels;c++){let inInd=c+closestX*inStride[2]+closestY*inStride[1]+bInd*inStride[0],outInd=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[outInd]=imageVals[inInd]}}}}return backend2.makeTensorInfo(output.shape,output.dtype,output.values)}var cropAndResizeConfig={kernelName:CropAndResize,backendName:"cpu",kernelFunc:cropAndResize2};function cumsum2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs;assertNotComplex(x,"cumsum");let permutation=backend_util_exports.getAxesPermutation([axis],x.shape.length),$x=x;permutation!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,x.shape.length)[0];if(permutedAxis!==$x.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length-1} but got axis=${permutedAxis}`);let resultDtype=upcastType($x.dtype,"int32"),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape),resultDtype),aVals=backend2.data.get($x.dataId).values,finalDim=$x.shape[$x.shape.length-1],indexAdjuster=reverse5?(i,j)=>i+finalDim-j-1:(i,j)=>i+j;for(let i=0;i<aVals.length;i+=finalDim)for(let j=0;j<finalDim;j++){let idx=indexAdjuster(i,j);if(j===0)vals[idx]=exclusive?0:aVals[idx];else{let prevIdx=indexAdjuster(i,j-1);vals[idx]=exclusive?aVals[prevIdx]+vals[prevIdx]:aVals[idx]+vals[prevIdx]}}let result=backend2.makeTensorInfo($x.shape,resultDtype,vals);if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose2({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo($x),reverseTransposedResult}return result}var cumsumConfig={kernelName:Cumsum,backendName:"cpu",kernelFunc:cumsum2};function denseBincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig={kernelName:DenseBincount,backendName:"cpu",kernelFunc:denseBincount2};function depthToSpace2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(dataFormat==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`),util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=x.shape[1],inputWidth=x.shape[2],inputDepth=x.shape[3],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),xValues=backend2.data.get(x.dataId).values,result=new Float32Array(batchSize*outputHeight*outputWidth*outputDepth),outputIdx=0;for(let b=0;b<batchSize;++b)for(let h=0;h<outputHeight;++h){let inH=Math.floor(h/blockSize),offsetH=h%blockSize;for(let w=0;w<outputWidth;++w){let inW=Math.floor(w/blockSize),offsetW=w%blockSize,offsetD=(offsetH*blockSize+offsetW)*outputDepth;for(let d=0;d<outputDepth;++d){let inputIdx=d+offsetD+inputDepth*(inW+inputWidth*(inH+inputHeight*b));result[outputIdx++]=xValues[inputIdx]}}}return backend2.makeTensorInfo([batchSize,outputHeight,outputWidth,outputDepth],x.dtype,result)}var depthToSpaceConfig={kernelName:DepthToSpace,backendName:"cpu",kernelFunc:depthToSpace2};function depthwiseConv2dNative(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"depthwiseConv2DNative");let xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),{filterHeight,filterWidth,dilationHeight,dilationWidth,padInfo}=convInfo,padLeft=padInfo.left,padTop=padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*y.strides[1],xRCorner=yR*convInfo.strideHeight-padLeft;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xStrides[1];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*y.strides[2],xCCorner=yC*convInfo.strideWidth-padTop;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*convInfo.inChannels,yOffset4=yOffset3,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1];for(let q=0;q<chMul;++q)yVals[yOffset4+q]+=xVal*wVals[wOffset3+q];yOffset4+=chMul,wOffset3+=chMul}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var depthwiseConv2dNativeConfig={kernelName:DepthwiseConv2dNative,backendName:"cpu",kernelFunc:depthwiseConv2dNative};function depthwiseConv2dNativeBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"depthwiseConv2dNativeBackpropFilter");let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,dilations,pad3,dimRoundingMode,!0),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,xVals=backend2.data.get(x.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyVals=backend2.data.get(dy.dataId).values,dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d2=0;d2<convInfo.outChannels;++d2){let d1=Math.trunc(d2/chMul),dm=d2%chMul,dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2)}}dW.set(dotProd,wR,wC,d1,dm)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var depthwiseConv2dNativeBackpropFilterConfig={kernelName:DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropFilter2};function depthwiseConv2dNativeBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,inputShape}=attrs;assertNotComplex([dy,filter],"depthwiseConv2DNativeBackpropInput");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,chMul=outChannels/inChannels;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yR+dyS2*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let dm=0;dm<chMul;++dm){let d2=d1*chMul+dm,pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+dm];dotProd+=pixel*weight}}}dxValues[dxS0*b+dxS1*xR+dxS2*xC+d1]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var depthwiseConv2dNativeBackpropInputConfig={kernelName:DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropInput2};function diag2(args){let{inputs,backend:backend2}=args,{x}=inputs,xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outBuf=buffer([xSize,xSize],x.dtype),vals=outBuf.values;for(let i=0;i<xVals.length;i++)vals[i*xSize+i]=xVals[i];let outShape=[...x.shape,...x.shape];return backend2.makeTensorInfo(outShape,outBuf.dtype,outBuf.values)}var diagConfig={kernelName:Diag,backendName:"cpu",kernelFunc:diag2},dilation2dConfig={kernelName:Dilation2D,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,xVals=cpuBackend.data.get(x.dataId).values,xRank=x.shape.length,filterVals=cpuBackend.data.get(filter.dataId).values,filterRank=filter.shape.length,{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations),outSize=util_exports.sizeFromShape(outShape),outRank=outShape.length,outputVals=util_exports.getArrayFromDType(x.dtype,outSize);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let xIndex=util_exports.locToIndex([b,hIn,wIn,d],xRank,util_exports.computeStrides(x.shape)),filterIndex=util_exports.locToIndex([h,w,d],filterRank,util_exports.computeStrides(filter.shape)),val=xVals[xIndex]+filterVals[filterIndex];val>curVal&&(curVal=val)}}}let outputIndex=util_exports.locToIndex([b,hOut,wOut,d],outRank,util_exports.computeStrides(outShape));outputVals[outputIndex]=curVal}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(outputVals,x.dtype),outShape,x.dtype),shape:outShape,dtype:x.dtype}}},dilation2dBackpropFilterConfig={kernelName:Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(filter.shape,filter.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hMax=0,wMax=0;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hMax=h,wMax=w)}}}gradients[hMax][wMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),filter.shape,filter.dtype),shape:filter.shape,dtype:filter.dtype}}},dilation2dBackpropInputConfig={kernelName:Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(x.shape,x.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hInMax=hBeg<0?0:hBeg,wInMax=wBeg<0?0:wBeg;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hInMax=hIn,wInMax=wIn)}}}gradients[b][hInMax][wInMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}};function eluGrad(args){let{inputs,backend:backend2}=args,{dy,y}=inputs;assertNotComplex([dy,y],"eluGrad");let resultValues=new Float32Array(util_exports.sizeFromShape(y.shape)),values=backend2.data.get(y.dataId).values,dyValues=backend2.data.get(dy.dataId).values;for(let i=0;i<values.length;++i){let v=values[i];v>=1?resultValues[i]=dyValues[i]:resultValues[i]=dyValues[i]*(v+1)}return backend2.makeTensorInfo(y.shape,"float32",resultValues)}var eluGradConfig2={kernelName:EluGrad,backendName:"cpu",kernelFunc:eluGrad},equalImpl=createSimpleBinaryKernelImpl((a,b)=>a===b?1:0),equal2=binaryKernelFunc(Equal,equalImpl,null,"bool"),equalConfig={kernelName:Equal,backendName:"cpu",kernelFunc:equal2},p=backend_util_exports.ERF_P,a1=backend_util_exports.ERF_A1,a2=backend_util_exports.ERF_A2,a3=backend_util_exports.ERF_A3,a4=backend_util_exports.ERF_A4,a5=backend_util_exports.ERF_A5,erf2=unaryKernelFunc(Erf,xi=>{let sign4=Math.sign(xi),v=Math.abs(xi),t=1/(1+p*v);return sign4*(1-((((a5*t+a4)*t+a3)*t+a2)*t+a1)*t*Math.exp(-v*v))}),erfConfig={kernelName:Erf,backendName:"cpu",kernelFunc:erf2};function expandDims3(args){let{inputs,backend:backend2,attrs}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig={kernelName:ExpandDims,backendName:"cpu",kernelFunc:expandDims3},realDivImpl=createSimpleBinaryKernelImpl((a,b)=>a/b),div2=binaryKernelFunc(RealDiv,realDivImpl),realDivConfig={kernelName:RealDiv,backendName:"cpu",kernelFunc:div2};function fftBatch(input2,inverse,cpuBackend){let inputShape=input2.shape,batch=inputShape[0],innerDim=inputShape[1],inputVals=cpuBackend.data.get(input2.dataId),real2D=inputVals.complexTensorInfos.real,imag2D=inputVals.complexTensorInfos.imag,resultShape=[batch,innerDim],resultSize=util_exports.sizeFromShape(resultShape),resultReal=util_exports.getTypedArrayFromDType("float32",resultSize),resultImag=util_exports.getTypedArrayFromDType("float32",resultSize);for(let b=0;b<batch;b++){let r=slice2({inputs:{x:real2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),i=slice2({inputs:{x:imag2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),input3=complex2({inputs:{real:r,imag:i},backend:cpuBackend}),{real:real4,imag:imag4}=fftImpl(input3,inverse,cpuBackend),res=backend_util_exports.mergeRealAndImagArrays(real4,imag4);for(let d=0;d<innerDim;d++){let c=backend_util_exports.getComplexWithIndex(res,d);resultReal[b*innerDim+d]=c.real,resultImag[b*innerDim+d]=c.imag}cpuBackend.disposeIntermediateTensorInfo(r),cpuBackend.disposeIntermediateTensorInfo(i),cpuBackend.disposeIntermediateTensorInfo(input3)}let $realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultReal),$imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultImag),result=complex2({inputs:{real:$realInfo,imag:$imagInfo},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($realInfo),cpuBackend.disposeIntermediateTensorInfo($imagInfo),result}function fftImpl(input2,inverse,cpuBackend){let inputSize=util_exports.sizeFromShape(input2.shape),inputVals=cpuBackend.data.get(input2.dataId),realVals=cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values,imagVals=cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;if(isExponentOf2(inputSize)){let result=fftRadix2(realVals,imagVals,inputSize,inverse,cpuBackend),resultShape=[input2.shape[0],input2.shape[1]];if(inverse){let realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.real),imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.imag),sizeInfo=cpuBackend.makeTensorInfo([],"float32",util_exports.createScalarValue(inputSize,"float32")),sizeInfoCopy=identity2({inputs:{x:sizeInfo},backend:cpuBackend}),divRealInfo=realDivConfig.kernelFunc({inputs:{a:realInfo,b:sizeInfo},backend:cpuBackend}),divImagInfo=realDivConfig.kernelFunc({inputs:{a:imagInfo,b:sizeInfoCopy},backend:cpuBackend}),divRealVals=cpuBackend.data.get(divRealInfo.dataId).values,divImagVals=cpuBackend.data.get(divImagInfo.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(realInfo),cpuBackend.disposeIntermediateTensorInfo(imagInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy),cpuBackend.disposeIntermediateTensorInfo(divRealInfo),cpuBackend.disposeIntermediateTensorInfo(divImagInfo),{real:divRealVals,imag:divImagVals}}return result}else{let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),rawOutput=fourierTransformByMatmul(data,inputSize,inverse);return backend_util_exports.splitRealAndImagArrays(rawOutput)}}function isExponentOf2(size){return(size&size-1)==0}function fftRadix2(realVals,imagVals,size,inverse,cpuBackend){if(size===1)return{real:realVals,imag:imagVals};let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),half=size/2,evenComplex=backend_util_exports.complexWithEvenIndex(data),evenRealVals=evenComplex.real,evenImagVals=evenComplex.imag,evenShape=[evenRealVals.length],evenRealInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenRealVals),evenImagInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenImagVals),evenTensorInfo=complex2({inputs:{real:evenRealInfo,imag:evenImagInfo},backend:cpuBackend}),oddComplex=backend_util_exports.complexWithOddIndex(data),oddRealVals=oddComplex.real,oddImagVals=oddComplex.imag,oddShape=[oddRealVals.length],oddRealInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddRealVals),oddImagInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddImagVals),oddTensorInfo=complex2({inputs:{real:oddRealInfo,imag:oddImagInfo},backend:cpuBackend}),$evenComplex=fftRadix2(evenRealVals,evenImagVals,half,inverse,cpuBackend),$evenRealVals=$evenComplex.real,$evenImagVals=$evenComplex.imag,$evenShape=[$evenRealVals.length],$evenRealInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenRealVals),$evenImagInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenImagVals),$evenTensorInfo=complex2({inputs:{real:$evenRealInfo,imag:$evenImagInfo},backend:cpuBackend}),$oddComplex=fftRadix2(oddRealVals,oddImagVals,half,inverse,cpuBackend),$oddRealVals=$oddComplex.real,$oddImagVals=$oddComplex.imag,$oddShape=[$oddRealVals.length],$oddRealInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddRealVals),$oddImagInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddImagVals),$oddTensorInfo=complex2({inputs:{real:$oddRealInfo,imag:$oddImagInfo},backend:cpuBackend}),e=backend_util_exports.exponents(size,inverse),eShape=[e.real.length],eRealInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.real),eImagInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.imag),complexInfo=complex2({inputs:{real:eRealInfo,imag:eImagInfo},backend:cpuBackend}),exponentInfo=multiply2({inputs:{a:complexInfo,b:$oddTensorInfo},backend:cpuBackend}),addPart=add4({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),subPart=sub2({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),addPartReal=real2({inputs:{input:addPart},backend:cpuBackend}),subPartReal=real2({inputs:{input:subPart},backend:cpuBackend}),addPartImag=imag2({inputs:{input:addPart},backend:cpuBackend}),subPartImag=imag2({inputs:{input:subPart},backend:cpuBackend}),$real=concat2({inputs:[addPartReal,subPartReal],backend:cpuBackend,attrs:{axis:0}}),$imag=concat2({inputs:[addPartImag,subPartImag],backend:cpuBackend,attrs:{axis:0}}),$realVals=cpuBackend.data.get($real.dataId).values,$imagVals=cpuBackend.data.get($imag.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(evenRealInfo),cpuBackend.disposeIntermediateTensorInfo(evenImagInfo),cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo(oddRealInfo),cpuBackend.disposeIntermediateTensorInfo(oddImagInfo),cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo($evenRealInfo),cpuBackend.disposeIntermediateTensorInfo($evenImagInfo),cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo($oddRealInfo),cpuBackend.disposeIntermediateTensorInfo($oddImagInfo),cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo(eRealInfo),cpuBackend.disposeIntermediateTensorInfo(eImagInfo),cpuBackend.disposeIntermediateTensorInfo(complexInfo),cpuBackend.disposeIntermediateTensorInfo(exponentInfo),cpuBackend.disposeIntermediateTensorInfo(addPart),cpuBackend.disposeIntermediateTensorInfo(subPart),cpuBackend.disposeIntermediateTensorInfo(addPartReal),cpuBackend.disposeIntermediateTensorInfo(addPartImag),cpuBackend.disposeIntermediateTensorInfo(subPartReal),cpuBackend.disposeIntermediateTensorInfo(subPartImag),cpuBackend.disposeIntermediateTensorInfo($real),cpuBackend.disposeIntermediateTensorInfo($imag),{real:$realVals,imag:$imagVals}}function fourierTransformByMatmul(data,size,inverse){let ret=new Float32Array(size*2);for(let r=0;r<size;r++){let real4=0,imag4=0;for(let c=0;c<size;c++){let e=backend_util_exports.exponent(r*c,size,inverse),term=backend_util_exports.getComplexWithIndex(data,c);real4+=term.real*e.real-term.imag*e.imag,imag4+=term.real*e.imag+term.imag*e.real}inverse&&(real4/=size,imag4/=size),backend_util_exports.assignToTypedArray(ret,real4,imag4,r)}return ret}function fft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!1,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var fftConfig={kernelName:FFT,backendName:"cpu",kernelFunc:fft2};function fill2(args){let{backend:backend2,attrs}=args,{shape,value,dtype}=attrs,$dtype=dtype||util_exports.inferDtype(value),values=util_exports.getArrayFromDType($dtype,util_exports.sizeFromShape(shape));return fillValues(values,value,$dtype),backend2.makeTensorInfo(shape,$dtype,values)}var fillConfig={kernelName:Fill,backendName:"cpu",kernelFunc:fill2};function fillValues(values,value,dtype){values.fill(value)}var flipLeftRightConfig={kernelName:FlipLeftRight,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let x=[batch,row,col,channel][2],coordX=Math.round(imageWidth-x),outIdx=batchOffset+rowOffset+colOffset+channel,outputValue=imageVals[outIdx];if(coordX>=0&&coordX<imageWidth){let rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}},floorDivImpl=createSimpleBinaryKernelImpl((a,b)=>Math.floor(a/b)),floorDiv2=binaryKernelFunc(FloorDiv,floorDivImpl,null,"int32"),floorDivConfig={kernelName:FloorDiv,backendName:"cpu",kernelFunc:floorDiv2};function fusedConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=conv2D({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let resultOld=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(resultOld)}if(activation2){let resultOld=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(resultOld)}return result}var fusedConv2DConfig={kernelName:FusedConv2D,backendName:"cpu",kernelFunc:fusedConv2D};function fusedDepthwiseConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=depthwiseConv2dNative({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let oldResult=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(oldResult)}if(activation2){let oldResult=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(oldResult)}return result}var fusedDepthwiseConv2DConfig={kernelName:FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:fusedDepthwiseConv2D};function gatherNd(args){let{inputs,backend:backend2}=args,{params,indices}=inputs,paramsSize=util_exports.sizeFromShape(params.shape),indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(params,indices);if(numSlices===0)return backend2.makeTensorInfo(resultShape,params.dtype,[]);let outBuf=buffer([numSlices,sliceSize],params.dtype),indicesData=backend2.data.get(indices.dataId).values,paramsData=backend2.data.get(params.dataId).values;for(let i=0;i<numSlices;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];flattenIndex+=dim*strides[j],index.push(dim)}if(flattenIndex<0||flattenIndex>=paramsSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${params.shape}`);for(let k=0;k<sliceSize;k++)outBuf.values[i*sliceSize+k]=paramsData[flattenIndex*sliceSize+k]}return backend2.makeTensorInfo(resultShape,outBuf.dtype,outBuf.values)}var gatherNdConfig={kernelName:GatherNd,backendName:"cpu",kernelFunc:gatherNd};function gatherV2(args){let{inputs,backend:backend2,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs;assertNotComplex([x,indices],"gatherV2");let $batchDims=batchDims;batchDims==null&&($batchDims=0);let indicesSize=util_exports.sizeFromShape(indices.shape),parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,$batchDims),flattenX=reshape3({inputs:{x},backend:backend2,attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]}}),flattenIndex=reshape3({inputs:{x:indices},backend:backend2,attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]}}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],indicesBuf=backend2.bufferSync(flattenIndex),xBuf=backend2.bufferSync(flattenX),outBuf=gatherV2Impl(xBuf,indicesBuf,flattenOutputShape);return backend2.disposeIntermediateTensorInfo(flattenX),backend2.disposeIntermediateTensorInfo(flattenIndex),backend2.makeTensorInfo(shapeInfo.outputShape,outBuf.dtype,outBuf.values)}var gatherV2Config={kernelName:GatherV2,backendName:"cpu",kernelFunc:gatherV2},greaterEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a>=b?1:0),greaterEqual2=binaryKernelFunc(GreaterEqual,greaterEqualImpl,null,"bool"),greaterEqualConfig={kernelName:GreaterEqual,backendName:"cpu",kernelFunc:greaterEqual2};function ifft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!0,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var ifftConfig={kernelName:IFFT,backendName:"cpu",kernelFunc:ifft2},isFinite3=unaryKernelFunc(IsFinite,xi=>Number.isFinite(xi)?1:0,"bool"),isFiniteConfig={kernelName:IsFinite,backendName:"cpu",kernelFunc:isFinite3},isInf2=unaryKernelFunc(IsInf,xi=>Math.abs(xi)===Infinity?1:0,"bool"),isInfConfig={kernelName:IsInf,backendName:"cpu",kernelFunc:isInf2},isNaN3=unaryKernelFunc(IsNan,xi=>Number.isNaN(xi)?1:0,"bool"),isNaNConfig={kernelName:IsNan,backendName:"cpu",kernelFunc:isNaN3},lessEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a<=b?1:0),lessEqual2=binaryKernelFunc(LessEqual,lessEqualImpl,null,"bool"),lessEqualConfig={kernelName:LessEqual,backendName:"cpu",kernelFunc:lessEqual2};function linSpace(args){let{backend:backend2,attrs}=args,{start,stop,num}=attrs,outVals=linSpaceImpl(start,stop,num);return backend2.makeTensorInfo([outVals.length],"float32",outVals)}var linSpaceConfig={kernelName:LinSpace,backendName:"cpu",kernelFunc:linSpace},log1p2=unaryKernelFunc(Log1p,xi=>Math.log1p(xi)),log1pConfig={kernelName:Log1p,backendName:"cpu",kernelFunc:log1p2},logicalAndImpl=createSimpleBinaryKernelImpl((a,b)=>a&&b),logicalAnd2=binaryKernelFunc(LogicalAnd,logicalAndImpl,null,"bool"),logicalAndConfig={kernelName:LogicalAnd,backendName:"cpu",kernelFunc:logicalAnd2},logicalNot2=unaryKernelFunc(LogicalNot,xi=>xi?0:1,"bool"),logicalNotConfig={kernelName:LogicalNot,backendName:"cpu",kernelFunc:logicalNot2},logicalOrImpl=createSimpleBinaryKernelImpl((a,b)=>a||b),logicalOr2=binaryKernelFunc(LogicalOr,logicalOrImpl,null,"bool"),logicalOrConfig={kernelName:LogicalOr,backendName:"cpu",kernelFunc:logicalOr2};function lRN(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(x,"LRN");let channels=x.shape[3],maxD=channels-1,xValues=backend2.data.get(x.dataId).values,size=util_exports.sizeFromShape(x.shape),result=new Float32Array(size);function sumAcrossChannels(offset){let currentChannel=offset%channels,beginSumOffset=offset-currentChannel+Math.max(0,currentChannel-depthRadius),endSumOffset=offset-currentChannel+Math.min(currentChannel+depthRadius,maxD),sum6=0;for(;beginSumOffset<=endSumOffset;beginSumOffset++){let z=xValues[beginSumOffset];sum6+=z*z}return sum6}for(let offset=0;offset<size;offset++){let sum6=sumAcrossChannels(offset),val=xValues[offset]*Math.pow(bias+alpha*sum6,-beta);result[offset]=val}return backend2.makeTensorInfo(x.shape,x.dtype,result)}var lRNConfig={kernelName:LRN,backendName:"cpu",kernelFunc:lRN};function lRNGrad(args){let{inputs,backend:backend2,attrs}=args,{x,y,dy}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(dy,"LRNGrad");let dySize=util_exports.sizeFromShape(dy.shape),channels=dy.shape[3],dyValues=backend2.data.get(dy.dataId).values,xValues=backend2.data.get(x.dataId).values,yValues=backend2.data.get(y.dataId).values,result=new Float32Array(dySize),size=dySize;for(let offset=0;offset<size;offset++){let currentChannel=offset%channels,depthBegin=offset-currentChannel+Math.max(0,currentChannel-depthRadius),depthEnd=offset-currentChannel+Math.min(channels,currentChannel+depthRadius+1),norm2=0;for(let k=depthBegin;k<depthEnd;k++)norm2+=Math.pow(xValues[k],2);norm2=alpha*norm2+bias;for(let k=depthBegin;k<depthEnd;k++){let dyi=-2*alpha*beta*xValues[k]*yValues[offset]/norm2;offset===k&&(dyi+=Math.pow(norm2,-beta)),dyi*=dyValues[offset],result[k]+=dyi}}return backend2.makeTensorInfo(dy.shape,x.dtype,result)}var lRNGradConfig={kernelName:LRNGrad,backendName:"cpu",kernelFunc:lRNGrad};function max3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reductionIndices,keepDims}=attrs,cpuBackend=backend2,xShape=x.shape,xRank=xShape.length,origAxes=util_exports.parseAxisParam(reductionIndices,xShape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xVals=cpuBackend.data.get(x.dataId).values;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];xVals=transposeImpl(xVals,xShape,x.dtype,permutedAxes,newShape),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xShape=newShape}assertNotComplex(x,"max"),backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),result=maxImpl(xVals,reduceSize,maxOutShape,x.dtype),dataId=cpuBackend.write(result,maxOutShape,x.dtype),outShape=maxOutShape;return keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes)),{dataId,shape:outShape,dtype:x.dtype}}var maxConfig={kernelName:Max,backendName:"cpu",kernelFunc:max3};function maxPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"maxPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"max");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var maxPoolConfig={kernelName:MaxPool,backendName:"cpu",kernelFunc:maxPool2};function maxPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"maxPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"max");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var maxPool3DConfig={kernelName:MaxPool3D,backendName:"cpu",kernelFunc:maxPool3D};function maxPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"maxPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),inputBuf=backend2.bufferSync(input2),maxPosBuf=maxPool3dPositions(inputBuf,convInfo),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;let maxPos=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(batch,dyDepth,dyRow,dyCol,channel),curPos=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterWidth+wCol,mask=maxPos===curPos?1:0;mask!==0&&(dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel)*mask)}}}dx.set(dotProd,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPool3DGradConfig2={kernelName:MaxPool3DGrad,backendName:"cpu",kernelFunc:maxPool3DGrad};function maxPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex([input2,output],"maxPoolGrad");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),xValues=backend2.data.get(x.dataId).values,maxPosBuf=buffer(convInfo.outShape,x.dtype,maxPoolPositions(xValues,x.shape,x.dtype,convInfo).values),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;let maxPos=effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(b,dyR,dyC,d),curPos=wR*effectiveFilterWidth+wC,mask=maxPos===curPos?1:0;mask!==0&&(dotProd+=dyBuf.get(b,dyR,dyC,d)*mask)}}dx.set(dotProd,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPoolGradConfig2={kernelName:MaxPoolGrad,backendName:"cpu",kernelFunc:maxPoolGrad2};function maxPoolWithArgmaxImpl(xValues,xShape,dtype,includeBatchInIndex,convInfo){let strides=util_exports.computeStrides(xShape),maxPools=pool2(xValues,xShape,dtype,strides,convInfo,"max"),maxPositions=maxPoolPositions(xValues,xShape,dtype,convInfo,!0,includeBatchInIndex);return[maxPools.values,maxPositions.values]}var maxPoolWithArgmaxConfig={kernelName:MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{x}=inputs,{filterSize,strides,pad:pad3,includeBatchInIndex}=attrs,cpuBackend=backend2;assertNotComplex(x,"MaxPoolWithArgmax");let values=cpuBackend.data.get(x.dataId).values,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,[1,1],pad3),[pooled,indexes]=maxPoolWithArgmaxImpl(values,x.shape,x.dtype,includeBatchInIndex,convInfo),pooledDataId=cpuBackend.write(pooled,convInfo.outShape,x.dtype),indexesDataId=cpuBackend.write(indexes,convInfo.outShape,x.dtype);return[{dataId:pooledDataId,shape:convInfo.outShape,dtype:x.dtype},{dataId:indexesDataId,shape:convInfo.outShape,dtype:"int32"}]}};function sum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"sum");let $x;x.dtype==="bool"?$x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"int32"}}):$x=identity2({inputs:{x},backend:backend2});let xRank=$x.shape.length,axes=util_exports.parseAxisParam(axis,$x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose2({inputs:{x:$x},backend:backend2,attrs:{perm:permutation}}),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank)),backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,permutedX.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(permutedX.shape,reductionAxes),resultDtype=backend_util_exports.upcastType(permutedX.dtype,"int32"),result=zeros3(backend2,outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=backend2.data.get(result.dataId).values,aVals=backend2.data.get(permutedX.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,sum6=0;for(let j=0;j<reduceSize;++j)sum6+=aVals[offset+j];vals[i]=sum6}if(keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(result.shape,axes),oldResult=result;result=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:newShape}}),backend2.disposeIntermediateTensorInfo(oldResult)}return backend2.disposeIntermediateTensorInfo($x),permutation!=null&&backend2.disposeIntermediateTensorInfo(permutedX),result}var sumConfig={kernelName:Sum,backendName:"cpu",kernelFunc:sum3};function mean2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs,axes=util_exports.parseAxisParam(axis,x.shape),reduceShape=backend_util_exports.computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=util_exports.sizeFromShape(reduceShape),toDispose=[],reduceSizeScalar=backend2.makeTensorInfo([],"float32",new Float32Array([reduceSize]));toDispose.push(reduceSizeScalar);let $x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}});toDispose.push($x);let res=div2({inputs:{a:$x,b:reduceSizeScalar},backend:backend2});toDispose.push(res);let result=sum3({inputs:{x:res},backend:backend2,attrs:{axis,keepDims}});return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var meanConfig={kernelName:Mean,backendName:"cpu",kernelFunc:mean2};function min3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"min");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("min",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value)}vals[i]=min6}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var minConfig={kernelName:Min,backendName:"cpu",kernelFunc:min3};function mirrorPad2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,mode}=attrs;assertNotComplex(x,"mirrorPad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),end=paddings.map((p2,i)=>p2[0]+x.shape[i]),offset=mode==="reflect"?0:1,xVals=backend2.data.get(x.dataId).values,xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);for(let i=0;i<resultSize;i++){let coords2=util_exports.indexToLoc(i,resultRank,resultStrides);for(let i2=0;i2<resultRank;i2++)coords2[i2]<start[i2]?coords2[i2]=start[i2]*2-coords2[i2]-offset:coords2[i2]>=end[i2]&&(coords2[i2]=(end[i2]-1)*2-coords2[i2]+offset);coords2=coords2.map((c,i2)=>c-start[i2]);let inIndex=util_exports.locToIndex(coords2,xRank,xStrides);resVals[i]=xVals[inIndex]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var mirrorPadConfig={kernelName:MirrorPad,backendName:"cpu",kernelFunc:mirrorPad2},modImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>{let rem=aValue%bValue;return aValue<0&&bValue<0||aValue>=0&&bValue>=0?rem:(rem+bValue)%bValue}),mod2=binaryKernelFunc(Mod,modImpl),modConfig={kernelName:Mod,backendName:"cpu",kernelFunc:mod2},seedrandom4=__toModule2(require_seedrandom2());function softmax3(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{dim}=attrs,logitsRank=logits.shape.length,$dim=dim;if($dim===-1&&($dim=logitsRank-1),$dim!==logitsRank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);let axes=util_exports.parseAxisParam([$dim],logits.shape),maxLogit=max3({inputs:{x:logits},backend:backend2,attrs:{reductionIndices:axes,keepDims:!1}}),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),maxLogitReshaped=reshape3({inputs:{x:maxLogit},backend:backend2,attrs:{shape:expandedShape}}),a=sub2({inputs:{a:logits,b:maxLogitReshaped},backend:backend2}),b=exp2({inputs:{x:a},backend:backend2}),sumExp=sum3({inputs:{x:b},backend:backend2,attrs:{axis:axes,keepDims:!1}}),sumReshaped=reshape3({inputs:{x:sumExp},backend:backend2,attrs:{shape:expandedShape}}),result=div2({inputs:{a:b,b:sumReshaped},backend:backend2});return backend2.disposeIntermediateTensorInfo(maxLogit),backend2.disposeIntermediateTensorInfo(maxLogitReshaped),backend2.disposeIntermediateTensorInfo(a),backend2.disposeIntermediateTensorInfo(b),backend2.disposeIntermediateTensorInfo(sumExp),backend2.disposeIntermediateTensorInfo(sumReshaped),result}var softmaxConfig={kernelName:Softmax,backendName:"cpu",kernelFunc:softmax3};function multinomial2(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{numSamples,seed,normalized}=attrs;assertNotComplex(logits,"multinomial");let probabilities=normalized?logits:softmax3({inputs:{logits},backend:backend2,attrs:{dim:-1}}),batchSize=probabilities.shape[0],numEvents=probabilities.shape[1],probVals=backend2.data.get(probabilities.dataId).values,resShape=[batchSize,numSamples],resVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape),"int32");for(let b=0;b<batchSize;++b){let offset=b*numEvents,cdf=new Float32Array(numEvents-1);cdf[0]=probVals[offset];for(let event=1;event<cdf.length;++event)cdf[event]=cdf[event-1]+probVals[offset+event];let random=seedrandom4.alea(seed.toString()),outOffset=b*numSamples;for(let sampleId=0;sampleId<numSamples;++sampleId){let r=random();resVals[outOffset+sampleId]=cdf.length;for(let event=0;event<cdf.length;event++)if(r<cdf[event]){resVals[outOffset+sampleId]=event;break}}}return normalized||backend2.disposeIntermediateTensorInfo(probabilities),backend2.makeTensorInfo(resShape,"int32",resVals)}var multinomialConfig={kernelName:Multinomial,backendName:"cpu",kernelFunc:multinomial2},nonMaxSuppressionV3Impl2=kernel_impls_exports.nonMaxSuppressionV3Impl;function nonMaxSuppressionV3(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold}=attrs;assertNotComplex(boxes,"NonMaxSuppression");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices}=nonMaxSuppressionV3Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices))}var nonMaxSuppressionV3Config={kernelName:NonMaxSuppressionV3,backendName:"cpu",kernelFunc:nonMaxSuppressionV3},nonMaxSuppressionV4Impl2=kernel_impls_exports.nonMaxSuppressionV4Impl;function nonMaxSuppressionV4(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs;assertNotComplex(boxes,"NonMaxSuppressionPadded");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([],"int32",new Int32Array([validOutputs]))]}var nonMaxSuppressionV4Config={kernelName:NonMaxSuppressionV4,backendName:"cpu",kernelFunc:nonMaxSuppressionV4},nonMaxSuppressionV5Impl2=kernel_impls_exports.nonMaxSuppressionV5Impl;function nonMaxSuppressionV5(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs;assertNotComplex(boxes,"NonMaxSuppressionWithScore");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl2(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([selectedScores.length],"float32",new Float32Array(selectedScores))]}var nonMaxSuppressionV5Config={kernelName:NonMaxSuppressionV5,backendName:"cpu",kernelFunc:nonMaxSuppressionV5};function oneHot2(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs;assertNotComplex(indices,"oneHot");let indicesSize=util_exports.sizeFromShape(indices.shape),res=new Float32Array(indicesSize*depth);res.fill(offValue);let indicesVal=backend2.data.get(indices.dataId).values;for(let event=0;event<indicesSize;++event)indicesVal[event]>=0&&indicesVal[event]<depth&&(res[event*depth+indicesVal[event]]=onValue);return backend2.makeTensorInfo([...indices.shape,depth],"int32",res)}var oneHotConfig={kernelName:OneHot,backendName:"cpu",kernelFunc:oneHot2};function zerosLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=zerosLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:0,dtype:x.dtype}})}var zerosLikeConfig={kernelName:ZerosLike,backendName:"cpu",kernelFunc:zerosLike2};function onesLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=onesLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:1,dtype:x.dtype}})}var onesLikeConfig={kernelName:OnesLike,backendName:"cpu",kernelFunc:onesLike2};function pack(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims3({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let intermediateTensorInfos=[],expandedTensors=inputs.map(t=>{let expandedT=expandDims3({inputs:{input:t},backend:backend2,attrs:{dim:axis}});return intermediateTensorInfos.push(expandedT),expandedT}),result=concat2({inputs:expandedTensors,backend:backend2,attrs:{axis}});return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var packConfig={kernelName:Pack,backendName:"cpu",kernelFunc:pack};function padV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,constantValue}=attrs;assertNotComplex(x,"pad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),xVals=backend2.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);constantValue!==0&&resVals.fill(constantValue);for(let i=0;i<xSize;i++){let outCoords=util_exports.indexToLoc(i,xRank,xStrides).map((c,i2)=>c+start[i2]),outIndex=util_exports.locToIndex(outCoords,resultRank,resultStrides);resVals[outIndex]=xVals[i]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var padV2Config={kernelName:PadV2,backendName:"cpu",kernelFunc:padV2},powImpl=createSimpleBinaryKernelImpl((a,b)=>Math.pow(a,b)),pow2=binaryKernelFunc(Pow,powImpl),powConfig={kernelName:Pow,backendName:"cpu",kernelFunc:pow2};function range3(args){let{backend:backend2,attrs}=args,{start,stop,dtype,step:step4}=attrs,values=rangeImpl(start,stop,step4,dtype);return backend2.makeTensorInfo([values.length],dtype,values)}var rangeConfig={kernelName:Range,backendName:"cpu",kernelFunc:range3},reciprocal2=unaryKernelFunc(Reciprocal,xi=>1/xi),reciprocalConfig={kernelName:Reciprocal,backendName:"cpu",kernelFunc:reciprocal2};function resizeBilinear2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeBilinear");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,result=new Float32Array(util_exports.sizeFromShape([batch,newHeight,newWidth,numChannels])),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],outputIdx=0,effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];for(let b=0;b<batch;b++)for(let r=0;r<newHeight;r++){let sourceFracRow;halfPixelCenters?sourceFracRow=effectiveRowSizeRatio*(r+.5)-.5:sourceFracRow=effectiveRowSizeRatio*r;let sourceRowFloor=Math.max(0,Math.floor(sourceFracRow)),rowFrac=sourceFracRow-sourceRowFloor,sourceRowCeil=Math.min(oldHeight-1,Math.ceil(sourceFracRow)),topRowOffset=b*imagesStrides[0]+sourceRowFloor*imagesStrides[1],botRowOffset=b*imagesStrides[0]+sourceRowCeil*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol;halfPixelCenters?sourceFracCol=effectiveColSizeRatio*(c+.5)-.5:sourceFracCol=effectiveColSizeRatio*c;let sourceColFloor=Math.max(0,Math.floor(sourceFracCol)),colFrac=sourceFracCol-sourceColFloor,sourceColCeil=Math.min(oldWidth-1,Math.ceil(sourceFracCol)),topLeftOffest=topRowOffset+sourceColFloor*imagesStrides[2],botLeftOffset=botRowOffset+sourceColFloor*imagesStrides[2],topRightOffset=topRowOffset+sourceColCeil*imagesStrides[2],botRightOffest=botRowOffset+sourceColCeil*imagesStrides[2];for(let d=0;d<numChannels;d++){let topLeft=xValues[topLeftOffest+d],bottomLeft=xValues[botLeftOffset+d],topRight=xValues[topRightOffset+d],bottomRight=xValues[botRightOffest+d],top=topLeft+(topRight-topLeft)*colFrac,bottom=bottomLeft+(bottomRight-bottomLeft)*colFrac,newValue=top+(bottom-top)*rowFrac;result[outputIdx++]=newValue}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],"float32",result)}var resizeBilinearConfig={kernelName:ResizeBilinear,backendName:"cpu",kernelFunc:resizeBilinear2};function resizeBilinearGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeBilinearGrad");let imagesStrides=util_exports.computeStrides(images.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],dyValues=backend2.data.get(dy.dataId).values,offset=0;for(let b=0;b<batch;b++){let bOffset=b*imagesStrides[0];for(let r=0;r<yHeight;r++){let dxR=r*heightScale,topDxRIndex=Math.floor(dxR),bottomDxRIndex=Math.min(Math.ceil(dxR),xHeight-1),topDxROffset=bOffset+topDxRIndex*imagesStrides[1],bottomDxROffset=bOffset+bottomDxRIndex*imagesStrides[1],dxRLerp=dxR-topDxRIndex,inverseDxRLerp=1-dxRLerp;for(let c=0;c<yWidth;c++){let dxC=c*widthScale,leftDxCIndex=Math.floor(dxC),rightDxCIndex=Math.min(Math.ceil(dxC),xWidth-1),dxCLerp=dxC-leftDxCIndex,inverseDxCLerp=1-dxCLerp,topLeftRCOffset=topDxROffset+leftDxCIndex*imagesStrides[2],topRightRCOffset=topDxROffset+rightDxCIndex*imagesStrides[2],bottomLeftRCOffset=bottomDxROffset+leftDxCIndex*imagesStrides[2],bottomRightRCOffset=bottomDxROffset+rightDxCIndex*imagesStrides[2],inverseDxRLerpTimesInverseDxCLerp=inverseDxRLerp*inverseDxCLerp,inverseDxRLerpTimesDxCLerp=inverseDxRLerp*dxCLerp,dxRLerpTimesInverseDxCLerp=dxRLerp*inverseDxCLerp,dxRLerpTimesDxCLerp=dxRLerp*dxCLerp;for(let d=0;d<depth;d++){let dyVal=dyValues[offset++];output[topLeftRCOffset+d]+=dyVal*inverseDxRLerpTimesInverseDxCLerp,output[topRightRCOffset+d]+=dyVal*inverseDxRLerpTimesDxCLerp,output[bottomLeftRCOffset+d]+=dyVal*dxRLerpTimesInverseDxCLerp,output[bottomRightRCOffset+d]+=dyVal*dxRLerpTimesDxCLerp}}}}return backend2.makeTensorInfo([batch,xWidth,xHeight,depth],"float32",output)}var resizeBilinearGradConfig2={kernelName:ResizeBilinearGrad,backendName:"cpu",kernelFunc:resizeBilinearGrad};function resizeNearestNeighbor2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeNearestNeighbor");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,output=new Float32Array(batch*newHeight*newWidth*numChannels),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1],outputOffset=0;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<newHeight;r++){let sourceFracRow=halfPixelCenters?effectiveRowSizeRatio*(r+.5):effectiveRowSizeRatio*r,sourceNearestRow=Math.min(oldHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));halfPixelCenters&&(sourceNearestRow=Math.max(0,sourceNearestRow));let rowOffset=batchOffset+sourceNearestRow*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=halfPixelCenters?effectiveColSizeRatio*(c+.5):effectiveColSizeRatio*c,sourceNearestCol=Math.min(oldWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));halfPixelCenters&&(sourceNearestCol=Math.max(0,sourceNearestCol));let colOffset=rowOffset+sourceNearestCol*imagesStrides[2];for(let d=0;d<numChannels;d++){let newVal=xValues[colOffset+d];output[outputOffset++]=newVal}}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],images.dtype,output)}var resizeNearestNeighborConfig={kernelName:ResizeNearestNeighbor,backendName:"cpu",kernelFunc:resizeNearestNeighbor2};function resizeNearestNeighborGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeNearestNeighborGrad");let imagesStrides=util_exports.computeStrides(images.shape),dyStrides=util_exports.computeStrides(dy.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),dyValues=backend2.data.get(dy.dataId).values,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<xHeight;r++){let rowOffset=batchOffset+r*imagesStrides[1],startRLerp=Math.floor(r*invHeightScale),startDyR=Math.floor(startRLerp-winHeight/2);for(let c=0;c<xWidth;c++){let colOffset=rowOffset+c*imagesStrides[2],startCLerp=Math.floor(c*invWidthScale),startDyC=Math.floor(startCLerp-winWidth/2);for(let d=0;d<depth;d++){let accum=0;for(let dyRIndex=0;dyRIndex<winHeight;dyRIndex++){let dyR=dyRIndex+startDyR;if(dyR<0||dyR>=yHeight)continue;let dyROffset=batchOffset+dyR*dyStrides[1],sourceFracRow=dyR*heightScale,sourceNearestRow=Math.min(xHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));if(r===sourceNearestRow)for(let dyCIndex=0;dyCIndex<winWidth;dyCIndex++){let dyC=dyCIndex+startDyC;if(dyC<0||dyC>=yWidth)continue;let dyCOffset=dyROffset+dyC*dyStrides[2],sourceFracCol=dyC*widthScale,sourceNearestCol=Math.min(xWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));c===sourceNearestCol&&(accum+=dyValues[dyCOffset+d])}}output[colOffset+d]=accum}}}}return backend2.makeTensorInfo(images.shape,images.dtype,output)}var resizeNearestNeighborGradConfig2={kernelName:ResizeNearestNeighborGrad,backendName:"cpu",kernelFunc:resizeNearestNeighborGrad};function reverse2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs;assertNotComplex(x,"reverse");let xRank=x.shape.length,$dims=util_exports.parseAxisParam(dims,x.shape);if(xRank===0)return identity2({inputs:{x},backend:backend2});let outBuf=new TensorBuffer(x.shape,x.dtype),xBuf=backend2.bufferSync(x);for(let i=0;i<outBuf.size;i++){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.slice();$dims.forEach(d=>inLoc[d]=x.shape[d]-1-inLoc[d]),outBuf.set(xBuf.get(...inLoc),...outLoc)}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var reverseConfig={kernelName:Reverse,backendName:"cpu",kernelFunc:reverse2},rotateWithOffsetConfig={kernelName:RotateWithOffset,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fullOpacityValue=255,sinFactor=Math.sin(radians),cosFactor=Math.cos(radians),imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],y=coords2[1],coordX=(x-centerX)*cosFactor-(y-centerY)*sinFactor,coordY=(x-centerX)*sinFactor+(y-centerY)*cosFactor;coordX=Math.round(coordX+centerX),coordY=Math.round(coordY+centerY);let outputValue=fillValue;if(typeof fillValue!="number"&&(channel===3?outputValue=fullOpacityValue:outputValue=fillValue[channel]),coordX>=0&&coordX<imageWidth&&coordY>=0&&coordY<imageHeight){let rotatedRowOffset=coordY*(imageWidth*numChannels),rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rotatedRowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}let outIdx=batchOffset+rowOffset+colOffset+channel;output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}},round3=unaryKernelFunc(Round,xi=>{let base2=Math.floor(xi);return xi-base2<.5?Math.floor(xi):xi-base2>.5?Math.ceil(xi):base2%2==0?base2:base2+1}),roundConfig={kernelName:Round,backendName:"cpu",kernelFunc:round3};function scatterImpl(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices){let flattenShape=[outputSize/sliceSize,sliceSize],indicesData=indices.values,updatesData=updates.values;if(outputSize===0)return buffer(shape,updates.dtype);let outBuf=buffer(flattenShape,updates.dtype);outBuf.values.fill(defaultValue);for(let i=0;i<numUpdates;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];index.push(dim),flattenIndex+=dim*strides[j]}if(flattenIndex<0||flattenIndex>=outputSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${shape}`);for(let k=0;k<sliceSize;k++)sumDupeIndices?outBuf.values[flattenIndex*sliceSize+k]+=updatesData[i*sliceSize+k]:outBuf.values[flattenIndex*sliceSize+k]=updates.rank===0?updatesData[0]:updatesData[i*sliceSize+k]}return outBuf}function scatterNd(args){let{inputs,backend:backend2,attrs}=args,{indices,updates}=inputs,{shape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),sumDupeIndices=!0,indicesBuf=backend2.bufferSync(indices),updatesBuf=backend2.bufferSync(updates),outBuf=scatterImpl(indicesBuf,updatesBuf,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,0,sumDupeIndices);return backend2.makeTensorInfo(shape,outBuf.dtype,outBuf.values)}var scatterNdConfig={kernelName:ScatterNd,backendName:"cpu",kernelFunc:scatterNd};function select(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs;assertNotComplex([condition,t,e],"select");let conditionRank=condition.shape.length,values=backend2.data.get(condition.dataId).values,tValues=backend2.data.get(t.dataId).values,eValues=backend2.data.get(e.dataId).values,resultDtype=upcastType(t.dtype,e.dtype),newValues=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape),resultDtype),index=0,offset=conditionRank===0||conditionRank>1||t.shape.length===1?1:util_exports.sizeFromShape(t.shape.slice(1));for(let i=0;i<values.length;i++)for(let j=0;j<offset;j++)values[i]===1?newValues[index++]=tValues[i]:newValues[index++]=eValues[i];return backend2.makeTensorInfo(t.shape,resultDtype,newValues)}var selectConfig={kernelName:Select,backendName:"cpu",kernelFunc:select},scaleAlpha=backend_util_exports.SELU_SCALEALPHA,scale=backend_util_exports.SELU_SCALE,selu2=unaryKernelFunc(Selu,xi=>xi>=0?scale*xi:scaleAlpha*(Math.exp(xi)-1)),seluConfig={kernelName:Selu,backendName:"cpu",kernelFunc:selu2},sigmoid2=unaryKernelFunc(Sigmoid,xi=>1/(1+Math.exp(-xi))),sigmoidConfig={kernelName:Sigmoid,backendName:"cpu",kernelFunc:sigmoid2},sign2=unaryKernelFunc(Sign,xi=>xi<0?-1:xi>0?1:0),signConfig={kernelName:Sign,backendName:"cpu",kernelFunc:sign2},sin2=unaryKernelFunc(Sin,xi=>Math.sin(xi)),sinConfig={kernelName:Sin,backendName:"cpu",kernelFunc:sin2},sinh2=unaryKernelFunc(Sinh,xi=>Math.sinh(xi)),sinhConfig={kernelName:Sinh,backendName:"cpu",kernelFunc:sinh2},epsilon2=11920928955078125e-23,threshold=Math.log(epsilon2)+2,softplus2=unaryKernelFunc(Softplus,xi=>{let tooLarge=xi>-threshold,tooSmall=xi<threshold,expX=Math.exp(xi),result;return tooSmall?result=expX:tooLarge?result=xi:result=Math.log(1+expX),result}),softplusConfig={kernelName:Softplus,backendName:"cpu",kernelFunc:softplus2};function spaceToBatchND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;assertNotComplex([x],"spaceToBatchND");let prod5=util_exports.sizeFromShape(blockShape),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=padV2Config.kernelFunc({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),paddedXReshaped=reshape3({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose2({inputs:{x:paddedXReshaped},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape3({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return backend2.disposeIntermediateTensorInfo(paddedX),backend2.disposeIntermediateTensorInfo(paddedXReshaped),backend2.disposeIntermediateTensorInfo(paddedXT),result}var spaceToBatchNDConfig={kernelName:SpaceToBatchND,backendName:"cpu",kernelFunc:spaceToBatchND2};function sparseToDense2(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,indicesBuf=backend2.bufferSync(sparseIndices),updatesBuf=backend2.bufferSync(sparseValues),$defaultValue=backend2.data.get(defaultValue.dataId).values[0],outBuf=scatterImpl(indicesBuf,updatesBuf,outputShape,outputSize,sliceSize,numUpdates,sliceRank,strides,$defaultValue,sumDupeIndices);return backend2.makeTensorInfo(outputShape,outBuf.dtype,outBuf.values)}var sparseToDenseConfig={kernelName:SparseToDense,backendName:"cpu",kernelFunc:sparseToDense2};function splitV(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[$axis]=s;let sliceT=slice2({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[$axis]+=s,sliceT})}var splitVConfig={kernelName:SplitV,backendName:"cpu",kernelFunc:splitV},sqrt2=unaryKernelFunc(Sqrt,xi=>Math.sqrt(xi)),sqrtConfig={kernelName:Sqrt,backendName:"cpu",kernelFunc:sqrt2},squareConfig={kernelName:Square,backendName:"cpu",kernelFunc:({inputs,backend:backend2})=>{let{x}=inputs,cpuBackend=backend2;assertNotComplex(x,"square");let values=cpuBackend.data.get(x.dataId).values,newValues=new Float32Array(values.length);for(let i=0;i<values.length;++i){let value=values[i];newValues[i]=value*value}return{dataId:cpuBackend.write(newValues,x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}},step2=unaryKernelFunc(Step,(xi,attrs)=>{let stepAttrs=attrs;return isNaN(xi)?NaN:xi>0?1:stepAttrs.alpha}),stepConfig={kernelName:Step,backendName:"cpu",kernelFunc:step2};function stridedSlice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs;assertNotComplex(x,"stridedSlice");let{nonStrided,$begin,$strides,size,newShape,outShape}=slice_util_exports.sliceInfo(x.shape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask),$x=reshape3({inputs:{x},backend:backend2,attrs:{shape:newShape}}),result;if(nonStrided){let sliced=slice2({inputs:{x:$x},backend:backend2,attrs:{begin:$begin,size}});result=reshape3({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(sliced)}else if(outShape.some(axis=>axis===0))result=backend2.makeTensorInfo(outShape,x.dtype,[]);else{let xBuf=backend2.bufferSync($x),outBuf=stridedSliceImpl(outShape,xBuf,$strides,$begin);result=backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:outShape}});return backend2.disposeIntermediateTensorInfo($x),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var stridedSliceConfig={kernelName:StridedSlice,backendName:"cpu",kernelFunc:stridedSlice2},tan2=unaryKernelFunc(Tan,xi=>Math.tan(xi)),tanConfig={kernelName:Tan,backendName:"cpu",kernelFunc:tan2},tanh3=unaryKernelFunc(Tanh,xi=>Math.tanh(xi)),tanhConfig={kernelName:Tanh,backendName:"cpu",kernelFunc:tanh3};function tile3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reps}=attrs;assertNotComplex(x,"tile");let outBuf=tileImpl(backend2.bufferSync(x),reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var tileConfig={kernelName:Tile,backendName:"cpu",kernelFunc:tile3};function topK(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs;assertNotComplex(x,"topk");let xVals=backend2.data.get(x.dataId).values,[allTopKVals,allTopKIndices]=topKImpl(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig={kernelName:TopK,backendName:"cpu",kernelFunc:topK};function unique3(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex(x,"unique");let values=backend2.data.get(x.dataId).values,{outputValues,outputShape,indices}=uniqueImpl(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig={kernelName:Unique,backendName:"cpu",kernelFunc:unique3};function unpack(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let valueRank=value.shape.length,num=value.shape[axis],outShape=new Array(valueRank-1),outIndex=0;for(let i=0;i<valueRank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let begin=new Array(valueRank).fill(0),size=value.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let tempRes=slice2({inputs:{x:value},backend:backend2,attrs:{begin,size}});res[i]=reshape3({inputs:{x:tempRes},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(tempRes)}return res}var unpackConfig={kernelName:Unpack,backendName:"cpu",kernelFunc:unpack};function unsortedSegmentSum2(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs;assertNotComplex(x,"unsortedSegmentSum");let xRank=x.shape.length,segmentIdsRank=segmentIds.shape.length,res=[],intermediates=[],numIters=xRank-segmentIdsRank,$segmentIds=segmentIds;for(let i=0;i<numIters;++i){let expanded=expandDims3({inputs:{input:$segmentIds},backend:backend2,attrs:{dim:i+1}});$segmentIds=expanded,intermediates.push(expanded)}for(let i=0;i<numSegments;++i){let scalarValue=util_exports.createScalarValue(i,"int32"),segmentId=backend2.makeTensorInfo([],"int32",scalarValue),mask=equal2({inputs:{a:segmentId,b:$segmentIds},backend:backend2}),maskCasted=cast3({inputs:{x:mask},backend:backend2,attrs:{dtype:"float32"}}),mul2=multiply2({inputs:{a:maskCasted,b:x},backend:backend2}),sumTensorInfo=sum3({inputs:{x:mul2},backend:backend2,attrs:{axis:0,keepDims:!1}});res.push(sumTensorInfo),intermediates.push(segmentId),intermediates.push(mask),intermediates.push(maskCasted),intermediates.push(mul2),intermediates.push(sumTensorInfo)}let result=pack({inputs:res,backend:backend2,attrs:{axis:0}});return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig={kernelName:UnsortedSegmentSum,backendName:"cpu",kernelFunc:unsortedSegmentSum2},kernelConfigs=[_fusedMatMulConfig,absConfig,acosConfig,acoshConfig,addConfig,addNConfig,allConfig,anyConfig,argMaxConfig,argMinConfig,asinConfig,asinhConfig,atanConfig,atan2Config,atanhConfig,avgPoolConfig,avgPool3DConfig,avgPool3DGradConfig2,avgPoolGradConfig2,batchMatMulConfig,batchNormConfig,batchToSpaceNDConfig,bincountConfig,castConfig,ceilConfig,clipConfig,complexConfig,complexAbsConfig,concatConfig,conv2DBackpropFilterConfig,conv2DBackpropInputConfig,conv2DConfig,conv3DBackpropFilterV2Config,conv3DBackpropInputV2Config,conv3DConfig,cosConfig,coshConfig,cropAndResizeConfig,cumsumConfig,denseBincountConfig,depthToSpaceConfig,depthwiseConv2dNativeConfig,depthwiseConv2dNativeBackpropFilterConfig,depthwiseConv2dNativeBackpropInputConfig,diagConfig,dilation2dConfig,dilation2dBackpropInputConfig,dilation2dBackpropFilterConfig,realDivConfig,eluConfig,eluGradConfig2,equalConfig,erfConfig,expConfig,expandDimsConfig,expm1Config,fftConfig,fillConfig,flipLeftRightConfig,floorConfig,floorDivConfig,fusedConv2DConfig,fusedDepthwiseConv2DConfig,gatherNdConfig,gatherV2Config,greaterConfig,greaterEqualConfig,identityConfig,ifftConfig,imagConfig,isFiniteConfig,isInfConfig,isNaNConfig,leakyReluConfig,lessConfig,lessEqualConfig,linSpaceConfig,logConfig,log1pConfig,logicalAndConfig,logicalNotConfig,logicalOrConfig,lRNConfig,lRNGradConfig,maximumConfig,maxPoolConfig,maxPool3DConfig,maxPool3DGradConfig2,maxPoolGradConfig2,maxPoolWithArgmaxConfig,maxConfig,meanConfig,minConfig,minimumConfig,mirrorPadConfig,modConfig,multinomialConfig,multiplyConfig,negConfig,nonMaxSuppressionV3Config,nonMaxSuppressionV4Config,nonMaxSuppressionV5Config,notEqualConfig,oneHotConfig,onesLikeConfig,packConfig,padV2Config,powConfig,preluConfig,prodConfig,rangeConfig,realConfig,reciprocalConfig,reluConfig,relu6Config,reshapeConfig,resizeBilinearConfig,resizeBilinearGradConfig2,resizeNearestNeighborConfig,resizeNearestNeighborGradConfig2,reverseConfig,rotateWithOffsetConfig,roundConfig,rsqrtConfig,scatterNdConfig,selectConfig,seluConfig,sigmoidConfig,signConfig,sinConfig,sinhConfig,sliceConfig,softmaxConfig,softplusConfig,spaceToBatchNDConfig,sparseToDenseConfig,splitVConfig,sqrtConfig,squareConfig,squaredDifferenceConfig,stepConfig,stridedSliceConfig,subConfig,sumConfig,tanConfig,tanhConfig,tileConfig,topKConfig,transposeConfig,uniqueConfig,unpackConfig,unsortedSegmentSumConfig,zerosLikeConfig];for(let kernelConfig of kernelConfigs)registerKernel(kernelConfig);var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(webGLVersion,gl){contexts[webGLVersion]=gl}function getWebGLContext(webGLVersion){if(!(webGLVersion in contexts)){let newCtx=getWebGLRenderingContext(webGLVersion);if(newCtx!==null)contexts[webGLVersion]=newCtx;else return console.log("Could not get context for WebGL version",webGLVersion),null}let gl=contexts[webGLVersion];return gl.isContextLost()?(delete contexts[webGLVersion],getWebGLContext(webGLVersion)):(gl.disable(gl.DEPTH_TEST),gl.disable(gl.STENCIL_TEST),gl.disable(gl.BLEND),gl.disable(gl.DITHER),gl.disable(gl.POLYGON_OFFSET_FILL),gl.disable(gl.SAMPLE_COVERAGE),gl.enable(gl.SCISSOR_TEST),gl.enable(gl.CULL_FACE),gl.cullFace(gl.BACK),contexts[webGLVersion])}function createCanvas(webGLVersion){if(typeof OffscreenCanvas!="undefined"&&webGLVersion===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function getWebGLRenderingContext(webGLVersion){if(webGLVersion!==1&&webGLVersion!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let canvas=createCanvas(webGLVersion);return canvas.addEventListener("webglcontextlost",ev=>{ev.preventDefault(),delete contexts[webGLVersion]},!1),webGLVersion===1?canvas.getContext("webgl",WEBGL_ATTRIBUTES)||canvas.getContext("experimental-webgl",WEBGL_ATTRIBUTES):canvas.getContext("webgl2",WEBGL_ATTRIBUTES)}var PackingScheme;(function(PackingScheme2){PackingScheme2[PackingScheme2.DENSE=0]="DENSE",PackingScheme2[PackingScheme2.SHARED_BATCH=1]="SHARED_BATCH"})(PackingScheme||(PackingScheme={}));var TextureUsage;(function(TextureUsage2){TextureUsage2[TextureUsage2.RENDER=0]="RENDER",TextureUsage2[TextureUsage2.UPLOAD=1]="UPLOAD",TextureUsage2[TextureUsage2.PIXELS=2]="PIXELS",TextureUsage2[TextureUsage2.DOWNLOAD=3]="DOWNLOAD"})(TextureUsage||(TextureUsage={}));var PhysicalTextureType;(function(PhysicalTextureType2){PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(PhysicalTextureType||(PhysicalTextureType={}));function getUnpackedMatrixTextureShapeWidthHeight(rows,columns){return[columns,rows]}function getUnpackedArraySizeFromMatrixSize(matrixSize,channelsPerTexture){return matrixSize*channelsPerTexture}function getDenseTexShape(shape){let size=util_exports.sizeFromShape(shape),texelsNeeded=Math.ceil(size/4);return util_exports.sizeToSquarishShape(texelsNeeded)}function getPackedMatrixTextureShapeWidthHeight(rows,columns){return[Math.max(1,Math.ceil(columns/2)),Math.max(1,Math.ceil(rows/2))]}function getPackedRGBAArraySizeFromMatrixShape(rows,columns){let[w,h]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return w*h*4}function getTextureConfig(gl,textureHalfFloatExtension){let glany=gl,internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat;return env().getNumber("WEBGL_VERSION")===2?(internalFormatFloat=glany.R32F,internalFormatHalfFloat=glany.R16F,internalFormatPackedHalfFloat=glany.RGBA16F,internalFormatPackedFloat=glany.RGBA32F,textureFormatFloat=glany.RED,downloadUnpackNumChannels=4,defaultNumChannels=1,textureTypeHalfFloat=glany.HALF_FLOAT,textureTypeFloat=glany.FLOAT):(internalFormatFloat=gl.RGBA,internalFormatHalfFloat=gl.RGBA,internalFormatPackedHalfFloat=gl.RGBA,internalFormatPackedFloat=glany.RGBA,textureFormatFloat=gl.RGBA,downloadUnpackNumChannels=4,defaultNumChannels=4,textureTypeHalfFloat=textureHalfFloatExtension!=null?textureHalfFloatExtension.HALF_FLOAT_OES:null,textureTypeFloat=gl.FLOAT),downloadTextureFormat=gl.RGBA,{internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat}}function callAndCheck(gl,func2){let returnValue=func2();return env().getBool("DEBUG")&&checkWebGLError(gl),returnValue}function checkWebGLError(gl){let error=gl.getError();if(error!==gl.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(gl,error))}var MIN_FLOAT16=596e-10,MAX_FLOAT16=65504;function canBeRepresented(num){return!!(env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||num===0||MIN_FLOAT16<Math.abs(num)&&Math.abs(num)<MAX_FLOAT16)}function getWebGLErrorMessage(gl,status){switch(status){case gl.NO_ERROR:return"NO_ERROR";case gl.INVALID_ENUM:return"INVALID_ENUM";case gl.INVALID_VALUE:return"INVALID_VALUE";case gl.INVALID_OPERATION:return"INVALID_OPERATION";case gl.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case gl.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case gl.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${status}`}}function getExtensionOrThrow(gl,extensionName){return throwIfNull(gl,()=>gl.getExtension(extensionName),'Extension "'+extensionName+'" not supported on this browser.')}function createVertexShader(gl,vertexShaderSource){let vertexShader=throwIfNull(gl,()=>gl.createShader(gl.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(vertexShader,vertexShaderSource)),callAndCheck(gl,()=>gl.compileShader(vertexShader)),gl.getShaderParameter(vertexShader,gl.COMPILE_STATUS)===!1)throw console.log(gl.getShaderInfoLog(vertexShader)),new Error("Failed to compile vertex shader.");return vertexShader}function createFragmentShader(gl,fragmentShaderSource){let fragmentShader=throwIfNull(gl,()=>gl.createShader(gl.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(fragmentShader,fragmentShaderSource)),callAndCheck(gl,()=>gl.compileShader(fragmentShader)),gl.getShaderParameter(fragmentShader,gl.COMPILE_STATUS)===!1)throw logShaderSourceAndInfoLog(fragmentShaderSource,gl.getShaderInfoLog(fragmentShader)),new Error("Failed to compile fragment shader.");return fragmentShader}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(shaderSource,shaderInfoLog){let lineNumberRegexResult=lineNumberRegex.exec(shaderInfoLog);if(lineNumberRegexResult==null){console.log(`Couldn't parse line number in error: ${shaderInfoLog}`),console.log(shaderSource);return}let lineNumber=+lineNumberRegexResult[1],shaderLines=shaderSource.split(`
 `),pad3=shaderLines.length.toString().length+2,linesWithLineNumbers=shaderLines.map((line,lineNumber2)=>util_exports.rightPad((lineNumber2+1).toString(),pad3)+line),maxLineLength=0;for(let i=0;i<linesWithLineNumbers.length;i++)maxLineLength=Math.max(linesWithLineNumbers[i].length,maxLineLength);let beforeErrorLines=linesWithLineNumbers.slice(0,lineNumber-1),errorLine=linesWithLineNumbers.slice(lineNumber-1,lineNumber),afterErrorLines=linesWithLineNumbers.slice(lineNumber);console.log(beforeErrorLines.join(`
 `)),console.log(shaderInfoLog.split(`
 `)[0]),console.log(`%c ${util_exports.rightPad(errorLine[0],maxLineLength)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(afterErrorLines.join(`
-`))}function createProgram(gl){return throwIfNull(gl,()=>gl.createProgram(),"Unable to create WebGLProgram.")}function linkProgram(gl,program){if(callAndCheck(gl,()=>gl.linkProgram(program)),gl.getProgramParameter(program,gl.LINK_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(gl,program){if(callAndCheck(gl,()=>gl.validateProgram(program)),gl.getProgramParameter(program,gl.VALIDATE_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createStaticIndexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createTexture(gl){return throwIfNull(gl,()=>gl.createTexture(),"Unable to create WebGLTexture.")}function validateTextureSize(width,height){let maxTextureSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(width<=0||height<=0){let requested=`[${width}x${height}]`;throw new Error("Requested texture size "+requested+" is invalid.")}if(width>maxTextureSize||height>maxTextureSize){let requested=`[${width}x${height}]`,max6=`[${maxTextureSize}x${maxTextureSize}]`;throw new Error("Requested texture size "+requested+" greater than WebGL maximum on this browser / GPU "+max6+".")}}function createFramebuffer(gl){return throwIfNull(gl,()=>gl.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(gl,program,attribute,buffer2,arrayEntriesPerItem,itemStrideInBytes,itemOffsetInBytes){let loc=gl.getAttribLocation(program,attribute);return loc===-1?!1:(callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.vertexAttribPointer(loc,arrayEntriesPerItem,gl.FLOAT,!1,itemStrideInBytes,itemOffsetInBytes)),callAndCheck(gl,()=>gl.enableVertexAttribArray(loc)),!0)}function bindTextureUnit(gl,texture,textureUnit){validateTextureUnit(gl,textureUnit),callAndCheck(gl,()=>gl.activeTexture(gl.TEXTURE0+textureUnit)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture))}function getProgramUniformLocationOrThrow(gl,program,uniformName){return throwIfNull(gl,()=>gl.getUniformLocation(program,uniformName),'uniform "'+uniformName+'" not present in program.')}function getProgramUniformLocation(gl,program,uniformName){return gl.getUniformLocation(program,uniformName)}function bindTextureToProgramUniformSampler(gl,texture,uniformSamplerLocation,textureUnit){callAndCheck(gl,()=>bindTextureUnit(gl,texture,textureUnit)),callAndCheck(gl,()=>gl.uniform1i(uniformSamplerLocation,textureUnit))}function bindColorTextureToFramebuffer(gl,texture,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0))}function unbindColorTextureFromFramebuffer(gl,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,null,0))}function validateFramebuffer(gl){let status=gl.checkFramebufferStatus(gl.FRAMEBUFFER);if(status!==gl.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(gl,status))}function getFramebufferErrorMessage(gl,status){switch(status){case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case gl.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${status}`}}function throwIfNull(gl,returnTOrNull,failureMessage){let tOrNull=callAndCheck(gl,()=>returnTOrNull());if(tOrNull==null)throw new Error(failureMessage);return tOrNull}function validateTextureUnit(gl,textureUnit){let maxTextureUnit=gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,glTextureUnit=textureUnit+gl.TEXTURE0;if(glTextureUnit<gl.TEXTURE0||glTextureUnit>maxTextureUnit){let textureUnitRange=`[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;throw new Error(`textureUnit must be in ${textureUnitRange}.`)}}function getBatchDim(shape,dimsToSkip=2){return util_exports.sizeFromShape(shape.slice(0,shape.length-dimsToSkip))}function getRowsCols(shape){if(shape.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[shape.length>1?shape[shape.length-2]:1,shape[shape.length-1]]}function getShapeAs3D(shape){let shapeAs3D=[1,1,1];return shape.length===0||shape.length===1&&shape[0]===1||(shapeAs3D=[getBatchDim(shape),...getRowsCols(shape)]),shapeAs3D}function getTextureShapeFromLogicalShape(logShape,isPacked=!1){let maxTexSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");isPacked&&(maxTexSize=maxTexSize*2,logShape=logShape.map((d,i)=>i>=logShape.length-2?util_exports.nearestLargerEven(logShape[i]):logShape[i]),logShape.length===1&&(logShape=[2,logShape[0]])),logShape.length!==2&&(logShape=util_exports.squeezeShape(logShape).newShape);let size=util_exports.sizeFromShape(logShape);if(logShape.length<=1&&size<=maxTexSize)return[1,size];if(logShape.length===2&&logShape[0]<=maxTexSize&&logShape[1]<=maxTexSize)return logShape;if(logShape.length===3&&logShape[0]*logShape[1]<=maxTexSize&&logShape[2]<=maxTexSize)return[logShape[0]*logShape[1],logShape[2]];if(logShape.length===3&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]];if(logShape.length===4&&logShape[0]*logShape[1]*logShape[2]<=maxTexSize&&logShape[3]<=maxTexSize)return[logShape[0]*logShape[1]*logShape[2],logShape[3]];if(logShape.length===4&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]*logShape[3]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]*logShape[3]];if(isPacked){let batchDim=getBatchDim(logShape),rows=2,cols=2;return logShape.length&&([rows,cols]=getRowsCols(logShape)),size=batchDim*(rows/2)*(cols/2),util_exports.sizeToSquarishShape(size).map(d=>d*2)}return util_exports.sizeToSquarishShape(size)}function isEven(n){return n%2===0}function isReshapeFree(shape1,shape2){if(shape1=shape1.slice(-2),shape2=shape2.slice(-2),util_exports.arraysEqual(shape1,shape2))return!0;if(!shape1.length||!shape2.length)return!0;if(shape1[0]===0||shape1[1]===0||shape2[0]===0||shape2[1]===0)return!0;if(shape1.length!==shape2.length){let shape1Cols=shape1.slice(-1)[0],shape2Cols=shape2.slice(-1)[0];if(shape1Cols===shape2Cols)return!0;if(isEven(shape1Cols)&&isEven(shape2Cols)&&(shape1[0]===1||shape2[0]===1))return!0}return shape1[1]===shape2[1]&&isEven(shape1[0])&&isEven(shape2[0])}var MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER;function getWebGLMaxTextureSize(webGLVersion){if(MAX_TEXTURE_SIZE==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURE_SIZE=gl.getParameter(gl.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(webGLVersion){if(MAX_TEXTURES_IN_SHADER==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURES_IN_SHADER=gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(webGLVersion){if(webGLVersion===0)return 0;let queryTimerVersion,gl=getWebGLContext(webGLVersion);return hasExtension(gl,"EXT_disjoint_timer_query_webgl2")&&webGLVersion===2?queryTimerVersion=2:hasExtension(gl,"EXT_disjoint_timer_query")?queryTimerVersion=1:queryTimerVersion=0,queryTimerVersion}function hasExtension(gl,extensionName){return gl.getExtension(extensionName)!=null}function isWebGLVersionEnabled(webGLVersion){try{if(getWebGLContext(webGLVersion)!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function isCapableOfRenderingToFloatTexture(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1}else if(!hasExtension(gl,"EXT_color_buffer_float"))return!1;return createFloatTextureAndBindToFramebuffer(gl)}function isDownloadFloatTextureEnabled(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1;if(!hasExtension(gl,"WEBGL_color_buffer_float"))return!1}else{if(hasExtension(gl,"EXT_color_buffer_float"))return createFloatTextureAndBindToFramebuffer(gl);let COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(hasExtension(gl,COLOR_BUFFER_HALF_FLOAT)){let textureHalfFloatExtension=gl.getExtension(COLOR_BUFFER_HALF_FLOAT);return createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension)}return!1}return createFloatTextureAndBindToFramebuffer(gl)}function createFloatTextureAndBindToFramebuffer(gl){let texConfig=getTextureConfig(gl),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension){let texConfig=getTextureConfig(gl,textureHalfFloatExtension),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatHalfFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeHalfFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function isWebGLFenceEnabled(webGLVersion){return webGLVersion!==2?!1:getWebGLContext(webGLVersion).fenceSync!=null}function assertNotComplex2(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the WebGL backend.`)})}var ENV3=env();ENV3.registerFlag("HAS_WEBGL",()=>ENV3.getNumber("WEBGL_VERSION")>0);ENV3.registerFlag("WEBGL_VERSION",()=>isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0);ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ENV3.get("WEBGL_VERSION")===2);ENV3.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ENV3.registerFlag("WEBGL_PACK",()=>ENV3.getBool("HAS_WEBGL"));ENV3.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_CLIP",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_REDUCE",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_LAZILY_UNPACK",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_CONV_IM2COL",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let webGLVersion=ENV3.getNumber("WEBGL_VERSION");return webGLVersion===0?0:getWebGLDisjointQueryTimerVersion(webGLVersion)});ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!device_util_exports.isMobile());ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ENV3.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_FENCE_API_ENABLED",()=>isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,threshold2=>{if(threshold2<0&&threshold2!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`)});function getGlslDifferences(){let version102,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound;return env().getNumber("WEBGL_VERSION")===2?(version102="#version 300 es",attribute="in",varyingVs="out",varyingFs="in",texture2D="texture",output="outputColor",defineOutput="out vec4 outputColor;",defineSpecialNaN=`
+`))}function createProgram(gl){return throwIfNull(gl,()=>gl.createProgram(),"Unable to create WebGLProgram.")}function linkProgram(gl,program){if(callAndCheck(gl,()=>gl.linkProgram(program)),gl.getProgramParameter(program,gl.LINK_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(gl,program){if(callAndCheck(gl,()=>gl.validateProgram(program)),gl.getProgramParameter(program,gl.VALIDATE_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createStaticIndexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createTexture(gl){return throwIfNull(gl,()=>gl.createTexture(),"Unable to create WebGLTexture.")}function validateTextureSize(width,height){let maxTextureSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(width<=0||height<=0){let requested=`[${width}x${height}]`;throw new Error("Requested texture size "+requested+" is invalid.")}if(width>maxTextureSize||height>maxTextureSize){let requested=`[${width}x${height}]`,max6=`[${maxTextureSize}x${maxTextureSize}]`;throw new Error("Requested texture size "+requested+" greater than WebGL maximum on this browser / GPU "+max6+".")}}function createFramebuffer(gl){return throwIfNull(gl,()=>gl.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(gl,program,attribute,buffer2,arrayEntriesPerItem,itemStrideInBytes,itemOffsetInBytes){let loc=gl.getAttribLocation(program,attribute);return loc===-1?!1:(callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.vertexAttribPointer(loc,arrayEntriesPerItem,gl.FLOAT,!1,itemStrideInBytes,itemOffsetInBytes)),callAndCheck(gl,()=>gl.enableVertexAttribArray(loc)),!0)}function bindTextureUnit(gl,texture,textureUnit){validateTextureUnit(gl,textureUnit),callAndCheck(gl,()=>gl.activeTexture(gl.TEXTURE0+textureUnit)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture))}function getProgramUniformLocationOrThrow(gl,program,uniformName){return throwIfNull(gl,()=>gl.getUniformLocation(program,uniformName),'uniform "'+uniformName+'" not present in program.')}function getProgramUniformLocation(gl,program,uniformName){return gl.getUniformLocation(program,uniformName)}function bindTextureToProgramUniformSampler(gl,texture,uniformSamplerLocation,textureUnit){callAndCheck(gl,()=>bindTextureUnit(gl,texture,textureUnit)),callAndCheck(gl,()=>gl.uniform1i(uniformSamplerLocation,textureUnit))}function bindColorTextureToFramebuffer(gl,texture,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0))}function unbindColorTextureFromFramebuffer(gl,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,null,0))}function validateFramebuffer(gl){let status=gl.checkFramebufferStatus(gl.FRAMEBUFFER);if(status!==gl.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(gl,status))}function getFramebufferErrorMessage(gl,status){switch(status){case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case gl.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${status}`}}function throwIfNull(gl,returnTOrNull,failureMessage){let tOrNull=callAndCheck(gl,()=>returnTOrNull());if(tOrNull==null)throw new Error(failureMessage);return tOrNull}function validateTextureUnit(gl,textureUnit){let maxTextureUnit=gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,glTextureUnit=textureUnit+gl.TEXTURE0;if(glTextureUnit<gl.TEXTURE0||glTextureUnit>maxTextureUnit){let textureUnitRange=`[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;throw new Error(`textureUnit must be in ${textureUnitRange}.`)}}function getBatchDim(shape,dimsToSkip=2){return util_exports.sizeFromShape(shape.slice(0,shape.length-dimsToSkip))}function getRowsCols(shape){if(shape.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[shape.length>1?shape[shape.length-2]:1,shape[shape.length-1]]}function getShapeAs3D(shape){let shapeAs3D=[1,1,1];return shape.length===0||shape.length===1&&shape[0]===1||(shapeAs3D=[getBatchDim(shape),...getRowsCols(shape)]),shapeAs3D}function getTextureShapeFromLogicalShape(logShape,isPacked=!1){let maxTexSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");isPacked&&(maxTexSize=maxTexSize*2,logShape=logShape.map((d,i)=>i>=logShape.length-2?util_exports.nearestLargerEven(logShape[i]):logShape[i]),logShape.length===1&&(logShape=[2,logShape[0]])),logShape.length!==2&&(logShape=util_exports.squeezeShape(logShape).newShape);let size=util_exports.sizeFromShape(logShape);if(logShape.length<=1&&size<=maxTexSize)return[1,size];if(logShape.length===2&&logShape[0]<=maxTexSize&&logShape[1]<=maxTexSize)return logShape;if(logShape.length===3&&logShape[0]*logShape[1]<=maxTexSize&&logShape[2]<=maxTexSize)return[logShape[0]*logShape[1],logShape[2]];if(logShape.length===3&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]];if(logShape.length===4&&logShape[0]*logShape[1]*logShape[2]<=maxTexSize&&logShape[3]<=maxTexSize)return[logShape[0]*logShape[1]*logShape[2],logShape[3]];if(logShape.length===4&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]*logShape[3]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]*logShape[3]];if(isPacked){let batchDim=getBatchDim(logShape),rows=2,cols=2;return logShape.length&&([rows,cols]=getRowsCols(logShape)),size=batchDim*(rows/2)*(cols/2),util_exports.sizeToSquarishShape(size).map(d=>d*2)}return util_exports.sizeToSquarishShape(size)}function isEven(n){return n%2==0}function isReshapeFree(shape1,shape2){if(shape1=shape1.slice(-2),shape2=shape2.slice(-2),util_exports.arraysEqual(shape1,shape2)||!shape1.length||!shape2.length||shape1[0]===0||shape1[1]===0||shape2[0]===0||shape2[1]===0)return!0;if(shape1.length!==shape2.length){let shape1Cols=shape1.slice(-1)[0],shape2Cols=shape2.slice(-1)[0];if(shape1Cols===shape2Cols||isEven(shape1Cols)&&isEven(shape2Cols)&&(shape1[0]===1||shape2[0]===1))return!0}return shape1[1]===shape2[1]&&isEven(shape1[0])&&isEven(shape2[0])}var MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER;function getWebGLMaxTextureSize(webGLVersion){if(MAX_TEXTURE_SIZE==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURE_SIZE=gl.getParameter(gl.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(webGLVersion){if(MAX_TEXTURES_IN_SHADER==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURES_IN_SHADER=gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(webGLVersion){if(webGLVersion===0)return 0;let queryTimerVersion,gl=getWebGLContext(webGLVersion);return hasExtension(gl,"EXT_disjoint_timer_query_webgl2")&&webGLVersion===2?queryTimerVersion=2:hasExtension(gl,"EXT_disjoint_timer_query")?queryTimerVersion=1:queryTimerVersion=0,queryTimerVersion}function hasExtension(gl,extensionName){return gl.getExtension(extensionName)!=null}function isWebGLVersionEnabled(webGLVersion){try{if(getWebGLContext(webGLVersion)!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function isCapableOfRenderingToFloatTexture(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1}else if(!hasExtension(gl,"EXT_color_buffer_float"))return!1;return createFloatTextureAndBindToFramebuffer(gl)}function isDownloadFloatTextureEnabled(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float")||!hasExtension(gl,"WEBGL_color_buffer_float"))return!1}else{if(hasExtension(gl,"EXT_color_buffer_float"))return createFloatTextureAndBindToFramebuffer(gl);let COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(hasExtension(gl,COLOR_BUFFER_HALF_FLOAT)){let textureHalfFloatExtension=gl.getExtension(COLOR_BUFFER_HALF_FLOAT);return createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension)}return!1}return createFloatTextureAndBindToFramebuffer(gl)}function createFloatTextureAndBindToFramebuffer(gl){let texConfig=getTextureConfig(gl),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension){let texConfig=getTextureConfig(gl,textureHalfFloatExtension),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatHalfFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeHalfFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function isWebGLFenceEnabled(webGLVersion){return webGLVersion!==2?!1:getWebGLContext(webGLVersion).fenceSync!=null}function assertNotComplex2(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the WebGL backend.`)})}var ENV3=env();ENV3.registerFlag("HAS_WEBGL",()=>ENV3.getNumber("WEBGL_VERSION")>0);ENV3.registerFlag("WEBGL_VERSION",()=>isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0);ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ENV3.get("WEBGL_VERSION")===2);ENV3.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ENV3.registerFlag("WEBGL_PACK",()=>ENV3.getBool("HAS_WEBGL"));ENV3.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_CLIP",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_REDUCE",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_LAZILY_UNPACK",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_CONV_IM2COL",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let webGLVersion=ENV3.getNumber("WEBGL_VERSION");return webGLVersion===0?0:getWebGLDisjointQueryTimerVersion(webGLVersion)});ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!device_util_exports.isMobile());ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ENV3.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_FENCE_API_ENABLED",()=>isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,threshold2=>{if(threshold2<0&&threshold2!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`)});function getGlslDifferences(){let version102,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound;return env().getNumber("WEBGL_VERSION")===2?(version102="#version 300 es",attribute="in",varyingVs="out",varyingFs="in",texture2D="texture",output="outputColor",defineOutput="out vec4 outputColor;",defineSpecialNaN=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -269,7 +269,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return createVertexShader(gl,vertexShaderSource)}function createVertexBuffer(gl){let vertexArray=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return createStaticVertexBuffer(gl,vertexArray)}function createIndexBuffer(gl){let triangleVertexIndices=new Uint16Array([0,1,2,2,1,3]);return createStaticIndexBuffer(gl,triangleVertexIndices)}function createAndConfigureTexture(gl,width,height,internalFormat,textureFormat,textureType){validateTextureSize(width,height);let texture=createTexture(gl),tex2d=gl.TEXTURE_2D;return callAndCheck(gl,()=>gl.bindTexture(tex2d,texture)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MIN_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MAG_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texImage2D(tex2d,0,internalFormat,width,height,0,textureFormat,textureType,null)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null)),texture}function getInternalFormatForFloat32MatrixTexture(textureConfig){return textureConfig.internalFormatFloat}function createFloat32MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat32MatrixTexture(textureConfig),textureConfig.textureFormatFloat,gl.FLOAT)}function getInternalFormatForFloat16MatrixTexture(textureConfig){return textureConfig.internalFormatHalfFloat}function createFloat16MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16MatrixTexture(textureConfig),textureConfig.textureFormatFloat,textureConfig.textureTypeHalfFloat)}function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig){return textureConfig.downloadTextureFormat}function createUnsignedBytesMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForUnsignedBytesMatrixTexture(textureConfig),gl.RGBA,gl.UNSIGNED_BYTE)}function getInternalFormatForPackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedFloat}function createPackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForPackedMatrixTexture(textureConfig),gl.RGBA,gl.FLOAT)}function getInternalFormatForFloat16PackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedHalfFloat}function createFloat16PackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16PackedMatrixTexture(textureConfig),gl.RGBA,textureConfig.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(gl,program,vertexBuffer){let posOffset=0,uvOffset=3*4,stride=3*4+2*4;return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,vertexBuffer)),bindVertexBufferToProgramAttribute(gl,program,"clipSpacePos",vertexBuffer,3,stride,posOffset)&&bindVertexBufferToProgramAttribute(gl,program,"uv",vertexBuffer,2,stride,uvOffset)}function uploadDenseMatrixToTexture(gl,texture,width,height,data,textureConfig){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture));let dataForUpload,texelDataType,internalFormat;data instanceof Uint8Array?(dataForUpload=new Uint8Array(width*height*4),texelDataType=gl.UNSIGNED_BYTE,internalFormat=gl.RGBA):(dataForUpload=new Float32Array(width*height*4),texelDataType=gl.FLOAT,internalFormat=textureConfig.internalFormatPackedFloat),dataForUpload.set(data),callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,internalFormat,width,height,0,gl.RGBA,texelDataType,dataForUpload)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function uploadPixelDataToTexture(gl,texture,pixels){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture)),pixels.data instanceof Uint8Array?callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,pixels.width,pixels.height,0,gl.RGBA,gl.UNSIGNED_BYTE,pixels.data)):callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,pixels)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function createBufferFromOutputTexture(gl2,rows,columns,textureConfig){let buffer2=gl2.createBuffer();callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2));let bufferSizeBytes=4*4*rows*columns;return callAndCheck(gl2,()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER,bufferSizeBytes,gl2.STREAM_READ)),callAndCheck(gl2,()=>gl2.readPixels(0,0,columns,rows,gl2.RGBA,gl2.FLOAT,0)),callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null)),buffer2}function downloadFloat32MatrixFromBuffer(gl,buffer2,size){let gl2=gl,downloadTarget=new Float32Array(size);return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadByteEncodedFloatMatrixFromOutputTexture(gl,rows,columns,textureConfig){let[w,h]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns),numChannels=4,downloadTarget=new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows*columns,numChannels));return callAndCheck(gl,()=>gl.readPixels(0,0,w,h,textureConfig.downloadTextureFormat,gl.UNSIGNED_BYTE,downloadTarget)),new Float32Array(downloadTarget.buffer)}function downloadPackedMatrixFromBuffer(gl,buffer2,batch,rows,cols,physicalRows,physicalCols,textureConfig){let gl2=gl,downloadTarget=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows,physicalCols));return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadMatrixFromPackedOutputTexture(gl,physicalRows,physicalCols){let packedRGBA=new Float32Array(physicalRows*physicalCols*4);return callAndCheck(gl,()=>gl.readPixels(0,0,physicalCols,physicalRows,gl.RGBA,gl.FLOAT,packedRGBA)),packedRGBA}var GPGPUContext=class{constructor(gl){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let glVersion=env().getNumber("WEBGL_VERSION");gl!=null?(this.gl=gl,setWebGLContext(glVersion,gl)):this.gl=getWebGLContext(glVersion);let COLOR_BUFFER_FLOAT="WEBGL_color_buffer_float",COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(env().getNumber("WEBGL_VERSION")===1){let TEXTURE_FLOAT="OES_texture_float",TEXTURE_HALF_FLOAT="OES_texture_half_float";if(this.textureFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_FLOAT),hasExtension(this.gl,TEXTURE_HALF_FLOAT))this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT),hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=getExtensionOrThrow(this.gl,COLOR_BUFFER_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(COLOR_BUFFER_FLOAT="EXT_color_buffer_float",hasExtension(this.gl,COLOR_BUFFER_FLOAT))this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT);else if(hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}get debug(){return env().getBool("DEBUG")}dispose(){if(!this.disposed){this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let gl=this.gl;callAndCheck(gl,()=>gl.finish()),callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,null)),callAndCheck(gl,()=>gl.deleteFramebuffer(this.framebuffer)),callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.deleteBuffer(this.indexBuffer)),this.disposed=!0}}createFloat32MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,rows,columns,this.textureConfig)}createFloat16MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,rows,columns,this.textureConfig)}createUnsignedBytesMatrixTexture(rows,columns){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,rows,columns,this.textureConfig)}uploadPixelDataToTexture(texture,pixels){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,texture,pixels)}uploadDenseMatrixToTexture(texture,width,height,data){this.throwIfDisposed(),uploadDenseMatrixToTexture(this.gl,texture,width,height,data,this.textureConfig)}createFloat16PackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}createPackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}deleteMatrixTexture(texture){this.throwIfDisposed(),this.outputTexture===texture&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,()=>this.gl.deleteTexture(texture))}downloadByteEncodedFloatMatrixFromOutputTexture(texture,rows,columns){return this.downloadMatrixDriver(texture,()=>downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,rows,columns,this.textureConfig))}downloadPackedMatrixFromBuffer(buffer2,batch,rows,columns,physicalRows,physicalCols){return downloadPackedMatrixFromBuffer(this.gl,buffer2,batch,rows,columns,physicalRows,physicalCols,this.textureConfig)}downloadFloat32MatrixFromBuffer(buffer2,size){return downloadFloat32MatrixFromBuffer(this.gl,buffer2,size)}createBufferFromTexture(texture,rows,columns){this.bindTextureToFrameBuffer(texture);let result=createBufferFromOutputTexture(this.gl,rows,columns,this.textureConfig);return this.unbindTextureToFrameBuffer(),result}createAndWaitForFence(){let fenceContext=this.createFence(this.gl);return this.pollFence(fenceContext)}createFence(gl){let query,isFencePassed;if(env().getBool("WEBGL_FENCE_API_ENABLED")){let gl2=gl,sync=gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE,0);gl.flush(),isFencePassed=()=>{let status=gl2.clientWaitSync(sync,0,0);return status===gl2.ALREADY_SIGNALED||status===gl2.CONDITION_SATISFIED},query=sync}else env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(query=this.beginQuery(),this.endQuery(),isFencePassed=()=>this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):isFencePassed=()=>!0;return{query,isFencePassed}}downloadMatrixFromPackedTexture(texture,physicalRows,physicalCols){return this.downloadMatrixDriver(texture,()=>downloadMatrixFromPackedOutputTexture(this.gl,physicalRows,physicalCols))}createProgram(fragmentShaderSource){this.throwIfDisposed();let gl=this.gl,fragmentShader=createFragmentShader(gl,fragmentShaderSource),vertexShader=createVertexShader2(gl),program=createProgram(gl);return callAndCheck(gl,()=>gl.attachShader(program,vertexShader)),callAndCheck(gl,()=>gl.attachShader(program,fragmentShader)),linkProgram(gl,program),this.debug&&validateProgram(gl,program),this.vertexAttrsAreBound||(this.setProgram(program),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(gl,this.program,this.vertexBuffer)),program}deleteProgram(program){this.throwIfDisposed(),program===this.program&&(this.program=null),program!=null&&callAndCheck(this.gl,()=>this.gl.deleteProgram(program))}setProgram(program){this.throwIfDisposed(),this.program=program,this.program!=null&&this.debug&&validateProgram(this.gl,this.program),callAndCheck(this.gl,()=>this.gl.useProgram(program))}getUniformLocation(program,uniformName,shouldThrow=!0){return this.throwIfDisposed(),shouldThrow?getProgramUniformLocationOrThrow(this.gl,program,uniformName):getProgramUniformLocation(this.gl,program,uniformName)}getAttributeLocation(program,attribute){return this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.getAttribLocation(program,attribute))}getUniformLocationNoThrow(program,uniformName){return this.throwIfDisposed(),this.gl.getUniformLocation(program,uniformName)}setInputMatrixTexture(inputMatrixTexture,uniformLocation,textureUnit){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,inputMatrixTexture,uniformLocation,textureUnit)}setOutputMatrixTexture(outputMatrixTexture,rows,columns){this.setOutputMatrixTextureDriver(outputMatrixTexture,columns,rows)}setOutputPackedMatrixTexture(outputPackedMatrixTexture,rows,columns){this.throwIfDisposed();let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);this.setOutputMatrixTextureDriver(outputPackedMatrixTexture,width,height)}setOutputMatrixWriteRegion(startRow,numRows,startColumn,numColumns){this.setOutputMatrixWriteRegionDriver(startColumn,startRow,numColumns,numRows)}setOutputPackedMatrixWriteRegion(startRow,numRows,startColumn,numColumns){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let gl=this.gl;this.debug&&this.debugValidate(),callAndCheck(gl,()=>gl.drawElements(gl.TRIANGLES,6,gl.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2(),query2=gl2.createQuery();return gl2.beginQuery(ext2.TIME_ELAPSED_EXT,query2),query2}let ext=this.getQueryTimerExtensionWebGL1(),query=ext.createQueryEXT();return ext.beginQueryEXT(ext.TIME_ELAPSED_EXT,query),query}endQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2();gl2.endQuery(ext2.TIME_ELAPSED_EXT);return}let ext=this.getQueryTimerExtensionWebGL1();ext.endQueryEXT(ext.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(query){return await util_exports.repeatedTry(()=>this.disposed||this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(query,queryTimerVersion){if(queryTimerVersion===0)return null;if(queryTimerVersion===2){let gl2=this.gl;return gl2.getQueryParameter(query,gl2.QUERY_RESULT)/1e6}else{let ext=this.getQueryTimerExtensionWebGL1();return ext.getQueryObjectEXT(query,ext.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(query,queryTimerVersion){if(queryTimerVersion===0)return!0;if(queryTimerVersion===2){let gl2=this.gl,ext=this.getQueryTimerExtensionWebGL2(),available=gl2.getQueryParameter(query,gl2.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}else{let ext=this.getQueryTimerExtensionWebGL1(),available=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}}pollFence(fenceContext){return new Promise(resolve=>{this.addItemToPoll(()=>fenceContext.isFencePassed(),()=>resolve())})}pollItems(){let index=linearSearchLastTrue(this.itemsToPoll.map(x=>x.isDoneFn));for(let i=0;i<=index;++i){let{resolveFn}=this.itemsToPoll[i];resolveFn()}this.itemsToPoll=this.itemsToPoll.slice(index+1)}addItemToPoll(isDoneFn,resolveFn){(this.itemsToPoll.push({isDoneFn,resolveFn}),!(this.itemsToPoll.length>1))&&util_exports.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(texture){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,texture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(texture,downloadAndDecode){this.bindTextureToFrameBuffer(texture);let result=downloadAndDecode();return this.unbindTextureToFrameBuffer(),result}setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked,width,height){this.throwIfDisposed();let gl=this.gl;bindColorTextureToFramebuffer(gl,outputMatrixTextureMaybePacked,this.framebuffer),this.debug&&validateFramebuffer(gl),this.outputTexture=outputMatrixTextureMaybePacked,callAndCheck(gl,()=>gl.viewport(0,0,width,height)),callAndCheck(gl,()=>gl.scissor(0,0,width,height))}setOutputMatrixWriteRegionDriver(x,y,width,height){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.scissor(x,y,width,height))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function linearSearchLastTrue(arr){let i=0;for(;i<arr.length&&arr[i]();++i);return i-1}var{getBroadcastDims:getBroadcastDims2}=backend_util_exports;function makeShader(inputsInfo,outputShape,userCode,usesPackedTextures){let prefixSnippets=[];inputsInfo.forEach(x=>{let size=util_exports.sizeFromShape(x.shapeInfo.logicalShape);x.shapeInfo.isUniform?prefixSnippets.push(`uniform float ${x.name}${size>1?`[${size}]`:""};`):(prefixSnippets.push(`uniform sampler2D ${x.name};`),prefixSnippets.push(`uniform int offset${x.name};`))});let inputPrefixSnippet=prefixSnippets.join(`
+    }`;return createVertexShader(gl,vertexShaderSource)}function createVertexBuffer(gl){let vertexArray=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return createStaticVertexBuffer(gl,vertexArray)}function createIndexBuffer(gl){let triangleVertexIndices=new Uint16Array([0,1,2,2,1,3]);return createStaticIndexBuffer(gl,triangleVertexIndices)}function createAndConfigureTexture(gl,width,height,internalFormat,textureFormat,textureType){validateTextureSize(width,height);let texture=createTexture(gl),tex2d=gl.TEXTURE_2D;return callAndCheck(gl,()=>gl.bindTexture(tex2d,texture)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MIN_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MAG_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texImage2D(tex2d,0,internalFormat,width,height,0,textureFormat,textureType,null)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null)),texture}function getInternalFormatForFloat32MatrixTexture(textureConfig){return textureConfig.internalFormatFloat}function createFloat32MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat32MatrixTexture(textureConfig),textureConfig.textureFormatFloat,gl.FLOAT)}function getInternalFormatForFloat16MatrixTexture(textureConfig){return textureConfig.internalFormatHalfFloat}function createFloat16MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16MatrixTexture(textureConfig),textureConfig.textureFormatFloat,textureConfig.textureTypeHalfFloat)}function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig){return textureConfig.downloadTextureFormat}function createUnsignedBytesMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForUnsignedBytesMatrixTexture(textureConfig),gl.RGBA,gl.UNSIGNED_BYTE)}function getInternalFormatForPackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedFloat}function createPackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForPackedMatrixTexture(textureConfig),gl.RGBA,gl.FLOAT)}function getInternalFormatForFloat16PackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedHalfFloat}function createFloat16PackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16PackedMatrixTexture(textureConfig),gl.RGBA,textureConfig.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(gl,program,vertexBuffer){let posOffset=0,uvOffset=3*4,stride=3*4+2*4;return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,vertexBuffer)),bindVertexBufferToProgramAttribute(gl,program,"clipSpacePos",vertexBuffer,3,stride,posOffset)&&bindVertexBufferToProgramAttribute(gl,program,"uv",vertexBuffer,2,stride,uvOffset)}function uploadDenseMatrixToTexture(gl,texture,width,height,data,textureConfig){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture));let dataForUpload,texelDataType,internalFormat;data instanceof Uint8Array?(dataForUpload=new Uint8Array(width*height*4),texelDataType=gl.UNSIGNED_BYTE,internalFormat=gl.RGBA):(dataForUpload=new Float32Array(width*height*4),texelDataType=gl.FLOAT,internalFormat=textureConfig.internalFormatPackedFloat),dataForUpload.set(data),callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,internalFormat,width,height,0,gl.RGBA,texelDataType,dataForUpload)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function uploadPixelDataToTexture(gl,texture,pixels){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture)),pixels.data instanceof Uint8Array?callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,pixels.width,pixels.height,0,gl.RGBA,gl.UNSIGNED_BYTE,pixels.data)):callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,pixels)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function createBufferFromOutputTexture(gl2,rows,columns,textureConfig){let buffer2=gl2.createBuffer();callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2));let bufferSizeBytes=4*4*rows*columns;return callAndCheck(gl2,()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER,bufferSizeBytes,gl2.STREAM_READ)),callAndCheck(gl2,()=>gl2.readPixels(0,0,columns,rows,gl2.RGBA,gl2.FLOAT,0)),callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null)),buffer2}function downloadFloat32MatrixFromBuffer(gl,buffer2,size){let gl2=gl,downloadTarget=new Float32Array(size);return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadByteEncodedFloatMatrixFromOutputTexture(gl,rows,columns,textureConfig){let[w,h]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns),numChannels=4,downloadTarget=new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows*columns,numChannels));return callAndCheck(gl,()=>gl.readPixels(0,0,w,h,textureConfig.downloadTextureFormat,gl.UNSIGNED_BYTE,downloadTarget)),new Float32Array(downloadTarget.buffer)}function downloadPackedMatrixFromBuffer(gl,buffer2,batch,rows,cols,physicalRows,physicalCols,textureConfig){let gl2=gl,downloadTarget=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows,physicalCols));return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadMatrixFromPackedOutputTexture(gl,physicalRows,physicalCols){let packedRGBA=new Float32Array(physicalRows*physicalCols*4);return callAndCheck(gl,()=>gl.readPixels(0,0,physicalCols,physicalRows,gl.RGBA,gl.FLOAT,packedRGBA)),packedRGBA}var GPGPUContext=class{constructor(gl){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let glVersion=env().getNumber("WEBGL_VERSION");gl!=null?(this.gl=gl,setWebGLContext(glVersion,gl)):this.gl=getWebGLContext(glVersion);let COLOR_BUFFER_FLOAT="WEBGL_color_buffer_float",COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(env().getNumber("WEBGL_VERSION")===1){let TEXTURE_FLOAT="OES_texture_float",TEXTURE_HALF_FLOAT="OES_texture_half_float";if(this.textureFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_FLOAT),hasExtension(this.gl,TEXTURE_HALF_FLOAT))this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT),hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=getExtensionOrThrow(this.gl,COLOR_BUFFER_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(COLOR_BUFFER_FLOAT="EXT_color_buffer_float",hasExtension(this.gl,COLOR_BUFFER_FLOAT))this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT);else if(hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}get debug(){return env().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let gl=this.gl;callAndCheck(gl,()=>gl.finish()),callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,null)),callAndCheck(gl,()=>gl.deleteFramebuffer(this.framebuffer)),callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,rows,columns,this.textureConfig)}createFloat16MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,rows,columns,this.textureConfig)}createUnsignedBytesMatrixTexture(rows,columns){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,rows,columns,this.textureConfig)}uploadPixelDataToTexture(texture,pixels){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,texture,pixels)}uploadDenseMatrixToTexture(texture,width,height,data){this.throwIfDisposed(),uploadDenseMatrixToTexture(this.gl,texture,width,height,data,this.textureConfig)}createFloat16PackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}createPackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}deleteMatrixTexture(texture){this.throwIfDisposed(),this.outputTexture===texture&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,()=>this.gl.deleteTexture(texture))}downloadByteEncodedFloatMatrixFromOutputTexture(texture,rows,columns){return this.downloadMatrixDriver(texture,()=>downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,rows,columns,this.textureConfig))}downloadPackedMatrixFromBuffer(buffer2,batch,rows,columns,physicalRows,physicalCols){return downloadPackedMatrixFromBuffer(this.gl,buffer2,batch,rows,columns,physicalRows,physicalCols,this.textureConfig)}downloadFloat32MatrixFromBuffer(buffer2,size){return downloadFloat32MatrixFromBuffer(this.gl,buffer2,size)}createBufferFromTexture(texture,rows,columns){this.bindTextureToFrameBuffer(texture);let result=createBufferFromOutputTexture(this.gl,rows,columns,this.textureConfig);return this.unbindTextureToFrameBuffer(),result}createAndWaitForFence(){let fenceContext=this.createFence(this.gl);return this.pollFence(fenceContext)}createFence(gl){let query,isFencePassed;if(env().getBool("WEBGL_FENCE_API_ENABLED")){let gl2=gl,sync=gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE,0);gl.flush(),isFencePassed=()=>{let status=gl2.clientWaitSync(sync,0,0);return status===gl2.ALREADY_SIGNALED||status===gl2.CONDITION_SATISFIED},query=sync}else env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(query=this.beginQuery(),this.endQuery(),isFencePassed=()=>this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):isFencePassed=()=>!0;return{query,isFencePassed}}downloadMatrixFromPackedTexture(texture,physicalRows,physicalCols){return this.downloadMatrixDriver(texture,()=>downloadMatrixFromPackedOutputTexture(this.gl,physicalRows,physicalCols))}createProgram(fragmentShaderSource){this.throwIfDisposed();let gl=this.gl,fragmentShader=createFragmentShader(gl,fragmentShaderSource),vertexShader=createVertexShader2(gl),program=createProgram(gl);return callAndCheck(gl,()=>gl.attachShader(program,vertexShader)),callAndCheck(gl,()=>gl.attachShader(program,fragmentShader)),linkProgram(gl,program),this.debug&&validateProgram(gl,program),this.vertexAttrsAreBound||(this.setProgram(program),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(gl,this.program,this.vertexBuffer)),program}deleteProgram(program){this.throwIfDisposed(),program===this.program&&(this.program=null),program!=null&&callAndCheck(this.gl,()=>this.gl.deleteProgram(program))}setProgram(program){this.throwIfDisposed(),this.program=program,this.program!=null&&this.debug&&validateProgram(this.gl,this.program),callAndCheck(this.gl,()=>this.gl.useProgram(program))}getUniformLocation(program,uniformName,shouldThrow=!0){return this.throwIfDisposed(),shouldThrow?getProgramUniformLocationOrThrow(this.gl,program,uniformName):getProgramUniformLocation(this.gl,program,uniformName)}getAttributeLocation(program,attribute){return this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.getAttribLocation(program,attribute))}getUniformLocationNoThrow(program,uniformName){return this.throwIfDisposed(),this.gl.getUniformLocation(program,uniformName)}setInputMatrixTexture(inputMatrixTexture,uniformLocation,textureUnit){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,inputMatrixTexture,uniformLocation,textureUnit)}setOutputMatrixTexture(outputMatrixTexture,rows,columns){this.setOutputMatrixTextureDriver(outputMatrixTexture,columns,rows)}setOutputPackedMatrixTexture(outputPackedMatrixTexture,rows,columns){this.throwIfDisposed();let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);this.setOutputMatrixTextureDriver(outputPackedMatrixTexture,width,height)}setOutputMatrixWriteRegion(startRow,numRows,startColumn,numColumns){this.setOutputMatrixWriteRegionDriver(startColumn,startRow,numColumns,numRows)}setOutputPackedMatrixWriteRegion(startRow,numRows,startColumn,numColumns){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let gl=this.gl;this.debug&&this.debugValidate(),callAndCheck(gl,()=>gl.drawElements(gl.TRIANGLES,6,gl.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2(),query2=gl2.createQuery();return gl2.beginQuery(ext2.TIME_ELAPSED_EXT,query2),query2}let ext=this.getQueryTimerExtensionWebGL1(),query=ext.createQueryEXT();return ext.beginQueryEXT(ext.TIME_ELAPSED_EXT,query),query}endQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2();gl2.endQuery(ext2.TIME_ELAPSED_EXT);return}let ext=this.getQueryTimerExtensionWebGL1();ext.endQueryEXT(ext.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(query){return await util_exports.repeatedTry(()=>this.disposed||this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(query,queryTimerVersion){if(queryTimerVersion===0)return null;if(queryTimerVersion===2){let gl2=this.gl;return gl2.getQueryParameter(query,gl2.QUERY_RESULT)/1e6}else{let ext=this.getQueryTimerExtensionWebGL1();return ext.getQueryObjectEXT(query,ext.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(query,queryTimerVersion){if(queryTimerVersion===0)return!0;if(queryTimerVersion===2){let gl2=this.gl,ext=this.getQueryTimerExtensionWebGL2(),available=gl2.getQueryParameter(query,gl2.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}else{let ext=this.getQueryTimerExtensionWebGL1(),available=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}}pollFence(fenceContext){return new Promise(resolve=>{this.addItemToPoll(()=>fenceContext.isFencePassed(),()=>resolve())})}pollItems(){let index=linearSearchLastTrue(this.itemsToPoll.map(x=>x.isDoneFn));for(let i=0;i<=index;++i){let{resolveFn}=this.itemsToPoll[i];resolveFn()}this.itemsToPoll=this.itemsToPoll.slice(index+1)}addItemToPoll(isDoneFn,resolveFn){this.itemsToPoll.push({isDoneFn,resolveFn}),!(this.itemsToPoll.length>1)&&util_exports.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(texture){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,texture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(texture,downloadAndDecode){this.bindTextureToFrameBuffer(texture);let result=downloadAndDecode();return this.unbindTextureToFrameBuffer(),result}setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked,width,height){this.throwIfDisposed();let gl=this.gl;bindColorTextureToFramebuffer(gl,outputMatrixTextureMaybePacked,this.framebuffer),this.debug&&validateFramebuffer(gl),this.outputTexture=outputMatrixTextureMaybePacked,callAndCheck(gl,()=>gl.viewport(0,0,width,height)),callAndCheck(gl,()=>gl.scissor(0,0,width,height))}setOutputMatrixWriteRegionDriver(x,y,width,height){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.scissor(x,y,width,height))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function linearSearchLastTrue(arr){let i=0;for(;i<arr.length&&arr[i]();++i);return i-1}var{getBroadcastDims:getBroadcastDims2}=backend_util_exports;function makeShader(inputsInfo,outputShape,userCode,usesPackedTextures){let prefixSnippets=[];inputsInfo.forEach(x=>{let size=util_exports.sizeFromShape(x.shapeInfo.logicalShape);x.shapeInfo.isUniform?prefixSnippets.push(`uniform float ${x.name}${size>1?`[${size}]`:""};`):(prefixSnippets.push(`uniform sampler2D ${x.name};`),prefixSnippets.push(`uniform int offset${x.name};`))});let inputPrefixSnippet=prefixSnippets.join(`
 `),inputSamplingSnippet=inputsInfo.map(x=>getInputSamplingSnippet(x,outputShape,usesPackedTextures)).join(`
 `),outTexShape=outputShape.texShape,glsl=getGlslDifferences(),floatTextureSampleSnippet=getFloatTextureSampleSnippet(glsl),outputSamplingSnippet,floatTextureSetOutputSnippet,shaderPrefix=getShaderPrefix(glsl);return outputShape.isPacked?(outputSamplingSnippet=getPackedOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRGBASnippet(glsl)):(outputSamplingSnippet=getOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRSnippet(glsl)),usesPackedTextures&&(shaderPrefix+=SHADER_PACKED_PREFIX),[shaderPrefix,floatTextureSampleSnippet,floatTextureSetOutputSnippet,inputPrefixSnippet,outputSamplingSnippet,inputSamplingSnippet,userCode].join(`
 `)}function getSamplerFromInInfo(inInfo){let shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getSamplerScalar(inInfo);case 1:return getSampler1D(inInfo);case 2:return getSampler2D(inInfo);case 3:return getSampler3D(inInfo);case 4:return getSampler4D(inInfo);case 5:return getSampler5D(inInfo);case 6:return getSampler6D(inInfo);default:throw new Error(`${shape.length}-D input sampling is not yet supported`)}}function getPackedSamplerFromInInfo(inInfo){switch(inInfo.shapeInfo.logicalShape.length){case 0:return getPackedSamplerScalar(inInfo);case 1:return getPackedSampler1D(inInfo);case 2:return getPackedSampler2D(inInfo);case 3:return getPackedSampler3D(inInfo);default:return getPackedSamplerND(inInfo)}}function getInputSamplingSnippet(inInfo,outShapeInfo,usesPackedTextures=!1){let res="";usesPackedTextures?res+=getPackedSamplerFromInInfo(inInfo):res+=getSamplerFromInInfo(inInfo);let inShape=inInfo.shapeInfo.logicalShape,outShape=outShapeInfo.logicalShape;return inShape.length<=outShape.length&&(usesPackedTextures?res+=getPackedSamplerAtOutputCoords(inInfo,outShapeInfo):res+=getSamplerAtOutputCoords(inInfo,outShapeInfo)),res}function getPackedOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(outShape,outTexShape);case 2:return getOutputPacked2DCoords(outShape,outTexShape);case 3:return getOutputPacked3DCoords(outShape,outTexShape);default:return getOutputPackedNDCoords(outShape,outTexShape)}}function getOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(outShape,outTexShape);case 2:return getOutput2DCoords(outShape,outTexShape);case 3:return getOutput3DCoords(outShape,outTexShape);case 4:return getOutput4DCoords(outShape,outTexShape);case 5:return getOutput5DCoords(outShape,outTexShape);case 6:return getOutput6DCoords(outShape,outTexShape);default:throw new Error(`${outShape.length}-D output sampling is not yet supported`)}}function getFloatTextureSampleSnippet(glsl){return`
@@ -827,7 +827,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${coordsSnippet}
       return get${texFuncSnippet}(${unpackedCoordsSnippet});
     }
-  `}function getCoordsDataType(rank){if(rank<=1)return"int";if(rank===2)return"ivec2";if(rank===3)return"ivec3";if(rank===4)return"ivec4";if(rank===5)return"ivec5";if(rank===6)return"ivec6";throw Error(`GPU for rank ${rank} is not yet supported`)}function squeezeInputInfo(inInfo,squeezedShape){let newInputInfo=JSON.parse(JSON.stringify(inInfo));return newInputInfo.shapeInfo.logicalShape=squeezedShape,newInputInfo}function getSqueezedParams(params,keptDims){return keptDims.map(d=>params[d]).join(", ")}function compileProgram(gpgpu,program,inputs,output){let userCode=program.userCode,inputInfos=inputs.map((input2,i)=>{let shapeInfo={logicalShape:input2.shape,texShape:input2.isUniform?null:input2.texData.texShape,isUniform:input2.isUniform,isPacked:input2.isUniform?!1:input2.texData.isPacked,flatOffset:null};return input2.texData!=null&&input2.texData.slice!=null&&input2.texData.slice.flatOffset>0&&(shapeInfo.flatOffset=input2.texData.slice.flatOffset),{name:program.variableNames[i],shapeInfo}}),inShapeInfos=inputInfos.map(x=>x.shapeInfo),outShapeInfo={logicalShape:output.shape,texShape:output.texData.texShape,isUniform:!1,isPacked:output.texData.isPacked,flatOffset:null},source=makeShader(inputInfos,outShapeInfo,userCode,program.packedInputs),webGLProgram=gpgpu.createProgram(source),infLoc=null,nanLoc=gpgpu.getUniformLocation(webGLProgram,"NAN",!1);env().getNumber("WEBGL_VERSION")===1&&(infLoc=gpgpu.getUniformLocation(webGLProgram,"INFINITY",!1));let uniformLocations={};for(let i=0;i<program.variableNames.length;i++){let varName=program.variableNames[i],shouldThrow=!1;uniformLocations[varName]=gpgpu.getUniformLocation(webGLProgram,varName,shouldThrow),uniformLocations[`offset${varName}`]=gpgpu.getUniformLocation(webGLProgram,`offset${varName}`,shouldThrow)}return{program,source,webGLProgram,uniformLocations,inShapeInfos,outShapeInfo,infLoc,nanLoc}}function validateBinaryAndProgram(shapeInfos,inputs){if(shapeInfos.length!==inputs.length)throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);shapeInfos.forEach((s,i)=>{let shapeA=s.logicalShape,input2=inputs[i],shapeB=input2.shape;if(!util_exports.arraysEqual(shapeA,shapeB))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);if(!(s.isUniform&&input2.isUniform)){let texShapeA=s.texShape,texShapeB=input2.isUniform?null:input2.texData.texShape;if(!util_exports.arraysEqual(texShapeA,texShapeB))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`)}})}function runProgram(gpgpu,binary,inputs,output,customSetup){validateBinaryAndProgram(binary.inShapeInfos,inputs),validateBinaryAndProgram([binary.outShapeInfo],[output]);let outTex=output.texData.texture,outTexShape=output.texData.texShape;output.texData.isPacked?gpgpu.setOutputPackedMatrixTexture(outTex,outTexShape[0],outTexShape[1]):gpgpu.setOutputMatrixTexture(outTex,outTexShape[0],outTexShape[1]),gpgpu.setProgram(binary.webGLProgram),env().getNumber("WEBGL_VERSION")===1&&binary.infLoc!==null&&gpgpu.gl.uniform1f(binary.infLoc,Infinity),binary.nanLoc!==null&&gpgpu.gl.uniform1f(binary.nanLoc,NaN),inputs.forEach((input2,i)=>{let varName=binary.program.variableNames[i],varLoc=binary.uniformLocations[varName],varOffsetLoc=binary.uniformLocations[`offset${varName}`];if(varLoc!=null){if(input2.isUniform){if(util_exports.sizeFromShape(input2.shape)<2)gpgpu.gl.uniform1f(varLoc,input2.uniformValues[0]);else{let vals=input2.uniformValues;vals instanceof Float32Array||(vals=new Float32Array(vals)),gpgpu.gl.uniform1fv(varLoc,vals)}return}input2.texData.slice!=null&&varOffsetLoc!=null&&gpgpu.gl.uniform1i(varOffsetLoc,input2.texData.slice.flatOffset),gpgpu.setInputMatrixTexture(input2.texData.texture,varLoc,i)}}),customSetup!=null&&customSetup(gpgpu,binary.webGLProgram),gpgpu.executeProgram()}function makeShaderKey(program,inputs,output){let keyInputs="";inputs.concat(output).forEach(x=>{let hasOffset=x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0,texShape=x.isUniform?"uniform":x.texData.texShape;keyInputs+=`${x.shape}_${texShape}_${hasOffset}`});let keyUserCode=program.userCode,key=program.constructor.name;return key+="_"+keyInputs+"_"+keyUserCode,key}var{addImpl:addImplCPU,bincountImpl:bincountImplCPU,bincountReduceImpl:bincountReduceImplCPU,ceilImpl:ceilImplCPU,concatImpl:concatImplCPU,expImpl:expImplCPU,expm1Impl:expm1ImplCPU,floorImpl:floorImplCPU,gatherV2Impl:gatherV2ImplCPU,greaterImpl:greaterImplCPU,lessImpl:lessImplCPU,linSpaceImpl:linSpaceImplCPU,logImpl:logImplCPU,maxImpl:maxImplCPU,maximumImpl:maximumImplCPU,minimumImpl:minimumImplCPU,multiplyImpl:multiplyImplCPU,negImpl:negImplCPU,prodImpl:prodImplCPU,rangeImpl:rangeImplCPU,rsqrtImpl:rsqrtImplCPU,simpleAbsImpl:simpleAbsImplCPU,sliceImpl:sliceImplCPU,stridedSliceImpl:stridedSliceImplCPU,subImpl:subImplCPU,tileImpl:tileImplCPU,topKImpl:topKImplCPU,transposeImpl:transposeImplCPU,uniqueImpl:uniqueImplCPU}=shared_exports;function getVecChannels(name,rank){return["x","y","z","w","u","v"].slice(0,rank).map(d=>`${name}.${d}`)}function getChannels(name,rank){return rank===1?[name]:getVecChannels(name,rank)}function getSourceCoords(rank,dims){if(rank===1)return"rc";let coords2="";for(let i=0;i<rank;i++)coords2+=dims[i],i<rank-1&&(coords2+=",");return coords2}var PackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=outputShape;let rank=outputShape.length;if(rank===0)this.userCode=`
+  `}function getCoordsDataType(rank){if(rank<=1)return"int";if(rank===2)return"ivec2";if(rank===3)return"ivec3";if(rank===4)return"ivec4";if(rank===5)return"ivec5";if(rank===6)return"ivec6";throw Error(`GPU for rank ${rank} is not yet supported`)}function squeezeInputInfo(inInfo,squeezedShape){let newInputInfo=JSON.parse(JSON.stringify(inInfo));return newInputInfo.shapeInfo.logicalShape=squeezedShape,newInputInfo}function getSqueezedParams(params,keptDims){return keptDims.map(d=>params[d]).join(", ")}function compileProgram(gpgpu,program,inputs,output){let userCode=program.userCode,inputInfos=inputs.map((input2,i)=>{let shapeInfo={logicalShape:input2.shape,texShape:input2.isUniform?null:input2.texData.texShape,isUniform:input2.isUniform,isPacked:input2.isUniform?!1:input2.texData.isPacked,flatOffset:null};return input2.texData!=null&&input2.texData.slice!=null&&input2.texData.slice.flatOffset>0&&(shapeInfo.flatOffset=input2.texData.slice.flatOffset),{name:program.variableNames[i],shapeInfo}}),inShapeInfos=inputInfos.map(x=>x.shapeInfo),outShapeInfo={logicalShape:output.shape,texShape:output.texData.texShape,isUniform:!1,isPacked:output.texData.isPacked,flatOffset:null},source=makeShader(inputInfos,outShapeInfo,userCode,program.packedInputs),webGLProgram=gpgpu.createProgram(source),infLoc=null,nanLoc=gpgpu.getUniformLocation(webGLProgram,"NAN",!1);env().getNumber("WEBGL_VERSION")===1&&(infLoc=gpgpu.getUniformLocation(webGLProgram,"INFINITY",!1));let uniformLocations={};for(let i=0;i<program.variableNames.length;i++){let varName=program.variableNames[i],shouldThrow=!1;uniformLocations[varName]=gpgpu.getUniformLocation(webGLProgram,varName,shouldThrow),uniformLocations[`offset${varName}`]=gpgpu.getUniformLocation(webGLProgram,`offset${varName}`,shouldThrow)}return{program,source,webGLProgram,uniformLocations,inShapeInfos,outShapeInfo,infLoc,nanLoc}}function validateBinaryAndProgram(shapeInfos,inputs){if(shapeInfos.length!==inputs.length)throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);shapeInfos.forEach((s,i)=>{let shapeA=s.logicalShape,input2=inputs[i],shapeB=input2.shape;if(!util_exports.arraysEqual(shapeA,shapeB))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);if(s.isUniform&&input2.isUniform)return;let texShapeA=s.texShape,texShapeB=input2.isUniform?null:input2.texData.texShape;if(!util_exports.arraysEqual(texShapeA,texShapeB))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`)})}function runProgram(gpgpu,binary,inputs,output,customSetup){validateBinaryAndProgram(binary.inShapeInfos,inputs),validateBinaryAndProgram([binary.outShapeInfo],[output]);let outTex=output.texData.texture,outTexShape=output.texData.texShape;output.texData.isPacked?gpgpu.setOutputPackedMatrixTexture(outTex,outTexShape[0],outTexShape[1]):gpgpu.setOutputMatrixTexture(outTex,outTexShape[0],outTexShape[1]),gpgpu.setProgram(binary.webGLProgram),env().getNumber("WEBGL_VERSION")===1&&binary.infLoc!==null&&gpgpu.gl.uniform1f(binary.infLoc,Infinity),binary.nanLoc!==null&&gpgpu.gl.uniform1f(binary.nanLoc,NaN),inputs.forEach((input2,i)=>{let varName=binary.program.variableNames[i],varLoc=binary.uniformLocations[varName],varOffsetLoc=binary.uniformLocations[`offset${varName}`];if(varLoc!=null){if(input2.isUniform){if(util_exports.sizeFromShape(input2.shape)<2)gpgpu.gl.uniform1f(varLoc,input2.uniformValues[0]);else{let vals=input2.uniformValues;vals instanceof Float32Array||(vals=new Float32Array(vals)),gpgpu.gl.uniform1fv(varLoc,vals)}return}input2.texData.slice!=null&&varOffsetLoc!=null&&gpgpu.gl.uniform1i(varOffsetLoc,input2.texData.slice.flatOffset),gpgpu.setInputMatrixTexture(input2.texData.texture,varLoc,i)}}),customSetup!=null&&customSetup(gpgpu,binary.webGLProgram),gpgpu.executeProgram()}function makeShaderKey(program,inputs,output){let keyInputs="";inputs.concat(output).forEach(x=>{let hasOffset=x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0,texShape=x.isUniform?"uniform":x.texData.texShape;keyInputs+=`${x.shape}_${texShape}_${hasOffset}`});let keyUserCode=program.userCode,key=program.constructor.name;return key+="_"+keyInputs+"_"+keyUserCode,key}var{addImpl:addImplCPU,bincountImpl:bincountImplCPU,bincountReduceImpl:bincountReduceImplCPU,ceilImpl:ceilImplCPU,concatImpl:concatImplCPU,expImpl:expImplCPU,expm1Impl:expm1ImplCPU,floorImpl:floorImplCPU,gatherV2Impl:gatherV2ImplCPU,greaterImpl:greaterImplCPU,lessImpl:lessImplCPU,linSpaceImpl:linSpaceImplCPU,logImpl:logImplCPU,maxImpl:maxImplCPU,maximumImpl:maximumImplCPU,minimumImpl:minimumImplCPU,multiplyImpl:multiplyImplCPU,negImpl:negImplCPU,prodImpl:prodImplCPU,rangeImpl:rangeImplCPU,rsqrtImpl:rsqrtImplCPU,simpleAbsImpl:simpleAbsImplCPU,sliceImpl:sliceImplCPU,stridedSliceImpl:stridedSliceImplCPU,subImpl:subImplCPU,tileImpl:tileImplCPU,topKImpl:topKImplCPU,transposeImpl:transposeImplCPU,uniqueImpl:uniqueImplCPU}=shared_exports;function getVecChannels(name,rank){return["x","y","z","w","u","v"].slice(0,rank).map(d=>`${name}.${d}`)}function getChannels(name,rank){return rank===1?[name]:getVecChannels(name,rank)}function getSourceCoords(rank,dims){if(rank===1)return"rc";let coords2="";for(let i=0;i<rank;i++)coords2+=dims[i],i<rank-1&&(coords2+=",");return coords2}var PackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=outputShape;let rank=outputShape.length;if(rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
@@ -856,7 +856,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             0, 0`:`getA(${sourceCoords[0]}),
           cEdge ? 0. : getA(${sourceCoords[1]}),
           rEdge ? 0. : getA(${sourceCoords[2]}),
-          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`}var ReshapePackedProgram=class{constructor(outputShape,inputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let mainLoop="";for(let i=0;i<4;i++){let thisRC="thisRC = rc;";i%2===1&&(thisRC+="thisRC.z += 1;"),i>1&&(thisRC+="thisRC.y += 1;"),mainLoop+=`
+          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`}var ReshapePackedProgram=class{constructor(outputShape,inputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let mainLoop="";for(let i=0;i<4;i++){let thisRC="thisRC = rc;";i%2==1&&(thisRC+="thisRC.z += 1;"),i>1&&(thisRC+="thisRC.y += 1;"),mainLoop+=`
         ${thisRC}
         ${i>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -889,7 +889,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape)}
       return ivec3(r, c, d);
     }
-  `}var TextureManager=class{constructor(gpgpu){this.gpgpu=gpgpu,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(shapeRC,usage,isPacked){let physicalTexType=getPhysicalFromLogicalTextureType(usage,isPacked),shapeKey=getKeyFromTextureShape(shapeRC,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]),shapeKey in this.usedTextures||(this.usedTextures[shapeKey]=[]);let texBytes=computeBytes(shapeRC,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked);if(this.freeTextures[shapeKey].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=texBytes,this.log();let newTexture2=this.freeTextures[shapeKey].shift();return this.usedTextures[shapeKey].push(newTexture2),newTexture2}let newTexture;return physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT32?newTexture=this.gpgpu.createPackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT16?newTexture=this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT32?newTexture=this.gpgpu.createFloat32MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT16?newTexture=this.gpgpu.createFloat16MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(newTexture=this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0],shapeRC[1])),this.usedTextures[shapeKey].push(newTexture),this.numUsedTextures++,this._numBytesAllocated+=texBytes,this.log(),newTexture}releaseTexture(texture,shape,logicalTexType,isPacked){if(this.freeTextures!=null){let physicalTexType=getPhysicalFromLogicalTextureType(logicalTexType,isPacked),shapeKey=getKeyFromTextureShape(shape,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]);let texBytes=computeBytes(shape,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked),deleteTexThreshold=env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");deleteTexThreshold!==-1&&this._numBytesAllocated>deleteTexThreshold?(this.gpgpu.deleteMatrixTexture(texture),this._numBytesAllocated-=texBytes):(this.freeTextures[shapeKey].push(texture),this.numFreeTextures++,this._numBytesFree+=texBytes),this.numUsedTextures--;let texList=this.usedTextures[shapeKey],texIndex=texList.indexOf(texture);if(texIndex<0)throw new Error("Cannot release a texture that was never provided by this texture manager");texList.splice(texIndex,1),this.log()}}log(){if(this.logEnabled){let total=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${total})`);let freeRatio=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*freeRatio)}%)`)}}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let texShape in this.freeTextures)this.freeTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});for(let texShape in this.usedTextures)this.usedTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function numBytesForInternalFormat(gl,internalFormat){let glany=gl;if(internalFormat===glany.R32F)return 4;if(internalFormat===glany.R16F)return 2;if(internalFormat===glany.RGBA32F)return 16;if(internalFormat===gl.RGBA)return 16;if(internalFormat===glany.RGBA16F)return 8;throw new Error(`Unknown internal format ${internalFormat}`)}function computeBytes(shape,physicalTexType,gl,textureConfig,isPacked){let internalFormat=internalFormatForPhysicalTexType(physicalTexType,textureConfig),numElements;if(isPacked){let[packedWidth,packedHeight]=getPackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=packedWidth*packedHeight}else{let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=width*height}let bytesPerElement2=numBytesForInternalFormat(gl,internalFormat);return numElements*bytesPerElement2}function internalFormatForPhysicalTexType(physicalTexType,textureConfig){switch(physicalTexType){case PhysicalTextureType.PACKED_2X2_FLOAT32:return getInternalFormatForPackedMatrixTexture(textureConfig);case PhysicalTextureType.PACKED_2X2_FLOAT16:return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT32:return getInternalFormatForFloat32MatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT16:return getInternalFormatForFloat16MatrixTexture(textureConfig);case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);default:throw new Error(`Unknown physical texture type ${physicalTexType}`)}}function getPhysicalTextureForRendering(isPacked){return env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?isPacked?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32:isPacked?PhysicalTextureType.PACKED_2X2_FLOAT16:PhysicalTextureType.UNPACKED_FLOAT16}function getPhysicalFromLogicalTextureType(logicalTexType,isPacked){if(logicalTexType===TextureUsage.UPLOAD)return PhysicalTextureType.PACKED_2X2_FLOAT32;if(logicalTexType===TextureUsage.RENDER||logicalTexType==null)return getPhysicalTextureForRendering(isPacked);if(logicalTexType===TextureUsage.DOWNLOAD||logicalTexType===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${logicalTexType}`)}function getKeyFromTextureShape(shapeRowsCol,physicalTexType,isPacked){return`${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`}var UnaryOpProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
+  `}var TextureManager=class{constructor(gpgpu){this.gpgpu=gpgpu,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(shapeRC,usage,isPacked){let physicalTexType=getPhysicalFromLogicalTextureType(usage,isPacked),shapeKey=getKeyFromTextureShape(shapeRC,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]),shapeKey in this.usedTextures||(this.usedTextures[shapeKey]=[]);let texBytes=computeBytes(shapeRC,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked);if(this.freeTextures[shapeKey].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=texBytes,this.log();let newTexture2=this.freeTextures[shapeKey].shift();return this.usedTextures[shapeKey].push(newTexture2),newTexture2}let newTexture;return physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT32?newTexture=this.gpgpu.createPackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT16?newTexture=this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT32?newTexture=this.gpgpu.createFloat32MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT16?newTexture=this.gpgpu.createFloat16MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(newTexture=this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0],shapeRC[1])),this.usedTextures[shapeKey].push(newTexture),this.numUsedTextures++,this._numBytesAllocated+=texBytes,this.log(),newTexture}releaseTexture(texture,shape,logicalTexType,isPacked){if(this.freeTextures==null)return;let physicalTexType=getPhysicalFromLogicalTextureType(logicalTexType,isPacked),shapeKey=getKeyFromTextureShape(shape,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]);let texBytes=computeBytes(shape,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked),deleteTexThreshold=env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");deleteTexThreshold!==-1&&this._numBytesAllocated>deleteTexThreshold?(this.gpgpu.deleteMatrixTexture(texture),this._numBytesAllocated-=texBytes):(this.freeTextures[shapeKey].push(texture),this.numFreeTextures++,this._numBytesFree+=texBytes),this.numUsedTextures--;let texList=this.usedTextures[shapeKey],texIndex=texList.indexOf(texture);if(texIndex<0)throw new Error("Cannot release a texture that was never provided by this texture manager");texList.splice(texIndex,1),this.log()}log(){if(!this.logEnabled)return;let total=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${total})`);let freeRatio=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*freeRatio)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let texShape in this.freeTextures)this.freeTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});for(let texShape in this.usedTextures)this.usedTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function numBytesForInternalFormat(gl,internalFormat){let glany=gl;if(internalFormat===glany.R32F)return 4;if(internalFormat===glany.R16F)return 2;if(internalFormat===glany.RGBA32F||internalFormat===gl.RGBA)return 16;if(internalFormat===glany.RGBA16F)return 8;throw new Error(`Unknown internal format ${internalFormat}`)}function computeBytes(shape,physicalTexType,gl,textureConfig,isPacked){let internalFormat=internalFormatForPhysicalTexType(physicalTexType,textureConfig),numElements;if(isPacked){let[packedWidth,packedHeight]=getPackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=packedWidth*packedHeight}else{let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=width*height}let bytesPerElement2=numBytesForInternalFormat(gl,internalFormat);return numElements*bytesPerElement2}function internalFormatForPhysicalTexType(physicalTexType,textureConfig){switch(physicalTexType){case PhysicalTextureType.PACKED_2X2_FLOAT32:return getInternalFormatForPackedMatrixTexture(textureConfig);case PhysicalTextureType.PACKED_2X2_FLOAT16:return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT32:return getInternalFormatForFloat32MatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT16:return getInternalFormatForFloat16MatrixTexture(textureConfig);case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);default:throw new Error(`Unknown physical texture type ${physicalTexType}`)}}function getPhysicalTextureForRendering(isPacked){return env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?isPacked?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32:isPacked?PhysicalTextureType.PACKED_2X2_FLOAT16:PhysicalTextureType.UNPACKED_FLOAT16}function getPhysicalFromLogicalTextureType(logicalTexType,isPacked){if(logicalTexType===TextureUsage.UPLOAD)return PhysicalTextureType.PACKED_2X2_FLOAT32;if(logicalTexType===TextureUsage.RENDER||logicalTexType==null)return getPhysicalTextureForRendering(isPacked);if(logicalTexType===TextureUsage.DOWNLOAD||logicalTexType===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${logicalTexType}`)}function getKeyFromTextureShape(shapeRowsCol,physicalTexType,isPacked){return`${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`}var UnaryOpProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
       float unaryOperation(float x) {
         ${opSnippet}
       }
@@ -951,7 +951,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(getChannel(packedInput, ${coords2}));
       }
-    `}},whereImpl3=kernel_impls_exports.whereImpl,EPSILON_FLOAT322=1e-7,EPSILON_FLOAT162=1e-4,binaryCaches={};function getBinaryCache(webGLVersion){return webGLVersion in binaryCaches||(binaryCaches[webGLVersion]={}),binaryCaches[webGLVersion]}var CPU_HANDOFF_SIZE_THRESHOLD=128,BEFORE_PAGING_CONSTANT=600;function numMBBeforeWarning(){return env().global.screen==null?1024:env().global.screen.height*env().global.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024/1024}var MathBackendWebGL=class extends KernelBackend{constructor(gpgpu){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(gpgpu==null){let gl=getWebGLContext(env().getNumber("WEBGL_VERSION"));this.binaryCache=getBinaryCache(env().getNumber("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(gl),this.canvas=gl.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=gpgpu,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=gpgpu.gl.canvas;this.textureManager=new TextureManager(this.gpgpu),this.numMBBeforeWarning=numMBBeforeWarning(),this.texData=new DataStorage(this,engine())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(values,shape,dtype){if((env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||env().getBool("DEBUG"))&&this.checkNumericalProblems(values),dtype==="complex64"&&values!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let dataId={};return this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0}),dataId}incRef(dataId){let texData=this.texData.get(dataId);texData.refCount++}decRef(dataId){if(this.texData.has(dataId)){let texData=this.texData.get(dataId);texData.refCount--}}move(dataId,values,shape,dtype){if(env().getBool("DEBUG")&&this.checkNumericalProblems(values),dtype==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.texData.has(dataId)){let textureData=this.texData.get(dataId);textureData.refCount--,textureData.refCount<1&&this.disposeData(dataId)}}readSync(dataId){let texData=this.texData.get(dataId),{values,dtype,complexTensorInfos,slice:slice5,shape,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.readSync(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(dtype==="string")return values;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let result;if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);result=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else result=this.getValuesFromTexture(dataId);return shouldTimeProgram&&(this.downloadWaitMs+=util_exports.now()-start),this.convertAndCacheOnCPU(dataId,result)}async read(dataId){if(this.pendingRead.has(dataId)){let subscribers2=this.pendingRead.get(dataId);return new Promise(resolve=>subscribers2.push(resolve))}let texData=this.texData.get(dataId),{values,shape,slice:slice5,dtype,complexTensorInfos,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.read(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let buffer2=null,tmpDownloadTarget;if(dtype!=="complex64"&&env().get("WEBGL_BUFFER_SUPPORTED")){tmpDownloadTarget=this.decode(dataId);let tmpData=this.texData.get(tmpDownloadTarget.dataId);buffer2=this.gpgpu.createBufferFromTexture(tmpData.texture,...getDenseTexShape(shape))}this.pendingRead.set(dataId,[]),dtype!=="complex64"&&await this.gpgpu.createAndWaitForFence();let vals;if(dtype==="complex64"){let ps=await Promise.all([this.read(complexTensorInfos.real.dataId),this.read(complexTensorInfos.imag.dataId)]),realValues=ps[0],imagValues=ps[1];vals=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else if(buffer2==null)vals=this.getValuesFromTexture(dataId);else{let size=util_exports.sizeFromShape(shape);vals=this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2,size)}tmpDownloadTarget!=null&&this.disposeIntermediateTensorInfo(tmpDownloadTarget);let dTypeVals=this.convertAndCacheOnCPU(dataId,vals),subscribers=this.pendingRead.get(dataId);return this.pendingRead.delete(dataId),subscribers.forEach(resolve=>resolve(dTypeVals)),this.pendingDisposal.has(dataId)&&(this.pendingDisposal.delete(dataId),this.disposeData(dataId),this.pendingDeletes--),dTypeVals}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}checkNumericalProblems(values){if(values!=null)for(let i=0;i<values.length;i++){let num=values[i];if(!canBeRepresented(num))throw env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${num} cannot be represented on this device.`)}}getValuesFromTexture(dataId){let{shape,dtype,isPacked}=this.texData.get(dataId),size=util_exports.sizeFromShape(shape);if(env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let tmpTarget=this.decode(dataId),tmpData2=this.texData.get(tmpTarget.dataId),vals2=this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture,...getDenseTexShape(shape)).subarray(0,size);return this.disposeIntermediateTensorInfo(tmpTarget),vals2}let shouldUsePackedProgram=env().getBool("WEBGL_PACK")&&isPacked===!0,outputShape=shouldUsePackedProgram?getShapeAs3D(shape):shape,program=shouldUsePackedProgram?new EncodeFloatPackedProgram(outputShape):new EncodeFloatProgram(outputShape),output=this.runWebGLProgram(program,[{shape:outputShape,dtype,dataId}],"float32"),tmpData=this.texData.get(output.dataId),vals=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture,tmpData.texShape[0],tmpData.texShape[1]).subarray(0,size);return this.disposeIntermediateTensorInfo(output),vals}async time(f){let oldActiveTimers=this.activeTimers,newActiveTimers=[],outerMostTime=!1;this.programTimersStack==null?(this.programTimersStack=newActiveTimers,outerMostTime=!0):this.activeTimers.push(newActiveTimers),this.activeTimers=newActiveTimers,f();let flattenedActiveTimerQueries=util_exports.flatten(this.activeTimers.map(d=>d.query)).filter(d=>d!=null),flattenedActiveTimerNames=util_exports.flatten(this.activeTimers.map(d=>d.name)).filter(d=>d!=null);this.activeTimers=oldActiveTimers,outerMostTime&&(this.programTimersStack=null);let res={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let kernelMs=await Promise.all(flattenedActiveTimerQueries);res.kernelMs=util_exports.sum(kernelMs),res.getExtraProfileInfo=()=>kernelMs.map((d,i)=>({name:flattenedActiveTimerNames[i],ms:d})).map(d=>`${d.name}: ${d.ms}`).join(", ")}else res.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,res}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:util_exports.now(),endMs:null}}endTimer(query){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),query):(query.endMs=util_exports.now(),query)}async getQueryTime(query){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(query);let timerQuery=query;return timerQuery.endMs-timerQuery.startMs}disposeData(dataId){if(!this.pendingDisposal.has(dataId)){if(this.pendingRead.has(dataId)){this.pendingDisposal.add(dataId),this.pendingDeletes++;return}if(this.texData.has(dataId)){if(this.texData.get(dataId).complexParentRefCount>0){this.texData.get(dataId).refCount--;return}this.releaseGPUData(dataId);let{complexTensorInfos}=this.texData.get(dataId);complexTensorInfos!=null&&(this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.real),this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.imag)),this.texData.delete(dataId)}}}releaseGPUData(dataId){let{texture,dtype,texShape,usage,isPacked,slice:slice5}=this.texData.get(dataId),key=slice5&&slice5.origDataId||dataId,refCount=this.dataRefCount.get(key);refCount>1?this.dataRefCount.set(key,refCount-1):(this.dataRefCount.delete(key),texture!=null&&(this.numBytesInGPU-=this.computeBytes(texShape,dtype),this.textureManager.releaseTexture(texture,texShape,usage,isPacked)));let texData=this.texData.get(dataId);texData.texture=null,texData.texShape=null,texData.isPacked=!1,texData.slice=null}getTexture(dataId){return this.uploadToGPU(dataId),this.texData.get(dataId).texture}getDataInfo(dataId){return this.texData.get(dataId)}getCPUBackend(){return env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=engine().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(inputs,sizeThreshold=CPU_HANDOFF_SIZE_THRESHOLD){let cpuBackend=this.getCPUBackend();return!env().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&cpuBackend==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),cpuBackend!=null&&inputs.every(input2=>this.texData.get(input2.dataId).texture==null&&util_exports.sizeFromShape(input2.shape)<sizeThreshold)}getGPGPUContext(){return this.gpgpu}where(condition){backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let condVals=condition.dataSync();return whereImpl3(condition.shape,condVals)}packedUnaryOp(x,op2,dtype){let program=new UnaryOpPackedProgram(x.shape,op2);return this.compileAndRun(program,[x],dtype)}abs(x){if(this.shouldExecuteOnCPU([x])&&x.dtype!=="complex64"){let outValues=simpleAbsImplCPU(this.texData.get(x.dataId).values);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ABS,x.dtype);let program=new UnaryOpProgram(x.shape,ABS);return this.compileAndRun(program,[x])}makeTensorInfo(shape,dtype,values){let dataId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));dataId=this.write(encodedValues,shape,dtype)}else dataId=this.write(values,shape,dtype);return this.texData.get(dataId).usage=null,{dataId,shape,dtype}}makeOutput(shape,dtype,values){let{dataId}=this.makeTensorInfo(shape,dtype,values);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}unpackTensor(input2){let program=new UnpackProgram(input2.shape);return this.runWebGLProgram(program,[input2],input2.dtype)}packTensor(input2){let program=new PackProgram(input2.shape),preventEagerUnpackingOutput=!0;return this.runWebGLProgram(program,[input2],input2.dtype,null,preventEagerUnpackingOutput)}packedReshape(input2,afterShape){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=this.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}decode(dataId){let texData=this.texData.get(dataId),{isPacked,shape,dtype}=texData,shapeAs3D=getShapeAs3D(shape),program;isPacked?program=new DecodeMatrixPackedProgram(shapeAs3D):program=new DecodeMatrixProgram(shapeAs3D);let preventEagerUnpackingOfOutput=!0,out=this.runWebGLProgram(program,[{shape:shapeAs3D,dtype,dataId}],dtype,null,preventEagerUnpackingOfOutput);return{dtype,shape,dataId:out.dataId}}runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){let output=this.makeTensorInfo(program.outputShape,outputDtype),outData=this.texData.get(output.dataId);if(program.packedOutput&&(outData.isPacked=!0),program.outPackingScheme===PackingScheme.DENSE){let texelShape=getDenseTexShape(program.outputShape);outData.texShape=texelShape.map(d=>d*2)}if(program.outTexUsage!=null&&(outData.usage=program.outTexUsage),util_exports.sizeFromShape(output.shape)===0)return outData.values=util_exports.getTypedArrayFromDType(output.dtype,0),output;let dataToDispose=[],inputsData=inputs.map(input2=>{if(input2.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let texData=this.texData.get(input2.dataId);if(texData.texture==null){if(!program.packedInputs&&util_exports.sizeFromShape(input2.shape)<=env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:input2.shape,texData:null,isUniform:!0,uniformValues:texData.values};program.packedInputs&&(texData.isPacked=!0,texData.shape=input2.shape)}else if(!!texData.isPacked!==!!program.packedInputs)input2=texData.isPacked?this.unpackTensor(input2):this.packTensor(input2),dataToDispose.push(input2),texData=this.texData.get(input2.dataId);else if(texData.isPacked&&!isReshapeFree(texData.shape,input2.shape)){let savedInput=input2,targetShape=input2.shape;input2.shape=texData.shape,input2=this.packedReshape(input2,targetShape),dataToDispose.push(input2),texData=this.texData.get(input2.dataId),savedInput.shape=targetShape}return this.uploadToGPU(input2.dataId),{shape:input2.shape,texData,isUniform:!1}});this.uploadToGPU(output.dataId);let outputData={shape:output.shape,texData:outData,isUniform:!1},key=makeShaderKey(program,inputsData,outputData),binary=this.getAndSaveBinary(key,()=>compileProgram(this.gpgpu,program,inputsData,outputData)),shouldTimeProgram=this.activeTimers!=null,query;if(shouldTimeProgram&&(query=this.startTimer()),runProgram(this.gpgpu,binary,inputsData,outputData,customSetup),dataToDispose.forEach(info=>this.disposeIntermediateTensorInfo(info)),shouldTimeProgram&&(query=this.endTimer(query),this.activeTimers.push({name:program.constructor.name,query:this.getQueryTime(query)})),!env().getBool("WEBGL_LAZILY_UNPACK")&&outData.isPacked&&preventEagerUnpackingOfOutput===!1){let unpacked=this.unpackTensor(output);return this.disposeIntermediateTensorInfo(output),unpacked}return output}compileAndRun(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){outputDtype=outputDtype||inputs[0].dtype;let outInfo=this.runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput);return engine().makeTensorFromDataId(outInfo.dataId,outInfo.shape,outInfo.dtype)}getAndSaveBinary(key,getBinary){return key in this.binaryCache||(this.binaryCache[key]=getBinary()),this.binaryCache[key]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(env().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(key=>{this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram),delete this.binaryCache[key]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=tidy(()=>{if(!env().get("WEBGL_RENDER_FLOAT32_ENABLED")){let debugFlag=env().getBool("DEBUG");env().set("DEBUG",!1);let underflowCheckValue=this.abs(scalar(1e-8)).dataSync()[0];if(env().set("DEBUG",debugFlag),underflowCheckValue>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT322:EPSILON_FLOAT162}uploadToGPU(dataId){let texData=this.texData.get(dataId),{shape,dtype,values,texture,usage,isPacked}=texData;if(texture==null){let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let texShape=texData.texShape;if(texShape==null&&(texShape=getTextureShapeFromLogicalShape(shape,isPacked),texData.texShape=texShape),values!=null){let shapeAs3D=getShapeAs3D(shape),program,width=texShape[1],height=texShape[0],isByteArray=values instanceof Uint8Array;isPacked?([width,height]=getPackedMatrixTextureShapeWidthHeight(texShape[0],texShape[1]),program=new EncodeMatrixPackedProgram(shapeAs3D,[height,width],isByteArray)):program=new EncodeMatrixProgram(shapeAs3D,[height,width],isByteArray);let tempDenseInputHandle=this.makeTensorInfo([height,width],dtype);isByteArray?this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.PIXELS:this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId),width,height,values);let preventEagerUnpacking=!0,encodedOutputTarget=this.runWebGLProgram(program,[tempDenseInputHandle],dtype,null,preventEagerUnpacking),outputTexData=this.texData.get(encodedOutputTarget.dataId);texData.texture=outputTexData.texture,texData.texShape=outputTexData.texShape,texData.isPacked=outputTexData.isPacked,texData.usage=outputTexData.usage,this.disposeIntermediateTensorInfo(tempDenseInputHandle),this.texData.delete(encodedOutputTarget.dataId),texData.values=null,shouldTimeProgram&&(this.uploadWaitMs+=util_exports.now()-start)}else{let newTexture=this.acquireTexture(texShape,usage,dtype,isPacked);texData.texture=newTexture}}}convertAndCacheOnCPU(dataId,float32Values){let texData=this.texData.get(dataId),{dtype}=texData;return this.releaseGPUData(dataId),float32Values!=null&&(texData.values=float32ToTypedArray(float32Values,dtype)),texData.values}acquireTexture(texShape,texType,dtype,isPacked){if(this.numBytesInGPU+=this.computeBytes(texShape,dtype),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let mb=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(texShape,texType,isPacked)}computeBytes(shape,dtype){return shape[0]*shape[1]*util_exports.bytesPerElement(dtype)}};function float32ToTypedArray(a,dtype){if(dtype==="float32"||dtype==="complex64")return a;if(dtype==="int32"||dtype==="bool"){let result=dtype==="int32"?new Int32Array(a.length):new Uint8Array(a.length);for(let i=0;i<result.length;++i)result[i]=Math.round(a[i]);return result}else throw new Error(`Unknown dtype ${dtype}`)}var version6="2.8.2";device_util_exports.isBrowser()&&registerBackend("webgl",()=>new MathBackendWebGL,2);var CHECK_NAN_SNIPPET2=`
+    `}},whereImpl3=kernel_impls_exports.whereImpl,EPSILON_FLOAT322=1e-7,EPSILON_FLOAT162=1e-4,binaryCaches={};function getBinaryCache(webGLVersion){return webGLVersion in binaryCaches||(binaryCaches[webGLVersion]={}),binaryCaches[webGLVersion]}var CPU_HANDOFF_SIZE_THRESHOLD=128,BEFORE_PAGING_CONSTANT=600;function numMBBeforeWarning(){return env().global.screen==null?1024:env().global.screen.height*env().global.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024/1024}var MathBackendWebGL=class extends KernelBackend{constructor(gpgpu){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(gpgpu==null){let gl=getWebGLContext(env().getNumber("WEBGL_VERSION"));this.binaryCache=getBinaryCache(env().getNumber("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(gl),this.canvas=gl.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=gpgpu,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=gpgpu.gl.canvas;this.textureManager=new TextureManager(this.gpgpu),this.numMBBeforeWarning=numMBBeforeWarning(),this.texData=new DataStorage(this,engine())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(values,shape,dtype){if((env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||env().getBool("DEBUG"))&&this.checkNumericalProblems(values),dtype==="complex64"&&values!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let dataId={};return this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0}),dataId}incRef(dataId){let texData=this.texData.get(dataId);texData.refCount++}decRef(dataId){if(this.texData.has(dataId)){let texData=this.texData.get(dataId);texData.refCount--}}move(dataId,values,shape,dtype){if(env().getBool("DEBUG")&&this.checkNumericalProblems(values),dtype==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.texData.has(dataId)){let textureData=this.texData.get(dataId);textureData.refCount--,textureData.refCount<1&&this.disposeData(dataId)}}readSync(dataId){let texData=this.texData.get(dataId),{values,dtype,complexTensorInfos,slice:slice5,shape,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.readSync(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(dtype==="string")return values;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let result;if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);result=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else result=this.getValuesFromTexture(dataId);return shouldTimeProgram&&(this.downloadWaitMs+=util_exports.now()-start),this.convertAndCacheOnCPU(dataId,result)}async read(dataId){if(this.pendingRead.has(dataId)){let subscribers2=this.pendingRead.get(dataId);return new Promise(resolve=>subscribers2.push(resolve))}let texData=this.texData.get(dataId),{values,shape,slice:slice5,dtype,complexTensorInfos,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.read(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let buffer2=null,tmpDownloadTarget;if(dtype!=="complex64"&&env().get("WEBGL_BUFFER_SUPPORTED")){tmpDownloadTarget=this.decode(dataId);let tmpData=this.texData.get(tmpDownloadTarget.dataId);buffer2=this.gpgpu.createBufferFromTexture(tmpData.texture,...getDenseTexShape(shape))}this.pendingRead.set(dataId,[]),dtype!=="complex64"&&await this.gpgpu.createAndWaitForFence();let vals;if(dtype==="complex64"){let ps=await Promise.all([this.read(complexTensorInfos.real.dataId),this.read(complexTensorInfos.imag.dataId)]),realValues=ps[0],imagValues=ps[1];vals=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else if(buffer2==null)vals=this.getValuesFromTexture(dataId);else{let size=util_exports.sizeFromShape(shape);vals=this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2,size)}tmpDownloadTarget!=null&&this.disposeIntermediateTensorInfo(tmpDownloadTarget);let dTypeVals=this.convertAndCacheOnCPU(dataId,vals),subscribers=this.pendingRead.get(dataId);return this.pendingRead.delete(dataId),subscribers.forEach(resolve=>resolve(dTypeVals)),this.pendingDisposal.has(dataId)&&(this.pendingDisposal.delete(dataId),this.disposeData(dataId),this.pendingDeletes--),dTypeVals}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}checkNumericalProblems(values){if(values!=null)for(let i=0;i<values.length;i++){let num=values[i];if(!canBeRepresented(num))throw env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${num} cannot be represented on this device.`)}}getValuesFromTexture(dataId){let{shape,dtype,isPacked}=this.texData.get(dataId),size=util_exports.sizeFromShape(shape);if(env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let tmpTarget=this.decode(dataId),tmpData2=this.texData.get(tmpTarget.dataId),vals2=this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture,...getDenseTexShape(shape)).subarray(0,size);return this.disposeIntermediateTensorInfo(tmpTarget),vals2}let shouldUsePackedProgram=env().getBool("WEBGL_PACK")&&isPacked===!0,outputShape=shouldUsePackedProgram?getShapeAs3D(shape):shape,program=shouldUsePackedProgram?new EncodeFloatPackedProgram(outputShape):new EncodeFloatProgram(outputShape),output=this.runWebGLProgram(program,[{shape:outputShape,dtype,dataId}],"float32"),tmpData=this.texData.get(output.dataId),vals=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture,tmpData.texShape[0],tmpData.texShape[1]).subarray(0,size);return this.disposeIntermediateTensorInfo(output),vals}async time(f){let oldActiveTimers=this.activeTimers,newActiveTimers=[],outerMostTime=!1;this.programTimersStack==null?(this.programTimersStack=newActiveTimers,outerMostTime=!0):this.activeTimers.push(newActiveTimers),this.activeTimers=newActiveTimers,f();let flattenedActiveTimerQueries=util_exports.flatten(this.activeTimers.map(d=>d.query)).filter(d=>d!=null),flattenedActiveTimerNames=util_exports.flatten(this.activeTimers.map(d=>d.name)).filter(d=>d!=null);this.activeTimers=oldActiveTimers,outerMostTime&&(this.programTimersStack=null);let res={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let kernelMs=await Promise.all(flattenedActiveTimerQueries);res.kernelMs=util_exports.sum(kernelMs),res.getExtraProfileInfo=()=>kernelMs.map((d,i)=>({name:flattenedActiveTimerNames[i],ms:d})).map(d=>`${d.name}: ${d.ms}`).join(", ")}else res.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,res}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:util_exports.now(),endMs:null}}endTimer(query){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),query):(query.endMs=util_exports.now(),query)}async getQueryTime(query){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(query);let timerQuery=query;return timerQuery.endMs-timerQuery.startMs}disposeData(dataId){if(this.pendingDisposal.has(dataId))return;if(this.pendingRead.has(dataId)){this.pendingDisposal.add(dataId),this.pendingDeletes++;return}if(!this.texData.has(dataId))return;if(this.texData.get(dataId).complexParentRefCount>0){this.texData.get(dataId).refCount--;return}this.releaseGPUData(dataId);let{complexTensorInfos}=this.texData.get(dataId);complexTensorInfos!=null&&(this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.real),this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.imag)),this.texData.delete(dataId)}releaseGPUData(dataId){let{texture,dtype,texShape,usage,isPacked,slice:slice5}=this.texData.get(dataId),key=slice5&&slice5.origDataId||dataId,refCount=this.dataRefCount.get(key);refCount>1?this.dataRefCount.set(key,refCount-1):(this.dataRefCount.delete(key),texture!=null&&(this.numBytesInGPU-=this.computeBytes(texShape,dtype),this.textureManager.releaseTexture(texture,texShape,usage,isPacked)));let texData=this.texData.get(dataId);texData.texture=null,texData.texShape=null,texData.isPacked=!1,texData.slice=null}getTexture(dataId){return this.uploadToGPU(dataId),this.texData.get(dataId).texture}getDataInfo(dataId){return this.texData.get(dataId)}getCPUBackend(){return env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=engine().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(inputs,sizeThreshold=CPU_HANDOFF_SIZE_THRESHOLD){let cpuBackend=this.getCPUBackend();return!env().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&cpuBackend==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),cpuBackend!=null&&inputs.every(input2=>this.texData.get(input2.dataId).texture==null&&util_exports.sizeFromShape(input2.shape)<sizeThreshold)}getGPGPUContext(){return this.gpgpu}where(condition){backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let condVals=condition.dataSync();return whereImpl3(condition.shape,condVals)}packedUnaryOp(x,op2,dtype){let program=new UnaryOpPackedProgram(x.shape,op2);return this.compileAndRun(program,[x],dtype)}abs(x){if(this.shouldExecuteOnCPU([x])&&x.dtype!=="complex64"){let outValues=simpleAbsImplCPU(this.texData.get(x.dataId).values);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ABS,x.dtype);let program=new UnaryOpProgram(x.shape,ABS);return this.compileAndRun(program,[x])}makeTensorInfo(shape,dtype,values){let dataId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));dataId=this.write(encodedValues,shape,dtype)}else dataId=this.write(values,shape,dtype);return this.texData.get(dataId).usage=null,{dataId,shape,dtype}}makeOutput(shape,dtype,values){let{dataId}=this.makeTensorInfo(shape,dtype,values);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}unpackTensor(input2){let program=new UnpackProgram(input2.shape);return this.runWebGLProgram(program,[input2],input2.dtype)}packTensor(input2){let program=new PackProgram(input2.shape),preventEagerUnpackingOutput=!0;return this.runWebGLProgram(program,[input2],input2.dtype,null,preventEagerUnpackingOutput)}packedReshape(input2,afterShape){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=this.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}decode(dataId){let texData=this.texData.get(dataId),{isPacked,shape,dtype}=texData,shapeAs3D=getShapeAs3D(shape),program;isPacked?program=new DecodeMatrixPackedProgram(shapeAs3D):program=new DecodeMatrixProgram(shapeAs3D);let preventEagerUnpackingOfOutput=!0,out=this.runWebGLProgram(program,[{shape:shapeAs3D,dtype,dataId}],dtype,null,preventEagerUnpackingOfOutput);return{dtype,shape,dataId:out.dataId}}runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){let output=this.makeTensorInfo(program.outputShape,outputDtype),outData=this.texData.get(output.dataId);if(program.packedOutput&&(outData.isPacked=!0),program.outPackingScheme===PackingScheme.DENSE){let texelShape=getDenseTexShape(program.outputShape);outData.texShape=texelShape.map(d=>d*2)}if(program.outTexUsage!=null&&(outData.usage=program.outTexUsage),util_exports.sizeFromShape(output.shape)===0)return outData.values=util_exports.getTypedArrayFromDType(output.dtype,0),output;let dataToDispose=[],inputsData=inputs.map(input2=>{if(input2.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let texData=this.texData.get(input2.dataId);if(texData.texture==null){if(!program.packedInputs&&util_exports.sizeFromShape(input2.shape)<=env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:input2.shape,texData:null,isUniform:!0,uniformValues:texData.values};program.packedInputs&&(texData.isPacked=!0,texData.shape=input2.shape)}else if(!!texData.isPacked!=!!program.packedInputs)input2=texData.isPacked?this.unpackTensor(input2):this.packTensor(input2),dataToDispose.push(input2),texData=this.texData.get(input2.dataId);else if(texData.isPacked&&!isReshapeFree(texData.shape,input2.shape)){let savedInput=input2,targetShape=input2.shape;input2.shape=texData.shape,input2=this.packedReshape(input2,targetShape),dataToDispose.push(input2),texData=this.texData.get(input2.dataId),savedInput.shape=targetShape}return this.uploadToGPU(input2.dataId),{shape:input2.shape,texData,isUniform:!1}});this.uploadToGPU(output.dataId);let outputData={shape:output.shape,texData:outData,isUniform:!1},key=makeShaderKey(program,inputsData,outputData),binary=this.getAndSaveBinary(key,()=>compileProgram(this.gpgpu,program,inputsData,outputData)),shouldTimeProgram=this.activeTimers!=null,query;if(shouldTimeProgram&&(query=this.startTimer()),runProgram(this.gpgpu,binary,inputsData,outputData,customSetup),dataToDispose.forEach(info=>this.disposeIntermediateTensorInfo(info)),shouldTimeProgram&&(query=this.endTimer(query),this.activeTimers.push({name:program.constructor.name,query:this.getQueryTime(query)})),!env().getBool("WEBGL_LAZILY_UNPACK")&&outData.isPacked&&preventEagerUnpackingOfOutput===!1){let unpacked=this.unpackTensor(output);return this.disposeIntermediateTensorInfo(output),unpacked}return output}compileAndRun(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){outputDtype=outputDtype||inputs[0].dtype;let outInfo=this.runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput);return engine().makeTensorFromDataId(outInfo.dataId,outInfo.shape,outInfo.dtype)}getAndSaveBinary(key,getBinary){return key in this.binaryCache||(this.binaryCache[key]=getBinary()),this.binaryCache[key]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(env().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(key=>{this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram),delete this.binaryCache[key]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=tidy(()=>{if(!env().get("WEBGL_RENDER_FLOAT32_ENABLED")){let debugFlag=env().getBool("DEBUG");env().set("DEBUG",!1);let underflowCheckValue=this.abs(scalar(1e-8)).dataSync()[0];if(env().set("DEBUG",debugFlag),underflowCheckValue>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT322:EPSILON_FLOAT162}uploadToGPU(dataId){let texData=this.texData.get(dataId),{shape,dtype,values,texture,usage,isPacked}=texData;if(texture!=null)return;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let texShape=texData.texShape;if(texShape==null&&(texShape=getTextureShapeFromLogicalShape(shape,isPacked),texData.texShape=texShape),values!=null){let shapeAs3D=getShapeAs3D(shape),program,width=texShape[1],height=texShape[0],isByteArray=values instanceof Uint8Array;isPacked?([width,height]=getPackedMatrixTextureShapeWidthHeight(texShape[0],texShape[1]),program=new EncodeMatrixPackedProgram(shapeAs3D,[height,width],isByteArray)):program=new EncodeMatrixProgram(shapeAs3D,[height,width],isByteArray);let tempDenseInputHandle=this.makeTensorInfo([height,width],dtype);isByteArray?this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.PIXELS:this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId),width,height,values);let preventEagerUnpacking=!0,encodedOutputTarget=this.runWebGLProgram(program,[tempDenseInputHandle],dtype,null,preventEagerUnpacking),outputTexData=this.texData.get(encodedOutputTarget.dataId);texData.texture=outputTexData.texture,texData.texShape=outputTexData.texShape,texData.isPacked=outputTexData.isPacked,texData.usage=outputTexData.usage,this.disposeIntermediateTensorInfo(tempDenseInputHandle),this.texData.delete(encodedOutputTarget.dataId),texData.values=null,shouldTimeProgram&&(this.uploadWaitMs+=util_exports.now()-start)}else{let newTexture=this.acquireTexture(texShape,usage,dtype,isPacked);texData.texture=newTexture}}convertAndCacheOnCPU(dataId,float32Values){let texData=this.texData.get(dataId),{dtype}=texData;return this.releaseGPUData(dataId),float32Values!=null&&(texData.values=float32ToTypedArray(float32Values,dtype)),texData.values}acquireTexture(texShape,texType,dtype,isPacked){if(this.numBytesInGPU+=this.computeBytes(texShape,dtype),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let mb=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(texShape,texType,isPacked)}computeBytes(shape,dtype){return shape[0]*shape[1]*util_exports.bytesPerElement(dtype)}};function float32ToTypedArray(a,dtype){if(dtype==="float32"||dtype==="complex64")return a;if(dtype==="int32"||dtype==="bool"){let result=dtype==="int32"?new Int32Array(a.length):new Uint8Array(a.length);for(let i=0;i<result.length;++i)result[i]=Math.round(a[i]);return result}else throw new Error(`Unknown dtype ${dtype}`)}var version6="2.8.3";device_util_exports.isBrowser()&&registerBackend("webgl",()=>new MathBackendWebGL,2);var CHECK_NAN_SNIPPET2=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,BinaryOpProgram=class{constructor(op2,aShape,bShape){this.variableNames=["A","B"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
@@ -1814,7 +1814,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         ${lowerRow}
         setOutput(result);
       }
-    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null)||gpgpu.gl.uniform1iv(this.startLoc,start)}}};function shallowSlice(x,begin,size,backend2){let xTexData=backend2.texData.get(x.dataId),t=backend2.makeTensorInfo(size,x.dtype),newTexData=backend2.texData.get(t.dataId);Object.assign(newTexData,xTexData),newTexData.shape=size,newTexData.dtype=x.dtype;let flatOffset=slice_util_exports.computeFlatOffset(begin,util_exports.computeStrides(x.shape));xTexData.slice&&(flatOffset+=xTexData.slice.flatOffset),newTexData.slice={flatOffset,origDataId:xTexData.slice&&xTexData.slice.origDataId||x.dataId};let refCount=backend2.dataRefCount.get(newTexData.slice.origDataId)||1;return backend2.dataRefCount.set(newTexData.slice.origDataId,refCount+1),t}function slice3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs,[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);if(slice_util_exports.assertParamsValid(x,$begin,$size),util_exports.sizeFromShape($size)===0)return backend2.makeTensorInfo($size,x.dtype,[]);if(backend2.shouldExecuteOnCPU([x])||x.dtype==="string"){let xTexData=backend2.texData.get(x.dataId),outValues=sliceImplCPU(xTexData.values,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outValues)}let{isPacked}=backend2.texData.get(x.dataId),isContinous=slice_util_exports.isSliceContinous(x.shape,$begin,$size);if(isPacked||!isContinous){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram($size):new SliceProgram($size),customSetup=program.getCustomSetupFunc($begin);return backend2.runWebGLProgram(program,[x],x.dtype,customSetup)}return backend2.uploadToGPU(x.dataId),shallowSlice(x,$begin,$size,backend2)}var sliceConfig2={kernelName:Slice,backendName:"webgl",kernelFunc:slice3},batchToSpaceND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;util_exports.assert(x.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),toDispose=[],reshapedIntermediate=reshape4({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),transposedIntermediate=transpose3({inputs:{x:reshapedIntermediate},backend:backend2,attrs:{perm:permuted}}),reshapedIntermediate2=reshape4({inputs:{x:transposedIntermediate},backend:backend2,attrs:{shape:reshapedPermuted}}),sliced=slice3({inputs:{x:reshapedIntermediate2},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return toDispose.push(reshapedIntermediate),toDispose.push(transposedIntermediate),toDispose.push(reshapedIntermediate2),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),sliced},batchToSpaceNDConfig2={kernelName:BatchToSpaceND,backendName:"webgl",kernelFunc:batchToSpaceND3};function bincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.texData.get(x.dataId).values,weightsVals=backend2.texData.get(weights.dataId).values,outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig2={kernelName:Bincount,backendName:"webgl",kernelFunc:bincount3},NOT_EQUAL="return float(a != b);",notEqual3=binaryKernelFunc2({opSnippet:NOT_EQUAL,dtype:"bool"}),notEqualConfig2={kernelName:NotEqual,backendName:"webgl",kernelFunc:notEqual3};function real3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.real},backend:backend2})}var realConfig2={kernelName:Real,backendName:"webgl",kernelFunc:real3},TO_INT="return float(int(x));";function int(input2,backend2){let program=new UnaryOpProgram(input2.shape,TO_INT),output=backend2.runWebGLProgram(program,[input2],"int32");return{dataId:output.dataId,shape:output.shape,dtype:output.dtype}}function cast4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity3({inputs:{x},backend:backend2});let zerosTensor=zeros(x.shape),floatX=cast4({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex3({inputs:{real:floatX,imag:zerosTensor},backend:backend2});return zerosTensor.dispose(),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real3({inputs:{input:x},backend:backend2}),result=cast4({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity3({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32")return int(x,backend2);if(dtype==="bool"){let zerosTensorInfo=backend2.makeTensorInfo([],"bool",util_exports.getTypedArrayFromDType("bool",1)),result=notEqual3({inputs:{a:x,b:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),result}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig2={kernelName:Cast,backendName:"webgl",kernelFunc:cast4},CEIL="return ceil(x);",ceil3=unaryKernelFunc2({opSnippet:CEIL,packedOpSnippet:CEIL,cpuKernelImpl:ceilImplCPU}),ceilConfig2={kernelName:Ceil,backendName:"webgl",kernelFunc:ceil3},ClipProgram=class{constructor(aShape){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
+    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null)||gpgpu.gl.uniform1iv(this.startLoc,start)}}};function shallowSlice(x,begin,size,backend2){let xTexData=backend2.texData.get(x.dataId),t=backend2.makeTensorInfo(size,x.dtype),newTexData=backend2.texData.get(t.dataId);Object.assign(newTexData,xTexData),newTexData.shape=size,newTexData.dtype=x.dtype;let flatOffset=slice_util_exports.computeFlatOffset(begin,util_exports.computeStrides(x.shape));xTexData.slice&&(flatOffset+=xTexData.slice.flatOffset),newTexData.slice={flatOffset,origDataId:xTexData.slice&&xTexData.slice.origDataId||x.dataId};let refCount=backend2.dataRefCount.get(newTexData.slice.origDataId)||1;return backend2.dataRefCount.set(newTexData.slice.origDataId,refCount+1),t}function slice3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs,[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);if(slice_util_exports.assertParamsValid(x,$begin,$size),util_exports.sizeFromShape($size)===0)return backend2.makeTensorInfo($size,x.dtype,[]);if(backend2.shouldExecuteOnCPU([x])||x.dtype==="string"){let xTexData=backend2.texData.get(x.dataId),outValues=sliceImplCPU(xTexData.values,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outValues)}let{isPacked}=backend2.texData.get(x.dataId),isContinous=slice_util_exports.isSliceContinous(x.shape,$begin,$size);if(isPacked||!isContinous){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram($size):new SliceProgram($size),customSetup=program.getCustomSetupFunc($begin);return backend2.runWebGLProgram(program,[x],x.dtype,customSetup)}return backend2.uploadToGPU(x.dataId),shallowSlice(x,$begin,$size,backend2)}var sliceConfig2={kernelName:Slice,backendName:"webgl",kernelFunc:slice3},batchToSpaceND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;util_exports.assert(x.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),toDispose=[],reshapedIntermediate=reshape4({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),transposedIntermediate=transpose3({inputs:{x:reshapedIntermediate},backend:backend2,attrs:{perm:permuted}}),reshapedIntermediate2=reshape4({inputs:{x:transposedIntermediate},backend:backend2,attrs:{shape:reshapedPermuted}}),sliced=slice3({inputs:{x:reshapedIntermediate2},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return toDispose.push(reshapedIntermediate),toDispose.push(transposedIntermediate),toDispose.push(reshapedIntermediate2),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),sliced},batchToSpaceNDConfig2={kernelName:BatchToSpaceND,backendName:"webgl",kernelFunc:batchToSpaceND3};function bincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.readSync(x.dataId),weightsVals=backend2.readSync(weights.dataId),outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig2={kernelName:Bincount,backendName:"webgl",kernelFunc:bincount3},NOT_EQUAL="return float(a != b);",notEqual3=binaryKernelFunc2({opSnippet:NOT_EQUAL,dtype:"bool"}),notEqualConfig2={kernelName:NotEqual,backendName:"webgl",kernelFunc:notEqual3};function real3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.real},backend:backend2})}var realConfig2={kernelName:Real,backendName:"webgl",kernelFunc:real3},TO_INT="return float(int(x));";function int(input2,backend2){let program=new UnaryOpProgram(input2.shape,TO_INT),output=backend2.runWebGLProgram(program,[input2],"int32");return{dataId:output.dataId,shape:output.shape,dtype:output.dtype}}function cast4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity3({inputs:{x},backend:backend2});let zerosTensor=zeros(x.shape),floatX=cast4({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex3({inputs:{real:floatX,imag:zerosTensor},backend:backend2});return zerosTensor.dispose(),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real3({inputs:{input:x},backend:backend2}),result=cast4({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity3({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32")return int(x,backend2);if(dtype==="bool"){let zerosTensorInfo=backend2.makeTensorInfo([],"bool",util_exports.getTypedArrayFromDType("bool",1)),result=notEqual3({inputs:{a:x,b:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),result}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig2={kernelName:Cast,backendName:"webgl",kernelFunc:cast4},CEIL="return ceil(x);",ceil3=unaryKernelFunc2({opSnippet:CEIL,packedOpSnippet:CEIL,cpuKernelImpl:ceilImplCPU}),ceilConfig2={kernelName:Ceil,backendName:"webgl",kernelFunc:ceil3},ClipProgram=class{constructor(aShape){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1900,7 +1900,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         }
         setOutput(result);
       }
-    `}};function shiftedChannels(channels,channel,shift){let channelIdx=channels.indexOf(channel);return channels.map((c,idx)=>idx===channelIdx?`${c} - ${shift}`:c).join()}function imag3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.imag},backend:backend2})}var imagConfig2={kernelName:Imag,backendName:"webgl",kernelFunc:imag3};function concatImpl2(inputs,axis,backend2){let dtype=inputs[0].dtype;if(dtype==="complex64"){let reals=inputs.map(t=>real3({inputs:{input:t},backend:backend2})),imags=inputs.map(t=>imag3({inputs:{input:t},backend:backend2})),realConcated=concatImpl2(reals,axis,backend2),imagConcated=concatImpl2(imags,axis,backend2),result2=complex3({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result2}if(dtype==="string"){let{tensors2D:tensors2D2,outShape:outShape2}=computeTensors2D(inputs,axis,backend2),inputsValShapes=tensors2D2.map(t=>({vals:backend2.texData.get(t.dataId).values,shape:t.shape})),simplyConcat=tensors2D2[0].shape[0]===1,outVals=concatImplCPU(inputsValShapes,outShape2,dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),outInfo=backend2.makeTensorInfo(finalOutShape,dtype,outVals);return tensors2D2.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}if(inputs.length>env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(inputs.length/2),leftSide=concatImpl2(inputs.slice(0,midIndex),axis,backend2),rightSide=concatImpl2(inputs.slice(midIndex),axis,backend2),result2=concatImpl2([leftSide,rightSide],axis,backend2);return backend2.disposeIntermediateTensorInfo(leftSide),backend2.disposeIntermediateTensorInfo(rightSide),result2}if(env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&inputs[0].shape.length>1){let program2=new ConcatPackedProgram(inputs.map(t=>t.shape),axis);return backend2.runWebGLProgram(program2,inputs,dtype)}let{tensors2D,outShape}=computeTensors2D(inputs,axis,backend2),program=new ConcatProgram(tensors2D.map(t=>t.shape)),result=backend2.runWebGLProgram(program,tensors2D,dtype);tensors2D.forEach(r=>backend2.disposeIntermediateTensorInfo(r));let reshapedResult=reshape4({inputs:{x:result},attrs:{shape:outShape},backend:backend2});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}function computeTensors2D(inputs,axis,backend2){let outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis);return{tensors2D:inputs.map(x=>reshape4({inputs:{x},attrs:{shape:[-1,util_exports.sizeFromShape(x.shape.slice(axis))]},backend:backend2})),outShape}}function concat3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);return backend_util_exports.assertParamsConsistent(shapes,$axis),concatImpl2($inputs,$axis,backend2)}var concatConfig2={kernelName:Concat,backendName:"webgl",kernelFunc:concat3},Conv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivationWeights=!1,hasLeakyreluAlpha=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4,isChannelsLast=convInfo.dataFormat==="channelsLast",rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivationWeights?activationSnippet=`float activation(float a) {
+    `}};function shiftedChannels(channels,channel,shift){let channelIdx=channels.indexOf(channel);return channels.map((c,idx)=>idx===channelIdx?`${c} - ${shift}`:c).join()}function imag3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.imag},backend:backend2})}var imagConfig2={kernelName:Imag,backendName:"webgl",kernelFunc:imag3};function concatImpl2(inputs,axis,backend2){let dtype=inputs[0].dtype;if(dtype==="complex64"){let reals=inputs.map(t=>real3({inputs:{input:t},backend:backend2})),imags=inputs.map(t=>imag3({inputs:{input:t},backend:backend2})),realConcated=concatImpl2(reals,axis,backend2),imagConcated=concatImpl2(imags,axis,backend2),result2=complex3({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result2}if(dtype==="string"){let{tensors2D:tensors2D2,outShape:outShape2}=computeTensors2D(inputs,axis,backend2),inputsValShapes=tensors2D2.map(t=>({vals:backend2.readSync(t.dataId),shape:t.shape})),simplyConcat=tensors2D2[0].shape[0]===1,outVals=concatImplCPU(inputsValShapes,outShape2,dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),outInfo=backend2.makeTensorInfo(finalOutShape,dtype,outVals);return tensors2D2.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}if(inputs.length>env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(inputs.length/2),leftSide=concatImpl2(inputs.slice(0,midIndex),axis,backend2),rightSide=concatImpl2(inputs.slice(midIndex),axis,backend2),result2=concatImpl2([leftSide,rightSide],axis,backend2);return backend2.disposeIntermediateTensorInfo(leftSide),backend2.disposeIntermediateTensorInfo(rightSide),result2}if(env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&inputs[0].shape.length>1){let program2=new ConcatPackedProgram(inputs.map(t=>t.shape),axis);return backend2.runWebGLProgram(program2,inputs,dtype)}let{tensors2D,outShape}=computeTensors2D(inputs,axis,backend2),program=new ConcatProgram(tensors2D.map(t=>t.shape)),result=backend2.runWebGLProgram(program,tensors2D,dtype);tensors2D.forEach(r=>backend2.disposeIntermediateTensorInfo(r));let reshapedResult=reshape4({inputs:{x:result},attrs:{shape:outShape},backend:backend2});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}function computeTensors2D(inputs,axis,backend2){let outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis);return{tensors2D:inputs.map(x=>reshape4({inputs:{x},attrs:{shape:[-1,util_exports.sizeFromShape(x.shape.slice(axis))]},backend:backend2})),outShape}}function concat3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);return backend_util_exports.assertParamsConsistent(shapes,$axis),concatImpl2($inputs,$axis,backend2)}var concatConfig2={kernelName:Concat,backendName:"webgl",kernelFunc:concat3},Conv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivationWeights=!1,hasLeakyreluAlpha=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4,isChannelsLast=convInfo.dataFormat==="channelsLast",rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivationWeights?activationSnippet=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${activation2}
         }`:hasLeakyreluAlpha?activationSnippet=`float activation(float a) {
@@ -2166,7 +2166,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
 
         ${glsl.output} = result;
       }
-    `}};function conv2dByMatMul({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let xShape=x.shape,xTexData=backend2.texData.get(x.dataId),sharedMatMulDim=convInfo.inChannels,outerShapeX=xShape[0]*xShape[1]*xShape[2],outerShapeFilter=convInfo.outChannels,isChannelsLast=convInfo.dataFormat==="channelsLast",transposeA=!1,transposeB=!1,out,intermediates=[],batchMatMulWillBeUnpacked=(outerShapeX===1||outerShapeFilter===1)&&sharedMatMulDim>MATMUL_SHARED_DIM_THRESHOLD,reshapeWillBeExpensive=xShape[2]%2!==0&&!!xTexData.isPacked;if(batchMatMulWillBeUnpacked||!env().getBool("WEBGL_LAZILY_UNPACK")||!env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!reshapeWillBeExpensive){let targetShape=isChannelsLast?xShape[0]*xShape[1]*xShape[2]:xShape[0]*xShape[2]*xShape[3],xReshaped=reshape4({inputs:{x},backend:backend2,attrs:{shape:[1,targetShape,convInfo.inChannels]}}),filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}}),result=batchMatMulImpl({a:xReshaped,b:filterReshaped,transposeA,transposeB,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});out=reshape4({inputs:{x:result},backend:backend2,attrs:{shape:convInfo.outShape}}),intermediates.push(xReshaped),intermediates.push(filterReshaped),intermediates.push(result)}else{let targetShape=isChannelsLast?xShape[0]*xShape[1]*(xShape[2]+1):xShape[0]*xShape[2]*(xShape[3]+1),xReshaped={dataId:x.dataId,shape:[1,targetShape,convInfo.inChannels],dtype:x.dtype},originalXTexDataShape=xTexData.shape;xTexData.shape=xTexData.shape.slice(),xTexData.shape[xTexData.shape.length-2]++,util_exports.assert(isReshapeFree(xTexData.shape,xReshaped.shape),()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);let filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}});intermediates.push(filterReshaped);let pointwiseConv=batchMatMulImpl({a:xReshaped,b:filterReshaped,backend:backend2,transposeA,transposeB,bias,activation:activation2,preluActivationWeights,leakyreluAlpha}),pointwiseConvTexData=backend2.texData.get(pointwiseConv.dataId);util_exports.assert(pointwiseConvTexData.isPacked,()=>"batchMatMul result is expected to be packed"),xTexData.shape=originalXTexDataShape,pointwiseConvTexData.shape=convInfo.outShape,out=identity3({inputs:{x:pointwiseConv},backend:backend2}),out.shape=convInfo.outShape,intermediates.push(pointwiseConv)}for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let{filterWidth,filterHeight,inChannels,outWidth,outHeight,dataFormat}=convInfo,isChannelsLast=dataFormat==="channelsLast",sharedDim=filterWidth*filterHeight*inChannels,numCols=outHeight*outWidth,x2ColShape=[sharedDim,numCols],transposeA=!0,transposeB=!1,intermediates=[],xSqueezed=reshape4({inputs:{x},backend:backend2,attrs:{shape:x.shape.slice(1)}}),w2Row=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,sharedDim,util_exports.sizeFromShape(filter.shape)/sharedDim]}});intermediates.push(xSqueezed),intermediates.push(w2Row);let im2ColProgram=new Im2ColPackedProgram(x2ColShape,xSqueezed.shape,convInfo),im2Col=backend2.runWebGLProgram(im2ColProgram,[xSqueezed],"float32"),im2ColReshaped=reshape4({inputs:{x:im2Col},backend:backend2,attrs:{shape:[1,x2ColShape[0],x2ColShape[1]]}});intermediates.push(im2Col),intermediates.push(im2ColReshaped);let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,matmulProgram=new MatMulPackedProgram(im2ColReshaped.shape,w2Row.shape,[1,numCols,convInfo.outChannels],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs=[im2ColReshaped,w2Row];if(bias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let product=backend2.runWebGLProgram(matmulProgram,inputs,"float32"),outShape=isChannelsLast?[1,outHeight,outWidth,convInfo.outChannels]:[1,convInfo.outChannels,outHeight,outWidth],out=reshape4({inputs:{x:product},backend:backend2,attrs:{shape:outShape}});intermediates.push(product);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2d4(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out;if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2});else{let program=new Conv2DProgram(convInfo);out=backend2.runWebGLProgram(program,[x,filter],"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return backend2.disposeIntermediateTensorInfo(out),outReshaped}var conv2DConfig2={kernelName:Conv2D,backendName:"webgl",kernelFunc:conv2d4},Conv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast";this.userCode=`
+    `}};function conv2dByMatMul({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let xShape=x.shape,xTexData=backend2.texData.get(x.dataId),sharedMatMulDim=convInfo.inChannels,outerShapeX=xShape[0]*xShape[1]*xShape[2],outerShapeFilter=convInfo.outChannels,isChannelsLast=convInfo.dataFormat==="channelsLast",transposeA=!1,transposeB=!1,out,intermediates=[],batchMatMulWillBeUnpacked=(outerShapeX===1||outerShapeFilter===1)&&sharedMatMulDim>MATMUL_SHARED_DIM_THRESHOLD,reshapeWillBeExpensive=xShape[2]%2!=0&&!!xTexData.isPacked;if(batchMatMulWillBeUnpacked||!env().getBool("WEBGL_LAZILY_UNPACK")||!env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!reshapeWillBeExpensive){let targetShape=isChannelsLast?xShape[0]*xShape[1]*xShape[2]:xShape[0]*xShape[2]*xShape[3],xReshaped=reshape4({inputs:{x},backend:backend2,attrs:{shape:[1,targetShape,convInfo.inChannels]}}),filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}}),result=batchMatMulImpl({a:xReshaped,b:filterReshaped,transposeA,transposeB,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});out=reshape4({inputs:{x:result},backend:backend2,attrs:{shape:convInfo.outShape}}),intermediates.push(xReshaped),intermediates.push(filterReshaped),intermediates.push(result)}else{let targetShape=isChannelsLast?xShape[0]*xShape[1]*(xShape[2]+1):xShape[0]*xShape[2]*(xShape[3]+1),xReshaped={dataId:x.dataId,shape:[1,targetShape,convInfo.inChannels],dtype:x.dtype},originalXTexDataShape=xTexData.shape;xTexData.shape=xTexData.shape.slice(),xTexData.shape[xTexData.shape.length-2]++,util_exports.assert(isReshapeFree(xTexData.shape,xReshaped.shape),()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);let filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}});intermediates.push(filterReshaped);let pointwiseConv=batchMatMulImpl({a:xReshaped,b:filterReshaped,backend:backend2,transposeA,transposeB,bias,activation:activation2,preluActivationWeights,leakyreluAlpha}),pointwiseConvTexData=backend2.texData.get(pointwiseConv.dataId);util_exports.assert(pointwiseConvTexData.isPacked,()=>"batchMatMul result is expected to be packed"),xTexData.shape=originalXTexDataShape,pointwiseConvTexData.shape=convInfo.outShape,out=identity3({inputs:{x:pointwiseConv},backend:backend2}),out.shape=convInfo.outShape,intermediates.push(pointwiseConv)}for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let{filterWidth,filterHeight,inChannels,outWidth,outHeight,dataFormat}=convInfo,isChannelsLast=dataFormat==="channelsLast",sharedDim=filterWidth*filterHeight*inChannels,numCols=outHeight*outWidth,x2ColShape=[sharedDim,numCols],transposeA=!0,transposeB=!1,intermediates=[],xSqueezed=reshape4({inputs:{x},backend:backend2,attrs:{shape:x.shape.slice(1)}}),w2Row=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,sharedDim,util_exports.sizeFromShape(filter.shape)/sharedDim]}});intermediates.push(xSqueezed),intermediates.push(w2Row);let im2ColProgram=new Im2ColPackedProgram(x2ColShape,xSqueezed.shape,convInfo),im2Col=backend2.runWebGLProgram(im2ColProgram,[xSqueezed],"float32"),im2ColReshaped=reshape4({inputs:{x:im2Col},backend:backend2,attrs:{shape:[1,x2ColShape[0],x2ColShape[1]]}});intermediates.push(im2Col),intermediates.push(im2ColReshaped);let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,matmulProgram=new MatMulPackedProgram(im2ColReshaped.shape,w2Row.shape,[1,numCols,convInfo.outChannels],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs=[im2ColReshaped,w2Row];if(bias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let product=backend2.runWebGLProgram(matmulProgram,inputs,"float32"),outShape=isChannelsLast?[1,outHeight,outWidth,convInfo.outChannels]:[1,convInfo.outChannels,outHeight,outWidth],out=reshape4({inputs:{x:product},backend:backend2,attrs:{shape:outShape}});intermediates.push(product);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2d4(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out;if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2});else{let program=new Conv2DProgram(convInfo);out=backend2.runWebGLProgram(program,[x,filter],"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return backend2.disposeIntermediateTensorInfo(out),outReshaped}var conv2DConfig2={kernelName:Conv2D,backendName:"webgl",kernelFunc:conv2d4},Conv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2440,7 +2440,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(index){return(gpgpu,webGLProgram)=>{this.index==null&&(this.index=gpgpu.getUniformLocation(webGLProgram,"index")),gpgpu.gl.uniform1f(this.index,index)}}};function getCoords2(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.x, ${name}.y`;if(rank===3)return`${name}.x, ${name}.y, ${name}.z`;if(rank===4)return`${name}.x, ${name}.y, ${name}.z, ${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function getFinalCoord(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.y`;if(rank===3)return`${name}.z`;if(rank===4)return`${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function cumsum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];if(permutedAxis!==xRank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.shape.length-1} but got axis=${axis}`);let size=x.shape[permutedAxis],result=identity3({inputs:{x:permutedX},backend:backend2});for(let i=0;i<=Math.ceil(Math.log2(size))-1;i++){let program=new CumSumProgram(permutedX.shape,!1,reverse5),customSetup=program.getCustomSetupFunc(i),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype,customSetup),backend2.disposeIntermediateTensorInfo(prevResult)}if(exclusive){let program=new CumSumProgram(permutedX.shape,exclusive,reverse5),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype),backend2.disposeIntermediateTensorInfo(prevResult)}if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose3({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo(permutedX),reverseTransposedResult}return result}var cumsumConfig2={kernelName:Cumsum,backendName:"webgl",kernelFunc:cumsum3};function denseBincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.texData.get(x.dataId).values,weightsVals=backend2.texData.get(weights.dataId).values,outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImplCPU(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig2={kernelName:DenseBincount,backendName:"webgl",kernelFunc:denseBincount3},DepthToSpaceProgram=class{constructor(outputShape,blockSize,dataFormat){this.variableNames=["x"],this.outputShape=[],this.outputShape=outputShape,this.blockSize=blockSize,this.dataFormat=dataFormat,this.userCode=`
+    `}getCustomSetupFunc(index){return(gpgpu,webGLProgram)=>{this.index==null&&(this.index=gpgpu.getUniformLocation(webGLProgram,"index")),gpgpu.gl.uniform1f(this.index,index)}}};function getCoords2(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.x, ${name}.y`;if(rank===3)return`${name}.x, ${name}.y, ${name}.z`;if(rank===4)return`${name}.x, ${name}.y, ${name}.z, ${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function getFinalCoord(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.y`;if(rank===3)return`${name}.z`;if(rank===4)return`${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function cumsum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];if(permutedAxis!==xRank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.shape.length-1} but got axis=${axis}`);let size=x.shape[permutedAxis],result=identity3({inputs:{x:permutedX},backend:backend2});for(let i=0;i<=Math.ceil(Math.log2(size))-1;i++){let program=new CumSumProgram(permutedX.shape,!1,reverse5),customSetup=program.getCustomSetupFunc(i),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype,customSetup),backend2.disposeIntermediateTensorInfo(prevResult)}if(exclusive){let program=new CumSumProgram(permutedX.shape,exclusive,reverse5),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype),backend2.disposeIntermediateTensorInfo(prevResult)}if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose3({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo(permutedX),reverseTransposedResult}return result}var cumsumConfig2={kernelName:Cumsum,backendName:"webgl",kernelFunc:cumsum3};function denseBincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.readSync(x.dataId),weightsVals=backend2.readSync(weights.dataId),outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImplCPU(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig2={kernelName:DenseBincount,backendName:"webgl",kernelFunc:denseBincount3},DepthToSpaceProgram=class{constructor(outputShape,blockSize,dataFormat){this.variableNames=["x"],this.outputShape=[],this.outputShape=outputShape,this.blockSize=blockSize,this.dataFormat=dataFormat,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2521,7 +2521,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
           vec4 xR${r}C${c} = vec4(0.);`;for(let r=0;r<filterHeight;r++)for(let texelC=0;texelC<texelsAcross;texelC++){let c=texelC*2;if(mainLoop+=`
           xR = xRCorner + ${r*dilationHeight};
           xC = xCCorner + ${c*dilationWidth};
-        `,strideWidth===1){if(c<filterWidth&&(padLeft%2===1?mainLoop+=`
+        `,strideWidth===1){if(c<filterWidth&&(padLeft%2==1?mainLoop+=`
                 xCOffset = xC + 1;
                 if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
                   xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
@@ -2557,7 +2557,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
                 }
 
                 xR${r}C${c} = xTexelR${r}C${c};
-              `,c+1<filterWidth)){let nextTexelOffset=padLeft%2===0?util_exports.nearestLargerEven(dilationWidth):dilationWidth;dilationWidth%2===0&&padLeft%2===1||dilationWidth%2!==0&&padLeft%2!==1?(mainLoop+=`
+              `,c+1<filterWidth)){let nextTexelOffset=padLeft%2==0?util_exports.nearestLargerEven(dilationWidth):dilationWidth;dilationWidth%2==0&&padLeft%2==1||dilationWidth%2!=0&&padLeft%2!=1?(mainLoop+=`
                   xCOffset = xC + ${padLeft%2} + ${nextTexelOffset};
 
                   if(xR >= 0 && xR < ${xNumRows} &&
@@ -2586,7 +2586,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
                   xR${r}C${c+1} = xTexelR${r}C${c+2};
                 `}}else c<filterWidth&&(mainLoop+=`
               if(xR >= 0 && xR < ${xNumRows}) {
-            `,padLeft%2===1?(mainLoop+=`
+            `,padLeft%2==1?(mainLoop+=`
                 xCOffset = xC + 1 - ${strideWidth};
                 if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                   xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
@@ -2668,7 +2668,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         ${applyActivationSnippet}
         setOutput(result);
       }
-    `}};function depthwiseConv2dNative2(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs,$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),program;return env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1?program=new DepthwiseConvPacked2DProgram(convInfo):program=new DepthwiseConv2DProgram(convInfo),backend2.runWebGLProgram(program,[x,filter],"float32")}var depthwiseConv2dNativeConfig2={kernelName:DepthwiseConv2dNative,backendName:"webgl",kernelFunc:depthwiseConv2dNative2},DepthwiseConv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
+    `}};function depthwiseConv2dNative2(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs,$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),program;return env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels==1?program=new DepthwiseConvPacked2DProgram(convInfo):program=new DepthwiseConv2DProgram(convInfo),backend2.runWebGLProgram(program,[x,filter],"float32")}var depthwiseConv2dNativeConfig2={kernelName:DepthwiseConv2dNative,backendName:"webgl",kernelFunc:depthwiseConv2dNative2},DepthwiseConv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2961,7 +2961,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
 
         ${glsl.output} = result;
       }
-    `}},fromPixelsConfig={kernelName:FromPixels,backendName:"webgl",kernelFunc:fromPixels2},fromPixels2DContext2;function fromPixels2(args){let{inputs,backend:backend2,attrs}=args,{pixels}=inputs,{numChannels}=attrs,isVideo=typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement,isImage=typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement,[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],texShape=[height,width],outShape=[height,width,numChannels];(isImage||isVideo)&&(fromPixels2DContext2==null&&(fromPixels2DContext2=document.createElement("canvas").getContext("2d")),fromPixels2DContext2.canvas.width=width,fromPixels2DContext2.canvas.height=height,fromPixels2DContext2.drawImage(pixels,0,0,width,height),pixels=fromPixels2DContext2.canvas);let tempPixelHandle=backend2.makeTensorInfo(texShape,"int32");backend2.texData.get(tempPixelHandle.dataId).usage=TextureUsage.PIXELS,backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId),pixels);let program=env().getBool("WEBGL_PACK")?new FromPixelsPackedProgram(outShape):new FromPixelsProgram(outShape),res=backend2.runWebGLProgram(program,[tempPixelHandle],"int32");return backend2.disposeData(tempPixelHandle.dataId),res}function fusedConv2d(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out,intermediates=[];if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else{let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!1):null,program=new Conv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs2=[x,filter];if(bias&&inputs2.push(bias),preluActivationWeights&&inputs2.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs2.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}out=backend2.runWebGLProgram(program,inputs2,"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return intermediates.push(out),intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outReshaped}var fusedConv2DConfig2={kernelName:FusedConv2D,backendName:"webgl",kernelFunc:fusedConv2d};function fusedDepthwiseConv2D2(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,intermediates=[],$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),shouldPackDepthwiseConv=env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1,fusedActivation=activation2?mapActivationToShaderProgram(activation2,shouldPackDepthwiseConv):null,programInputs=[x,filter],hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu";if(hasBias&&programInputs.push(bias),hasPreluActivationWeights&&programInputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));programInputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let program;shouldPackDepthwiseConv?program=new DepthwiseConvPacked2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha):program=new DepthwiseConv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha);let result=backend2.runWebGLProgram(program,programInputs,"float32");return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var fusedDepthwiseConv2DConfig2={kernelName:FusedDepthwiseConv2D,backendName:"webgl",kernelFunc:fusedDepthwiseConv2D2},GatherNDProgram=class{constructor(sliceDim,strides,shape){this.sliceDim=sliceDim,this.strides=strides,this.variableNames=["x","indices"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),strideString=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},fromPixelsConfig={kernelName:FromPixels,backendName:"webgl",kernelFunc:fromPixels2},fromPixels2DContext2;function fromPixels2(args){let{inputs,backend:backend2,attrs}=args,{pixels}=inputs,{numChannels}=attrs,isVideo=typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement,isImage=typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement,isImageBitmap=typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap,[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],texShape=[height,width],outShape=[height,width,numChannels];(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext2==null&&(fromPixels2DContext2=document.createElement("canvas").getContext("2d")),fromPixels2DContext2.canvas.width=width,fromPixels2DContext2.canvas.height=height,fromPixels2DContext2.drawImage(pixels,0,0,width,height),pixels=fromPixels2DContext2.canvas);let tempPixelHandle=backend2.makeTensorInfo(texShape,"int32");backend2.texData.get(tempPixelHandle.dataId).usage=TextureUsage.PIXELS,backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId),pixels);let program=env().getBool("WEBGL_PACK")?new FromPixelsPackedProgram(outShape):new FromPixelsProgram(outShape),res=backend2.runWebGLProgram(program,[tempPixelHandle],"int32");return backend2.disposeData(tempPixelHandle.dataId),res}function fusedConv2d(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out,intermediates=[];if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else{let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!1):null,program=new Conv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs2=[x,filter];if(bias&&inputs2.push(bias),preluActivationWeights&&inputs2.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs2.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}out=backend2.runWebGLProgram(program,inputs2,"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return intermediates.push(out),intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outReshaped}var fusedConv2DConfig2={kernelName:FusedConv2D,backendName:"webgl",kernelFunc:fusedConv2d};function fusedDepthwiseConv2D2(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,intermediates=[],$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),shouldPackDepthwiseConv=env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels==1,fusedActivation=activation2?mapActivationToShaderProgram(activation2,shouldPackDepthwiseConv):null,programInputs=[x,filter],hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu";if(hasBias&&programInputs.push(bias),hasPreluActivationWeights&&programInputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));programInputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let program;shouldPackDepthwiseConv?program=new DepthwiseConvPacked2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha):program=new DepthwiseConv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha);let result=backend2.runWebGLProgram(program,programInputs,"float32");return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var fusedDepthwiseConv2DConfig2={kernelName:FusedDepthwiseConv2D,backendName:"webgl",kernelFunc:fusedDepthwiseConv2D2},GatherNDProgram=class{constructor(sliceDim,strides,shape){this.sliceDim=sliceDim,this.strides=strides,this.variableNames=["x","indices"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),strideString=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${stridesType} strides = ${stridesType}(${this.strides});
          void main() {
           ${dtype} coords = getOutputCoords();
@@ -3921,7 +3921,7 @@ return a / b;`,DIV_PACKED=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,softplus3=unaryKernelFunc2({opSnippet:SOFTPLUS}),softplusConfig2={kernelName:Softplus,backendName:"webgl",kernelFunc:softplus3},spaceToBatchND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;util_exports.assert(x.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let toDispose=[],paddedX=padV22({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),reshapedPaddedX=reshape4({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose3({inputs:{x:reshapedPaddedX},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape4({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return toDispose.push(paddedX),toDispose.push(reshapedPaddedX),toDispose.push(paddedXT),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result},spaceToBatchNDConfig2={kernelName:SpaceToBatchND,backendName:"webgl",kernelFunc:spaceToBatchND3};function sparseToDense3(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,program=new ScatterProgram(numUpdates,sliceRank,sparseIndices.shape.length,sparseValues.shape.length,strides,[outputSize,1],sumDupeIndices),res=backend2.runWebGLProgram(program,[sparseValues,sparseIndices,defaultValue],sparseValues.dtype),reshaped=reshape4({inputs:{x:res},backend:backend2,attrs:{shape:outputShape}});return backend2.disposeIntermediateTensorInfo(res),reshaped}var sparseToDenseConfig2={kernelName:SparseToDense,backendName:"webgl",kernelFunc:sparseToDense3};function splitV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),xRank=x.shape.length,begin=new Array(xRank).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[axis]=s;let sliceT=slice3({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[axis]+=s,sliceT})}var splitVConfig2={kernelName:SplitV,backendName:"webgl",kernelFunc:splitV2},SQRT="return sqrt(x);",sqrt3=unaryKernelFunc2({opSnippet:SQRT}),sqrtConfig2={kernelName:Sqrt,backendName:"webgl",kernelFunc:sqrt3},SQUARE="return x * x;",square3=unaryKernelFunc2({opSnippet:SQUARE}),squareConfig2={kernelName:Square,backendName:"webgl",kernelFunc:square3},SQUARED_DIFFERENCE="return (a - b) * (a - b);",squaredDifference3=binaryKernelFunc2({opSnippet:SQUARED_DIFFERENCE,packedOpSnippet:SQUARED_DIFFERENCE}),squaredDifferenceConfig2={kernelName:SquaredDifference,backendName:"webgl",kernelFunc:squaredDifference3};function step3({inputs,attrs,backend:backend2}){let{x}=inputs,opSnippet=CHECK_NAN_SNIPPET+`
+`,softplus3=unaryKernelFunc2({opSnippet:SOFTPLUS}),softplusConfig2={kernelName:Softplus,backendName:"webgl",kernelFunc:softplus3},spaceToBatchND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;util_exports.assert(x.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let toDispose=[],paddedX=padV22({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),reshapedPaddedX=reshape4({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose3({inputs:{x:reshapedPaddedX},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape4({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return toDispose.push(paddedX),toDispose.push(reshapedPaddedX),toDispose.push(paddedXT),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result},spaceToBatchNDConfig2={kernelName:SpaceToBatchND,backendName:"webgl",kernelFunc:spaceToBatchND3};function sparseToDense3(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,program=new ScatterProgram(numUpdates,sliceRank,sparseIndices.shape.length,sparseValues.shape.length,strides,[outputSize,1],sumDupeIndices),res=backend2.runWebGLProgram(program,[sparseValues,sparseIndices,defaultValue],sparseValues.dtype),reshaped=reshape4({inputs:{x:res},backend:backend2,attrs:{shape:outputShape}});return backend2.disposeIntermediateTensorInfo(res),reshaped}var sparseToDenseConfig2={kernelName:SparseToDense,backendName:"webgl",kernelFunc:sparseToDense3};function splitV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),xRank=x.shape.length,begin=new Array(xRank).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[$axis]=s;let sliceT=slice3({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[$axis]+=s,sliceT})}var splitVConfig2={kernelName:SplitV,backendName:"webgl",kernelFunc:splitV2},SQRT="return sqrt(x);",sqrt3=unaryKernelFunc2({opSnippet:SQRT}),sqrtConfig2={kernelName:Sqrt,backendName:"webgl",kernelFunc:sqrt3},SQUARE="return x * x;",square3=unaryKernelFunc2({opSnippet:SQUARE}),squareConfig2={kernelName:Square,backendName:"webgl",kernelFunc:square3},SQUARED_DIFFERENCE="return (a - b) * (a - b);",squaredDifference3=binaryKernelFunc2({opSnippet:SQUARED_DIFFERENCE,packedOpSnippet:SQUARED_DIFFERENCE}),squaredDifferenceConfig2={kernelName:SquaredDifference,backendName:"webgl",kernelFunc:squaredDifference3};function step3({inputs,attrs,backend:backend2}){let{x}=inputs,opSnippet=CHECK_NAN_SNIPPET+`
     return x > 0.0 ? 1.0 : float(${attrs.alpha});
   `,program=new UnaryOpProgram(x.shape,opSnippet);return backend2.runWebGLProgram(program,[x],x.dtype)}var stepConfig2={kernelName:Step,backendName:"webgl",kernelFunc:step3},StridedSliceProgram=class{constructor(begin,strides,size){this.variableNames=["x"],this.outputShape=size;let rank=size.length,inputDtype=getCoordsDataType(size.length),dtype=getCoordsDataType(size.length),newCoords="";if(rank===1)newCoords="coords * strides + begin";else{let outputAxis=0;newCoords=size.map((_,i)=>(outputAxis++,size.length===1?`coords * strides[${i}] + begin[${i}]`:`coords[${outputAxis-1}] * strides[${i}] + begin[${i}]`)).join(",")}this.userCode=`
       ${inputDtype} begin = ${inputDtype}(${begin});
@@ -3939,7 +3939,7 @@ return a / b;`,DIV_PACKED=`
         ${dtype} resRC = getOutputCoords();
         setOutput(getA(${sourceCoords}));
       }
-    `}};function getSourceCoords3(aShape){let rank=aShape.length;if(rank>5)throw Error(`Tile for rank ${rank} is not yet supported`);if(rank===1)return`imod(resRC, ${aShape[0]})`;let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],sourceCoords=[];for(let i=0;i<aShape.length;i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);return sourceCoords.join()}function tile4(params){let{inputs,backend:backend2,attrs}=params,{x}=inputs,{reps}=attrs;if(x.dtype==="string"){let decodedData=backend2.texData.get(x.dataId).values.map(d=>util_exports.decodeString(d)),buf=buffer(x.shape,x.dtype,decodedData),outBuf=tileImplCPU(buf,reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let program=new TileProgram(x.shape,reps);return backend2.runWebGLProgram(program,[x],x.dtype)}var tileConfig2={kernelName:Tile,backendName:"webgl",kernelFunc:tile4};function topK2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs,xVals=backend2.texData.get(x.dataId).values,[allTopKVals,allTopKIndices]=topKImplCPU(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig2={kernelName:TopK,backendName:"webgl",kernelFunc:topK2};function unique4(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex2(x,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let values=backend2.readSync(x.dataId),{outputValues,outputShape,indices}=uniqueImplCPU(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig2={kernelName:Unique,backendName:"webgl",kernelFunc:unique4};function unpack2(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let x=value,xRank=x.shape.length,num=value.shape[axis],outShape=new Array(xRank-1),outIndex=0;for(let i=0;i<xRank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let toDispose=[],begin=new Array(xRank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let sliced=slice3({inputs:{x},backend:backend2,attrs:{begin,size}}),reshaped=reshape4({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}});res[i]=reshaped,toDispose.push(sliced)}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),res}var unpackConfig2={kernelName:Unpack,backendName:"webgl",kernelFunc:unpack2},SegmentOpProgram=class{constructor(segOpInfo,segOpType){this.variableNames=["x","segmentIds"];let windowSize=segOpInfo.windowSize,batchSize=segOpInfo.batchSize,inSize=segOpInfo.inSize,numSegments=segOpInfo.numSegments,outSize=numSegments*Math.ceil(inSize/windowSize);this.outputShape=[batchSize,outSize];let initializationValue="0.0",returnValue="sumValue",windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
+    `}};function getSourceCoords3(aShape){let rank=aShape.length;if(rank>5)throw Error(`Tile for rank ${rank} is not yet supported`);if(rank===1)return`imod(resRC, ${aShape[0]})`;let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],sourceCoords=[];for(let i=0;i<aShape.length;i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);return sourceCoords.join()}function tile4(params){let{inputs,backend:backend2,attrs}=params,{x}=inputs,{reps}=attrs;if(x.dtype==="string"){let decodedData=backend2.readSync(x.dataId).map(d=>util_exports.decodeString(d)),buf=buffer(x.shape,x.dtype,decodedData),outBuf=tileImplCPU(buf,reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let program=new TileProgram(x.shape,reps);return backend2.runWebGLProgram(program,[x],x.dtype)}var tileConfig2={kernelName:Tile,backendName:"webgl",kernelFunc:tile4};function topK2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs,xVals=backend2.readSync(x.dataId),[allTopKVals,allTopKIndices]=topKImplCPU(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig2={kernelName:TopK,backendName:"webgl",kernelFunc:topK2};function unique4(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex2(x,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let values=backend2.readSync(x.dataId),{outputValues,outputShape,indices}=uniqueImplCPU(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig2={kernelName:Unique,backendName:"webgl",kernelFunc:unique4};function unpack2(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let x=value,xRank=x.shape.length,num=value.shape[axis],outShape=new Array(xRank-1),outIndex=0;for(let i=0;i<xRank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let toDispose=[],begin=new Array(xRank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let sliced=slice3({inputs:{x},backend:backend2,attrs:{begin,size}}),reshaped=reshape4({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}});res[i]=reshaped,toDispose.push(sliced)}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),res}var unpackConfig2={kernelName:Unpack,backendName:"webgl",kernelFunc:unpack2},SegmentOpProgram=class{constructor(segOpInfo,segOpType){this.variableNames=["x","segmentIds"];let windowSize=segOpInfo.windowSize,batchSize=segOpInfo.batchSize,inSize=segOpInfo.inSize,numSegments=segOpInfo.numSegments,outSize=numSegments*Math.ceil(inSize/windowSize);this.outputShape=[batchSize,outSize];let initializationValue="0.0",returnValue="sumValue",windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
         sumValue += dot(values, segFilter);
     `,checkValueOutOfBounds="";inSize%windowSize>0&&(checkValueOutOfBounds=`
         if (inIdx < 0 || inIdx >= ${inSize}) {
@@ -4045,7 +4045,7 @@ return a / b;`,DIV_PACKED=`
         }
         setOutput(${returnValue});
       }
-    `}};function unsortedSegmentSum3(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape4({inputs:{x:permutedX},backend:backend2,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend2.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range4({backend:backend2,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile4({inputs:{x:rangeInfo},backend:backend2,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape4({inputs:{x:segOpResult},backend:backend2,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend2,attrs:{perm}})}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3},kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var version7="2.8.2",version8={"tfjs-core":version,"tfjs-backend-cpu":version5,"tfjs-backend-webgl":version6,"tfjs-data":version4,"tfjs-layers":version2,"tfjs-converter":version3,tfjs:version7},CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend2){wasmFusedMatMul=backend2.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend2.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend2,inputs}=args,{a,b}=inputs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend2.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend2.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend2){wasmFunc=backend2.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend2}=args,out=backend2.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend2.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity4},wasmTranspose;function setup2(backend2){wasmTranspose=backend2.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend2,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i<perm.length;i++)perm[i]!==i&&(permIsNoOp=!1);let outShape=computeOutShape4(inputs.x.shape,attrs.perm),x={dataId:inputs.x.dataId,shape:reducedShape,dtype:inputs.x.dtype};if(permIsNoOp){let cloned=identity4({inputs,backend:backend2});return cloned.shape=outShape,cloned}let out=backend2.makeOutput(outShape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,permBytes=new Uint8Array(new Int32Array(perm).buffer),xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmTranspose(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],outId,permBytes,perm.length),out}function computeOutShape4(inShape,perm){let outShape=new Array(inShape.length);for(let i=0;i<outShape.length;i++)outShape[i]=inShape[perm[i]];return outShape}function removeOneSizeDims(shape,perm){let newShape=[],newPerm=[];for(let i=0;i<shape.length;++i)shape[i]!==1&&newShape.push(shape[i]),shape[perm[i]]!==1&&newPerm.push(perm[i]);for(let i=0;i<newPerm.length;++i){let minValIdx=-1;for(let j=0;j<newPerm.length;++j)newPerm[j]>=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend2){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xTransposed=transpose4({inputs:{x},attrs:{perm:permutedAxes},backend:backend2});let xId=backend2.dataIdMap.get(x.dataId).id;backend2.dataIdMap.get(xTransposed.dataId).id!==xId&&(inputWasTransposed=!0)}return{transposed:xTransposed,originalAxes,axes,inputWasTransposed}}var wasmFunc2;function setup3(backend2){wasmFunc2=backend2.wasm.cwrap(ArgMax,null,["number","number","number","number","number"])}function argmax(args){let{backend:backend2,inputs,attrs}=args,{axis}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let outShape=input2.shape.slice(0,-1),out=backend2.makeOutput(outShape,"int32"),outId=backend2.dataIdMap.get(out.dataId).id,outerSize=util_exports.sizeFromShape(out.shape),innerSize=input2.shape[axes[0]];return wasmFunc2(inputId,CppDType[input2.dtype],outerSize,innerSize,outId),inputWasTransposed&&backend2.disposeData(transposed.dataId),out}var argMaxConfig3={kernelName:ArgMax,backendName:"wasm",kernelFunc:argmax,setupFunc:setup3},wasmAvgPool;function setup4(backend2){wasmAvgPool=backend2.wasm.cwrap(AvgPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function avgPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,channels=convInfo.inChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);if(convInfo.dilationWidth!==1||convInfo.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmAvgPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,strideHeight,strideWidth,channels,outId),out}var avgPoolConfig3={kernelName:AvgPool,backendName:"wasm",setupFunc:setup4,kernelFunc:avgPool4};function reshape5(args){let{inputs,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize);return util_exports.assert(xSize===util_exports.sizeFromShape($shape),()=>`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape5},wasmBatchMatMul;function setup5(backend2){wasmBatchMatMul=backend2.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape5({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape5({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),a3dId=backend2.dataIdMap.get(a3d.dataId).id,b3dId=backend2.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend2.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast5(args){let{inputs:{x},attrs:{dtype},backend:backend2}=args,out=backend2.makeOutput(x.shape,dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast5},wasmClip;function setup6(backend2){wasmClip=backend2.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend2}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity4({inputs:{x:$inputs[0]},backend:backend2});let out=backend2.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape5({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend2.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend2.typedArrayFromHeap(input2)),outVals=backend2.typedArrayFromHeap(out);for(let b=0;b<batchDim;b++){let outOffset=b*sumInnerDims;for(let i=0;i<inVals.length;i++){let innerDim=innerDims[i],inOffset=b*innerDim,vals=inVals[i].subarray(inOffset,inOffset+innerDim);outVals.set(vals,outOffset),outOffset+=innerDim}}return out}var concatConfig3={kernelName:Concat,backendName:"wasm",kernelFunc:concat4},wasmConv2d;function setup7(backend2){wasmConv2d=backend2.wasm.cwrap(Conv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode,dataFormat}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var conv2DConfig3={kernelName:Conv2D,backendName:"wasm",setupFunc:setup7,kernelFunc:conv2d5},wasmConv2DBackpropInput;function setup8(backend2){wasmConv2DBackpropInput=backend2.wasm.cwrap(Conv2DBackpropInput,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2DBackpropInput4(args){let{backend:backend2,inputs,attrs}=args,{dy,filter}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,inputShape}=attrs,dilations=1,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast",dxStrides=util_exports.computeStrides(convInfo.inShape),dyStrides=util_exports.computeStrides(dy.shape),[fltS0,fltS1,fltS2]=util_exports.computeStrides(filter.shape),xBatchStride=dxStrides[0],xRowStride=isChannelsLast?dxStrides[1]:dxStrides[2],xColStride=isChannelsLast?dxStrides[2]:1,xChannelStride=isChannelsLast?1:dxStrides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1],out=backend2.makeOutput(convInfo.inShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,dyId=backend2.dataIdMap.get(dy.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id;return wasmConv2DBackpropInput(dyId,filterId,batchSize,filterHeight,filterWidth,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,strideHeight,strideWidth,topPad,leftPad,fltS0,fltS1,fltS2,xBatchStride,xRowStride,xColStride,xChannelStride,yBatchStride,yRowStride,yColStride,yChannelStride,outId),out}var conv2DBackpropInputConfig3={kernelName:Conv2DBackpropInput,backendName:"wasm",setupFunc:setup8,kernelFunc:conv2DBackpropInput4},cosConfig3=createUnaryKernelConfig(Cos),InterpolationMethod;(function(InterpolationMethod2){InterpolationMethod2[InterpolationMethod2.bilinear=0]="bilinear",InterpolationMethod2[InterpolationMethod2.nearest=1]="nearest"})(InterpolationMethod||(InterpolationMethod={}));var wasmCropAndResize;function setup9(backend2){wasmCropAndResize=backend2.wasm.cwrap(CropAndResize,null,["number","number","number","number","array","number","number","number","number","number"])}function cropAndResize4(args){let{backend:backend2,inputs,attrs}=args,{method,extrapolationValue,cropSize}=attrs,{image:image3,boxes,boxInd}=inputs,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,outShape=[numBoxes,cropHeight,cropWidth,image3.shape[3]],imagesData=backend2.dataIdMap.get(image3.dataId),castedData;image3.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:image3},attrs:{dtype:"float32"}}),imagesData=backend2.dataIdMap.get(castedData.dataId));let imagesId=imagesData.id,boxesId=backend2.dataIdMap.get(boxes.dataId).id,boxIndId=backend2.dataIdMap.get(boxInd.dataId).id,out=backend2.makeOutput(outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,imagesShapeBytes=new Uint8Array(new Int32Array(image3.shape).buffer);return wasmCropAndResize(imagesId,boxesId,boxIndId,numBoxes,imagesShapeBytes,cropHeight,cropWidth,InterpolationMethod[method],extrapolationValue,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var cropAndResizeConfig3={kernelName:CropAndResize,backendName:"wasm",setupFunc:setup9,kernelFunc:cropAndResize4},wasmCumsum;function setup10(backend2){wasmCumsum=backend2.wasm.cwrap(Cumsum,null,["number","number","number","number","number","number"])}function cumsum4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length;util_exports.assert(x.dtype==="float32"||x.dtype==="int32",()=>`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend2}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend2.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend2.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend2.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend2}),backend2.disposeData(permutedX.dataId),backend2.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4},wasmDepthToSpace;function setup11(backend2){wasmDepthToSpace=backend2.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend2.makeOutput(outputShape,"float32"),xId=backend2.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4},wasmDepthwiseConv2d;function setup12(backend2){wasmDepthwiseConv2d=backend2.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d5},supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool"),expConfig3=createUnaryKernelConfig(Exp);function expandDims5(args){let{inputs,attrs,backend:backend2}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape5({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims5};function fill4(args){let{attrs:{shape,value,dtype},backend:backend2}=args,out=backend2.makeOutput(shape,dtype);return backend2.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4},wasmFlipLeftRight;function setup13(backend2){wasmFlipLeftRight=backend2.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend2}=args,{image:image3}=inputs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},floorConfig3=createUnaryKernelConfig(Floor),supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3),wasmBatchNorm;function setup14(backend2){wasmBatchNorm=backend2.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend2,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale22}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,meanId=backend2.dataIdMap.get(mean4.dataId).id,varianceId=backend2.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend2.dataIdMap.get(offset.dataId).id:0,scaleId=scale22!=null?backend2.dataIdMap.get(scale22.dataId).id:0,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend2){wasmFusedConv2d=backend2.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2},wasmFusedDepthwiseConv2d;function setup16(backend2){wasmFusedDepthwiseConv2d=backend2.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend2){wasmGatherNd=backend2.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend2,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend2.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend2.dataIdMap.get(params.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3},wasmGather;function setup18(backend2){wasmGather=backend2.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend2,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape5({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend2}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape5({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend2}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend2.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend2.dataIdMap.get(flattenX.dataId).id,indicesId=backend2.dataIdMap.get(flattenIndex.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23},supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool"),supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool"),wasmFunc3;function setupFunc2(backend2){wasmFunc3=backend2.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend2}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4},supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool"),logConfig3=createUnaryKernelConfig(Log),supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool"),wasmMax;function setup19(backend2){wasmMax=backend2.wasm.cwrap(Max,null,["number, number, number"])}function max5(args){let{backend:backend2,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend2.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max5},supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9),wasmMaxPool;function setup20(backend2){wasmMaxPool=backend2.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMean;function setup21(backend2){wasmMean=backend2.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast5({backend:backend2,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend2.dataIdMap.get(castedInput.dataId).id);let out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend2.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3},wasmMin;function setup22(backend2){wasmMin=backend2.wasm.cwrap(Min,null,["number, number, number"])}function min5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min5},supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10),supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11),negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend2,resOffset){let result=new Int32Array(backend2.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend2.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend2){wasmFunc4=backend2.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);return backend2.wasm._free(pSelectedScores),backend2.wasm._free(pValidOutputs),backend2.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc},wasmFunc5;function setup24(backend2){wasmFunc5=backend2.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pSelectedScores);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend2.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43},wasmFunc6;function setup25(backend2){wasmFunc6=backend2.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pValidOutputs);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend2.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2},supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool"),wasmOneHot;function setup26(backend2){wasmOneHot=backend2.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend2.makeOutput([...indices.shape,depth],"int32"),outId=backend2.dataIdMap.get(out.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims5({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims5({inputs:{input:t},backend:backend2,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend2,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3},wasmPadV2;function setup27(backend2){wasmPadV2=backend2.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend2,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(outShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27},supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13),wasmPrelu;function setup28(backend2){wasmPrelu=backend2.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu5(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,weightsId=backend2.dataIdMap.get(alpha.dataId).id,out=backend2.makeOutput(x.shape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu5},wasmProd;function setup29(backend2){wasmProd=backend2.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4},range5=args=>{let{backend:backend2,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend2.makeOutput([values.length],dtype);return backend2.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range5},supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14),reluConfig3=createUnaryKernelConfig(Relu),relu6Config3=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup30(backend2){wasmResizeBilinear=backend2.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend2,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend2.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend2.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4},wasmReverse;function setup31(backend2){wasmReverse=backend2.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity4({inputs:{x},backend:backend2});let out=backend2.makeOutput(x.shape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape5({inputs:{x:out},attrs:{shape:x.shape},backend:backend2})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31},wasmRotate;function setup32(backend2){wasmRotate=backend2.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend2,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32},rsqrtConfig3=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup33(backend2){wasmScatterNd=backend2.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend2,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend2.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend2.dataIdMap.get(indices.dataId).id,updatesId=backend2.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3},wasmSelect;function setup34(backend2){wasmSelect=backend2.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs,conditionId=backend2.dataIdMap.get(condition.dataId).id,tId=backend2.dataIdMap.get(t.dataId).id,eId=backend2.dataIdMap.get(e.dataId).id,out=backend2.makeOutput(t.shape,t.dtype),outId=backend2.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34},wasmFunc7;function setup35(backend2){wasmFunc7=backend2.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4},sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend2}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend2.readSync(x.dataId),out=backend2.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend2.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend2.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend2.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i<endI;i++){let xOffset=i*xStride+beginJ;outVals.set(xVals.subarray(xOffset,xOffset+size[1]),outOffset),outOffset+=size[1]}}function slice3d2(xVals,xStride1,xStride2,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++){let xOffset=i*xStride1+j*xStride2+beginK;outVals.set(xVals.subarray(xOffset,xOffset+size[2]),outOffset),outOffset+=size[2]}}function slice4d2(xVals,xStride1,xStride2,xStride3,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1],endK=beginK+size[2],beginL=begin[3];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++)for(let k=beginK;k<endK;k++){let xOffset=i*xStride1+j*xStride2+k*xStride3+beginL;outVals.set(xVals.subarray(xOffset,xOffset+size[3]),outOffset),outOffset+=size[3]}}var sliceConfig3={kernelName:Slice,backendName:"wasm",kernelFunc:slice4},wasmFunc8;function setup36(backend2){wasmFunc8=backend2.wasm.cwrap(Softmax,null,["number","number","number","number"])}function softmax5(args){let{backend:backend2,inputs:{logits},attrs:{dim}}=args,xId=backend2.dataIdMap.get(logits.dataId).id,out=backend2.makeOutput(logits.shape,logits.dtype),outId=backend2.dataIdMap.get(out.dataId).id,channels=logits.shape[dim],batch=util_exports.sizeFromShape(logits.shape)/channels;return util_exports.sizeFromShape(out.shape)===0||wasmFunc8(xId,outId,channels,batch),out}var softmaxConfig3={kernelName:Softmax,backendName:"wasm",setupFunc:setup36,kernelFunc:softmax5};function splitV3(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend2});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3},sqrtConfig3=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup37(backend2){wasmStridedSlice=backend2.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape5({inputs:{x},attrs:{shape:newShape},backend:backend2}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend2});return reshape5({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend2})}let out=backend2.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend2.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape5({inputs:{x:out},attrs:{shape:outShape},backend:backend2})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup38(backend2){wasmSum=backend2.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5},tanhConfig3=createUnaryKernelConfig(Tanh),wasmTile;function setup39(backend2){wasmTile=backend2.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile5(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[i]*reps[i];let xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),newShapeBytes=new Uint8Array(new Int32Array(newShape).buffer),out=backend2.makeOutput(newShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmTile(xId,xShapeBytes,x.shape.length,newShapeBytes,newShape.length,CppDType[out.dtype],outId),out}var tileConfig3={kernelName:Tile,backendName:"wasm",setupFunc:setup39,kernelFunc:tile5},wasmTopK;function setup40(backend2){wasmTopK=backend2.wasm.cwrap(TopK,null,["number","array","number","number","number","bool","number","number"])}var topk2=({inputs,backend:backend2,attrs})=>{let{x}=inputs,{k,sorted}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend2.makeOutput(outputShape,x.dtype),outValuesId=backend2.dataIdMap.get(outValues.dataId).id,outIndices=backend2.makeOutput(outputShape,"int32"),outIndicesId=backend2.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i<rank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let outs=new Array(numOutputs),begin=new Array(rank).fill(0),size=value.shape.slice();size[axis]=1;for(let i=0;i<outs.length;i++)begin[axis]=i,outs[i]=slice4({inputs:{x:value},attrs:{begin,size},backend:backend2});return outs.map(({dataId,dtype})=>({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4},kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data=this.dataIdMap.get(dataId);this.wasm._free(data.memoryOffset),this.wasm.tfjs.disposeData(data.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.2",WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);var draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven2,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range6,round:()=>round5});var Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!==0}function isEven2(num){return num%2===0}function round5(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale3=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale3),Math.round(height*scale3))}function getCenterPoint(pts){return pts.reduce((sum6,pt)=>sum6.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range6(num,start,step4){return Array(num).fill(0).map((_,i)=>start+i*step4)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min6,x)=>x<min6?x:min6,Infinity),minY=ys.reduce((min6,y)=>y<min6?y:min6,Infinity),maxX=xs.reduce((max6,x)=>max6<x?x:max6,0),maxY=ys.reduce((max6,y)=>max6<y?y:max6,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression2(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}function normalize(x,meanRgb){return tidy(()=>{let[r,g,b]=meanRgb,avg_r=fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=concat([avg_r,avg_g,avg_b],3);return sub(x,avg_rgb)})}function padToSquare(imgTensor,isCenterImage=!1){return tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>cast(t,"float32"));return concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array2=inputArray.slice();for(let i=array2.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array2[i];array2[i]=array2[j],array2[j]=x}return array2}function sigmoid5(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round5(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json18){let descriptors=json18.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json18.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch3=window.fetch;if(!fetch3)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:fetch3,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer2)=>err?reject(err):resolve(buffer2))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch3=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch:fetch3,...fileSystem}}function isBrowser2(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env3){environment=env3}function initialize(){return isBrowser2()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env3){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env3;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env3.createCanvasElement||(()=>new Canvas),environment.createImageElement=env3.createImageElement||(()=>new Image),environment.ImageData=env3.ImageData||environment.ImageData,environment.Video=env3.Video||environment.Video,environment.fetch=env3.fetch||environment.fetch,environment.readFile=env3.readFile||environment.readFile}var env2={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser:isBrowser2,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env2.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env2.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round5(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}function isMediaLoaded(media){let{Image,Video}=env2.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env2.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env2.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input2){let{Image,Video}=env2.getEnv();return input2 instanceof Image?new Dimensions(input2.naturalWidth,input2.naturalHeight):input2 instanceof Video?new Dimensions(input2.videoWidth,input2.videoHeight):new Dimensions(input2.width,input2.height)}function createCanvas2({width,height}){let{createCanvasElement}=env2.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env2.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas2({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env2.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await browser_exports.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input2){let{Image,Canvas,Video}=env2.getEnv();return input2 instanceof Image||input2 instanceof Canvas||input2 instanceof Video}function imageToSquare(input2,inputSize,centerImage=!1){let{Image,Canvas}=env2.getEnv();if(!(input2 instanceof Image||input2 instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input2),scale3=inputSize/Math.max(dims.height,dims.width),width=scale3*dims.width,height=scale3*dims.height,targetCanvas=createCanvas2({width:inputSize,height:inputSize}),inputCanvas=input2 instanceof Canvas?input2:createCanvasFromMedia(input2),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input2,idx)=>{if(isTensor3D(input2)){this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape;return}if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape.slice(1);return}let canvas=input2 instanceof env2.getEnv().Canvas?input2:createCanvasFromMedia(input2);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range6(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tidy(()=>{let inputTensors=range6(this.batchSize,0,1).map(batchIdx=>{let input2=this.getInput(batchIdx);if(input2 instanceof Tensor){let imgTensor=isTensor4D(input2)?input2:input2.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input2 instanceof env2.getEnv().Canvas)return browser_exports.fromPixels(imageToSquare(input2,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input2}`)});return stack(inputTensors.map(t=>cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input2,i)=>{if(!isMediaElement(input2)&&!isTensor3D(input2)&&!isTensor4D(input2))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input2=>isMediaElement(input2)&&awaitMediaLoaded(input2))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input2,detections){let{Canvas}=env2.getEnv(),canvas=input2;if(!(input2 instanceof Canvas)){let netInput=await toNetInput(input2);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas2({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init2){let{fetch:fetch3}=env2.getEnv(),res=await fetch3(url,init2);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return io_exports.loadWeights(manifest,modelBaseUri)}function matchDimensions(input2,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input2.width=width,input2.height=height,{width,height}}var NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable2})=>{let tensor2=tensor(variable2.dataSync());variable2.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env2.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights2=io_exports.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights2(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}};function depthwiseSeparableConv(x,params,stride){return tidy(()=>{let out=separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=add2(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return relu(add2(out1,add2(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=relu(add2(out1,add2(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return relu(add2(out1,add2(out2,add2(out3,out4))))})}function convLayer(x,params,padding="same",withRelu=!1){return tidy(()=>{let out=add2(conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}};function fullyConnectedLayer(x,params){return tidy(()=>add2(matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}},FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input2){return tidy(()=>softmax(this.runNet(input2)))}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictExpressions(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round5(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version10="0.10.1";function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range6(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range6(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return add2(conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=maxPool(out,[3,3],[2,2],"same"),out=add2(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv2,[1,1]),out=add2(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(256)),out=relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range6(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2,pooled=avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input2){return tidy(()=>{let{age,gender}=this.runNet(input2);return{age,gender:softmax(gender)}})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictAgeAndGender(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),ages=unstack(out.age),genders=unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale3=inputSize/Math.max(height,width);return{width:width*scale3,height:height*scale3}}),batchSize=inputDimensions.length;return tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>stack([fill([68],fillX,"float32"),fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding2=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding2(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding2(batchIdx,(w,h)=>h<w);return output.mul(fill([batchSize,136],inputSize,"float32")).sub(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input2){return tidy(()=>{let out=this.runNet(input2);return this.postProcess(out,input2.inputSize,input2.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async detectLandmarks(input2){let netInput=await toNetInput(input2),landmarkTensors=tidy(()=>unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven2(i)),yCoords=landmarksArray.filter((_,i)=>!isEven2(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};function scale2(x,params){return add2(mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=conv2d(x,filters,strides,padding);return out=add2(out,bias),out=scale2(out,params.scale),withRelu?relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tidy(()=>transpose(tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tensor1d(extractWeights(numWeights)),biases=tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale3=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale3}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tidy(()=>transpose(tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale3=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale3}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=add2(out,x),out=relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=avgPool(x,2,2,"valid"),zeros4=zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=zeros(padShapeX);out=concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=zeros(padShapeY);out=concat([out,zerosH],2)}return pooled=isPad?concat([pooled,zeros4],3):pooled,out=add2(pooled,out),out=relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(256)),out=convDown(normalized,params.conv32_down);out=maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return matMul(globalAvg,params.fc)})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async computeFaceDescriptor(input2){let netInput=await toNetInput(input2),faceDescriptorTensors=tidy(()=>unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tensor1d(extractWeights(numChannels)),batch_norm_offset=tensor1d(extractWeights(numChannels)),batch_norm_mean=tensor1d(extractWeights(numChannels)),batch_norm_variance=tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function pointwiseConvLayer(x,params,strides){return tidy(()=>{let out=conv2d(x,params.filters,strides,"same");return out=add2(out,params.batch_norm_offset),clipByValue(out,0,6)})}var epsilon3=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tidy(()=>{let out=depthwiseConv2d(x,params.filters,strides,"same");return out=batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon3),clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression3(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(!(selected.length>=outputSize)){let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}}),selected}function getCenterCoordinatesAndSizesLayer(x){let vec=unstack(transpose(x,[1,0])),sizes=[sub(vec[2],vec[0]),sub(vec[3],vec[1])],centers=[add2(vec[0],div(sizes[0],scalar(2))),add2(vec[1],div(sizes[1],scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=unstack(transpose(x1,[1,0])),div0_out=div(mul(exp(div(vec[2],scalar(5))),sizes[0]),scalar(2)),add0_out=add2(mul(div(vec[0],scalar(10)),sizes[0]),centers[0]),div1_out=div(mul(exp(div(vec[3],scalar(5))),sizes[1]),scalar(2)),add1_out=add2(mul(div(vec[1],scalar(10)),sizes[1]),centers[1]);return transpose(stack([sub(add0_out,div0_out),sub(add1_out,div1_out),add2(add0_out,div0_out),add2(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(reshape(tile(params.extra_dim,[batchSize,1,1]),[-1,4]),reshape(boxPredictions,[-1,4]));boxes=reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=sigmoid(slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=unstack(boxes),scoresByBatch=unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}function boxPredictionLayer(x,params){return tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input2){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(512,!1),"float32"),x=sub(mul(batchTensor,scalar(.007843137718737125)),scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async locateFaces(input2,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input2),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression3(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME2="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",isNumber2=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber2(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber2(a.x)&&isNumber2(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber2)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}function leaky(x){return tidy(()=>{let min6=mul(x,scalar(.10000000149011612));return add2(relu(sub(x,min6)),min6)})}function convWithBatchNorm(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=conv2d(out,params.conv.filters,[1,1],"valid"),out=sub(out,params.bn.sub),out=mul(out,params.bn.truediv),out=add2(out,params.conv.bias),leaky(out)})}function depthwiseSeparableConv2(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=add2(out,params.bias),leaky(out)})}function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub4=tensor1d(extractWeights(size)),truediv=tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub4,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub4=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub4,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}},TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(x,params){let out=convWithBatchNorm(x,params.conv0);return out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input2,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input2,inputSize){return this.forwardInput(await toNetInput(input2),inputSize)}async detect(input2,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input2),out=await this.forwardInput(netInput,inputSize),out0=tidy(()=>unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression2(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid5(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid5(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid5(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max6,curr)=>max6.classScore>curr.classScore?max6:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){return(await this.detect(input2,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME2}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function extractAllFacesAndComputeResults(parentResults,input2,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input2 instanceof Tensor?await extractFaceTensors(input2,faceBoxes):await extractFaces(input2,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input2,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input2,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){return(await this.detect(input2,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}},nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input2,options)=>nets.ssdMobilenetv1.locateFaces(input2,options),tinyFaceDetector=(input2,options)=>nets.tinyFaceDetector.locateFaces(input2,options),tinyYolov2=(input2,options)=>nets.tinyYolov2.locateFaces(input2,options),detectFaceLandmarks=input2=>nets.faceLandmark68Net.detectLandmarks(input2),detectFaceLandmarksTiny=input2=>nets.faceLandmark68TinyNet.detectLandmarks(input2),computeFaceDescriptor=input2=>nets.faceRecognitionNet.computeFaceDescriptor(input2),recognizeFaceExpressions=input2=>nets.faceExpressionNet.predictExpressions(input2),predictAgeAndGender=input2=>nets.ageGenderNet.predictAgeAndGender(input2),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input2){super();this.parentTask=parentTask;this.input=input2}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input2,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input2;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{detection}=parentResult,faces=this.input instanceof Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input2,options=new SsdMobilenetv1Options){super();this.input=input2;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input:input2,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input3=>nets.tinyFaceDetector.locateFaces(input3,options):options instanceof SsdMobilenetv1Options?input3=>nets.ssdMobilenetv1.locateFaces(input3,options):options instanceof TinyYolov2Options?input3=>nets.tinyYolov2.locateFaces(input3,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input2)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input2,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input2,options)}function detectAllFaces(input2,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input2,options)}async function allFacesSsdMobilenetv1(input2,minConfidence){return detectAllFaces(input2,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input2,forwardParams={}){return detectAllFaces(input2,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count2=1,createUniqueLabel=()=>`person ${count2++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json18){let labeledDescriptors=json18.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json18.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version11={faceapi:version10,node,browser};return src_exports;})();
+    `}};function unsortedSegmentSum3(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape4({inputs:{x:permutedX},backend:backend2,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend2.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range4({backend:backend2,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile4({inputs:{x:rangeInfo},backend:backend2,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape4({inputs:{x:segOpResult},backend:backend2,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend2,attrs:{perm}})}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3},kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var version7="2.8.3",version8={"tfjs-core":version,"tfjs-backend-cpu":version5,"tfjs-backend-webgl":version6,"tfjs-data":version4,"tfjs-layers":version2,"tfjs-converter":version3,tfjs:version7},CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend2){wasmFusedMatMul=backend2.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend2.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend2,inputs}=args,{a,b}=inputs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend2.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend2.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend2){wasmFunc=backend2.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend2}=args,out=backend2.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend2.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity4},wasmTranspose;function setup2(backend2){wasmTranspose=backend2.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend2,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i<perm.length;i++)perm[i]!==i&&(permIsNoOp=!1);let outShape=computeOutShape4(inputs.x.shape,attrs.perm),x={dataId:inputs.x.dataId,shape:reducedShape,dtype:inputs.x.dtype};if(permIsNoOp){let cloned=identity4({inputs,backend:backend2});return cloned.shape=outShape,cloned}let out=backend2.makeOutput(outShape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,permBytes=new Uint8Array(new Int32Array(perm).buffer),xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmTranspose(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],outId,permBytes,perm.length),out}function computeOutShape4(inShape,perm){let outShape=new Array(inShape.length);for(let i=0;i<outShape.length;i++)outShape[i]=inShape[perm[i]];return outShape}function removeOneSizeDims(shape,perm){let newShape=[],newPerm=[];for(let i=0;i<shape.length;++i)shape[i]!==1&&newShape.push(shape[i]),shape[perm[i]]!==1&&newPerm.push(perm[i]);for(let i=0;i<newPerm.length;++i){let minValIdx=-1;for(let j=0;j<newPerm.length;++j)newPerm[j]>=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend2){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xTransposed=transpose4({inputs:{x},attrs:{perm:permutedAxes},backend:backend2});let xId=backend2.dataIdMap.get(x.dataId).id;backend2.dataIdMap.get(xTransposed.dataId).id!==xId&&(inputWasTransposed=!0)}return{transposed:xTransposed,originalAxes,axes,inputWasTransposed}}var wasmFunc2;function setup3(backend2){wasmFunc2=backend2.wasm.cwrap(ArgMax,null,["number","number","number","number","number"])}function argmax(args){let{backend:backend2,inputs,attrs}=args,{axis}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let outShape=input2.shape.slice(0,-1),out=backend2.makeOutput(outShape,"int32"),outId=backend2.dataIdMap.get(out.dataId).id,outerSize=util_exports.sizeFromShape(out.shape),innerSize=input2.shape[axes[0]];return wasmFunc2(inputId,CppDType[input2.dtype],outerSize,innerSize,outId),inputWasTransposed&&backend2.disposeData(transposed.dataId),out}var argMaxConfig3={kernelName:ArgMax,backendName:"wasm",kernelFunc:argmax,setupFunc:setup3},wasmAvgPool;function setup4(backend2){wasmAvgPool=backend2.wasm.cwrap(AvgPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function avgPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,channels=convInfo.inChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);if(convInfo.dilationWidth!==1||convInfo.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmAvgPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,strideHeight,strideWidth,channels,outId),out}var avgPoolConfig3={kernelName:AvgPool,backendName:"wasm",setupFunc:setup4,kernelFunc:avgPool4};function reshape5(args){let{inputs,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize);return util_exports.assert(xSize===util_exports.sizeFromShape($shape),()=>`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape5},wasmBatchMatMul;function setup5(backend2){wasmBatchMatMul=backend2.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape5({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape5({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),a3dId=backend2.dataIdMap.get(a3d.dataId).id,b3dId=backend2.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend2.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast5(args){let{inputs:{x},attrs:{dtype},backend:backend2}=args,out=backend2.makeOutput(x.shape,dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast5},wasmClip;function setup6(backend2){wasmClip=backend2.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend2}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity4({inputs:{x:$inputs[0]},backend:backend2});let out=backend2.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape5({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend2.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend2.typedArrayFromHeap(input2)),outVals=backend2.typedArrayFromHeap(out);for(let b=0;b<batchDim;b++){let outOffset=b*sumInnerDims;for(let i=0;i<inVals.length;i++){let innerDim=innerDims[i],inOffset=b*innerDim,vals=inVals[i].subarray(inOffset,inOffset+innerDim);outVals.set(vals,outOffset),outOffset+=innerDim}}return out}var concatConfig3={kernelName:Concat,backendName:"wasm",kernelFunc:concat4},wasmConv2d;function setup7(backend2){wasmConv2d=backend2.wasm.cwrap(Conv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode,dataFormat}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var conv2DConfig3={kernelName:Conv2D,backendName:"wasm",setupFunc:setup7,kernelFunc:conv2d5},wasmConv2DBackpropInput;function setup8(backend2){wasmConv2DBackpropInput=backend2.wasm.cwrap(Conv2DBackpropInput,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2DBackpropInput4(args){let{backend:backend2,inputs,attrs}=args,{dy,filter}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,inputShape}=attrs,dilations=1,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast",dxStrides=util_exports.computeStrides(convInfo.inShape),dyStrides=util_exports.computeStrides(dy.shape),[fltS0,fltS1,fltS2]=util_exports.computeStrides(filter.shape),xBatchStride=dxStrides[0],xRowStride=isChannelsLast?dxStrides[1]:dxStrides[2],xColStride=isChannelsLast?dxStrides[2]:1,xChannelStride=isChannelsLast?1:dxStrides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1],out=backend2.makeOutput(convInfo.inShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,dyId=backend2.dataIdMap.get(dy.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id;return wasmConv2DBackpropInput(dyId,filterId,batchSize,filterHeight,filterWidth,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,strideHeight,strideWidth,topPad,leftPad,fltS0,fltS1,fltS2,xBatchStride,xRowStride,xColStride,xChannelStride,yBatchStride,yRowStride,yColStride,yChannelStride,outId),out}var conv2DBackpropInputConfig3={kernelName:Conv2DBackpropInput,backendName:"wasm",setupFunc:setup8,kernelFunc:conv2DBackpropInput4},cosConfig3=createUnaryKernelConfig(Cos),InterpolationMethod;(function(InterpolationMethod2){InterpolationMethod2[InterpolationMethod2.bilinear=0]="bilinear",InterpolationMethod2[InterpolationMethod2.nearest=1]="nearest"})(InterpolationMethod||(InterpolationMethod={}));var wasmCropAndResize;function setup9(backend2){wasmCropAndResize=backend2.wasm.cwrap(CropAndResize,null,["number","number","number","number","array","number","number","number","number","number"])}function cropAndResize4(args){let{backend:backend2,inputs,attrs}=args,{method,extrapolationValue,cropSize}=attrs,{image:image3,boxes,boxInd}=inputs,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,outShape=[numBoxes,cropHeight,cropWidth,image3.shape[3]],imagesData=backend2.dataIdMap.get(image3.dataId),castedData;image3.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:image3},attrs:{dtype:"float32"}}),imagesData=backend2.dataIdMap.get(castedData.dataId));let imagesId=imagesData.id,boxesId=backend2.dataIdMap.get(boxes.dataId).id,boxIndId=backend2.dataIdMap.get(boxInd.dataId).id,out=backend2.makeOutput(outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,imagesShapeBytes=new Uint8Array(new Int32Array(image3.shape).buffer);return wasmCropAndResize(imagesId,boxesId,boxIndId,numBoxes,imagesShapeBytes,cropHeight,cropWidth,InterpolationMethod[method],extrapolationValue,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var cropAndResizeConfig3={kernelName:CropAndResize,backendName:"wasm",setupFunc:setup9,kernelFunc:cropAndResize4},wasmCumsum;function setup10(backend2){wasmCumsum=backend2.wasm.cwrap(Cumsum,null,["number","number","number","number","number","number"])}function cumsum4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length;util_exports.assert(x.dtype==="float32"||x.dtype==="int32",()=>`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend2}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend2.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend2.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend2.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend2}),backend2.disposeData(permutedX.dataId),backend2.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4},wasmDepthToSpace;function setup11(backend2){wasmDepthToSpace=backend2.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend2.makeOutput(outputShape,"float32"),xId=backend2.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4},wasmDepthwiseConv2d;function setup12(backend2){wasmDepthwiseConv2d=backend2.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d5},supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool"),expConfig3=createUnaryKernelConfig(Exp);function expandDims5(args){let{inputs,attrs,backend:backend2}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape5({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims5};function fill4(args){let{attrs:{shape,value,dtype},backend:backend2}=args,out=backend2.makeOutput(shape,dtype);return backend2.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4},wasmFlipLeftRight;function setup13(backend2){wasmFlipLeftRight=backend2.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend2}=args,{image:image3}=inputs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},floorConfig3=createUnaryKernelConfig(Floor),supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3),wasmBatchNorm;function setup14(backend2){wasmBatchNorm=backend2.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend2,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale22}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,meanId=backend2.dataIdMap.get(mean4.dataId).id,varianceId=backend2.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend2.dataIdMap.get(offset.dataId).id:0,scaleId=scale22!=null?backend2.dataIdMap.get(scale22.dataId).id:0,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend2){wasmFusedConv2d=backend2.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2},wasmFusedDepthwiseConv2d;function setup16(backend2){wasmFusedDepthwiseConv2d=backend2.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend2){wasmGatherNd=backend2.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend2,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend2.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend2.dataIdMap.get(params.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3},wasmGather;function setup18(backend2){wasmGather=backend2.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend2,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape5({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend2}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape5({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend2}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend2.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend2.dataIdMap.get(flattenX.dataId).id,indicesId=backend2.dataIdMap.get(flattenIndex.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23},supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool"),supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool"),wasmFunc3;function setupFunc2(backend2){wasmFunc3=backend2.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend2}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4},supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool"),logConfig3=createUnaryKernelConfig(Log),supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool"),wasmMax;function setup19(backend2){wasmMax=backend2.wasm.cwrap(Max,null,["number, number, number"])}function max5(args){let{backend:backend2,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend2.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max5},supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9),wasmMaxPool;function setup20(backend2){wasmMaxPool=backend2.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMean;function setup21(backend2){wasmMean=backend2.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast5({backend:backend2,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend2.dataIdMap.get(castedInput.dataId).id);let out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend2.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3},wasmMin;function setup22(backend2){wasmMin=backend2.wasm.cwrap(Min,null,["number, number, number"])}function min5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min5},supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10),supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11),negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend2,resOffset){let result=new Int32Array(backend2.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend2.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend2){wasmFunc4=backend2.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);return backend2.wasm._free(pSelectedScores),backend2.wasm._free(pValidOutputs),backend2.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc},wasmFunc5;function setup24(backend2){wasmFunc5=backend2.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pSelectedScores);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend2.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43},wasmFunc6;function setup25(backend2){wasmFunc6=backend2.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pValidOutputs);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend2.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2},supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool"),wasmOneHot;function setup26(backend2){wasmOneHot=backend2.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend2.makeOutput([...indices.shape,depth],"int32"),outId=backend2.dataIdMap.get(out.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims5({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims5({inputs:{input:t},backend:backend2,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend2,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3},wasmPadV2;function setup27(backend2){wasmPadV2=backend2.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend2,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(outShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27},supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13),wasmPrelu;function setup28(backend2){wasmPrelu=backend2.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu5(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,weightsId=backend2.dataIdMap.get(alpha.dataId).id,out=backend2.makeOutput(x.shape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu5},wasmProd;function setup29(backend2){wasmProd=backend2.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4},range5=args=>{let{backend:backend2,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend2.makeOutput([values.length],dtype);return backend2.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range5},supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14),reluConfig3=createUnaryKernelConfig(Relu),relu6Config3=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup30(backend2){wasmResizeBilinear=backend2.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend2,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend2.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend2.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4},wasmReverse;function setup31(backend2){wasmReverse=backend2.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity4({inputs:{x},backend:backend2});let out=backend2.makeOutput(x.shape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape5({inputs:{x:out},attrs:{shape:x.shape},backend:backend2})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31},wasmRotate;function setup32(backend2){wasmRotate=backend2.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend2,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32},roundConfig3=createUnaryKernelConfig(Round),rsqrtConfig3=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup33(backend2){wasmScatterNd=backend2.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend2,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend2.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend2.dataIdMap.get(indices.dataId).id,updatesId=backend2.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3},wasmSelect;function setup34(backend2){wasmSelect=backend2.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs,conditionId=backend2.dataIdMap.get(condition.dataId).id,tId=backend2.dataIdMap.get(t.dataId).id,eId=backend2.dataIdMap.get(e.dataId).id,out=backend2.makeOutput(t.shape,t.dtype),outId=backend2.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34},wasmFunc7;function setup35(backend2){wasmFunc7=backend2.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4},sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend2}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend2.readSync(x.dataId),out=backend2.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend2.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend2.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend2.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i<endI;i++){let xOffset=i*xStride+beginJ;outVals.set(xVals.subarray(xOffset,xOffset+size[1]),outOffset),outOffset+=size[1]}}function slice3d2(xVals,xStride1,xStride2,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++){let xOffset=i*xStride1+j*xStride2+beginK;outVals.set(xVals.subarray(xOffset,xOffset+size[2]),outOffset),outOffset+=size[2]}}function slice4d2(xVals,xStride1,xStride2,xStride3,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1],endK=beginK+size[2],beginL=begin[3];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++)for(let k=beginK;k<endK;k++){let xOffset=i*xStride1+j*xStride2+k*xStride3+beginL;outVals.set(xVals.subarray(xOffset,xOffset+size[3]),outOffset),outOffset+=size[3]}}var sliceConfig3={kernelName:Slice,backendName:"wasm",kernelFunc:slice4},wasmFunc8;function setup36(backend2){wasmFunc8=backend2.wasm.cwrap(Softmax,null,["number","number","number","number"])}function softmax5(args){let{backend:backend2,inputs:{logits},attrs:{dim}}=args,xId=backend2.dataIdMap.get(logits.dataId).id,out=backend2.makeOutput(logits.shape,logits.dtype),outId=backend2.dataIdMap.get(out.dataId).id,channels=logits.shape[dim],batch=util_exports.sizeFromShape(logits.shape)/channels;return util_exports.sizeFromShape(out.shape)===0||wasmFunc8(xId,outId,channels,batch),out}var softmaxConfig3={kernelName:Softmax,backendName:"wasm",setupFunc:setup36,kernelFunc:softmax5};function splitV3(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend2});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3},sqrtConfig3=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup37(backend2){wasmStridedSlice=backend2.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape5({inputs:{x},attrs:{shape:newShape},backend:backend2}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend2});return reshape5({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend2})}let out=backend2.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend2.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape5({inputs:{x:out},attrs:{shape:outShape},backend:backend2})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup38(backend2){wasmSum=backend2.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5},tanhConfig3=createUnaryKernelConfig(Tanh),wasmTile;function setup39(backend2){wasmTile=backend2.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile5(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[i]*reps[i];let xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),newShapeBytes=new Uint8Array(new Int32Array(newShape).buffer),out=backend2.makeOutput(newShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmTile(xId,xShapeBytes,x.shape.length,newShapeBytes,newShape.length,CppDType[out.dtype],outId),out}var tileConfig3={kernelName:Tile,backendName:"wasm",setupFunc:setup39,kernelFunc:tile5},wasmTopK;function setup40(backend2){wasmTopK=backend2.wasm.cwrap(TopK,null,["number","array","number","number","number","bool","number","number"])}var topk2=({inputs,backend:backend2,attrs})=>{let{x}=inputs,{k,sorted}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend2.makeOutput(outputShape,x.dtype),outValuesId=backend2.dataIdMap.get(outValues.dataId).id,outIndices=backend2.makeOutput(outputShape,"int32"),outIndicesId=backend2.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i<rank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let outs=new Array(numOutputs),begin=new Array(rank).fill(0),size=value.shape.slice();size[axis]=1;for(let i=0;i<outs.length;i++)begin[axis]=i,outs[i]=slice4({inputs:{x:value},attrs:{begin,size},backend:backend2});return outs.map(({dataId,dtype})=>({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4},kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,roundConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data=this.dataIdMap.get(dataId);this.wasm._free(data.memoryOffset),this.wasm.tfjs.disposeData(data.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.3",WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);var draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven2,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range6,round:()=>round5});var Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!=0}function isEven2(num){return num%2==0}function round5(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale3=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale3),Math.round(height*scale3))}function getCenterPoint(pts){return pts.reduce((sum6,pt)=>sum6.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range6(num,start,step4){return Array(num).fill(0).map((_,i)=>start+i*step4)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}};var Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}};var BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}};var FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min6,x)=>x<min6?x:min6,Infinity),minY=ys.reduce((min6,y)=>y<min6?y:min6,Infinity),maxX=xs.reduce((max6,x)=>max6<x?x:max6,0),maxY=ys.reduce((max6,y)=>max6<y?y:max6,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression2(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}function normalize(x,meanRgb){return tidy(()=>{let[r,g,b]=meanRgb,avg_r=fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=concat([avg_r,avg_g,avg_b],3);return sub(x,avg_rgb)})}function padToSquare(imgTensor,isCenterImage=!1){return tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>cast(t,"float32"));return concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array2=inputArray.slice();for(let i=array2.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array2[i];array2[i]=array2[j],array2[j]=x}return array2}function sigmoid5(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}};var relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round5(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json18){let descriptors=json18.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json18.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch3=window.fetch;if(!fetch3)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:fetch3,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer2)=>err?reject(err):resolve(buffer2))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch3=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch:fetch3,...fileSystem}}function isBrowser2(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env3){environment=env3}function initialize(){return isBrowser2()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env3){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env3;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env3.createCanvasElement||(()=>new Canvas),environment.createImageElement=env3.createImageElement||(()=>new Image),environment.ImageData=env3.ImageData||environment.ImageData,environment.Video=env3.Video||environment.Video,environment.fetch=env3.fetch||environment.fetch,environment.readFile=env3.readFile||environment.readFile}var env2={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser:isBrowser2,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env2.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env2.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}};var DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round5(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}function isMediaLoaded(media){let{Image,Video}=env2.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env2.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env2.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input2){let{Image,Video}=env2.getEnv();return input2 instanceof Image?new Dimensions(input2.naturalWidth,input2.naturalHeight):input2 instanceof Video?new Dimensions(input2.videoWidth,input2.videoHeight):new Dimensions(input2.width,input2.height)}function createCanvas2({width,height}){let{createCanvasElement}=env2.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env2.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas2({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env2.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await browser_exports.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input2){let{Image,Canvas,Video}=env2.getEnv();return input2 instanceof Image||input2 instanceof Canvas||input2 instanceof Video}function imageToSquare(input2,inputSize,centerImage=!1){let{Image,Canvas}=env2.getEnv();if(!(input2 instanceof Image||input2 instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input2),scale3=inputSize/Math.max(dims.height,dims.width),width=scale3*dims.width,height=scale3*dims.height,targetCanvas=createCanvas2({width:inputSize,height:inputSize}),inputCanvas=input2 instanceof Canvas?input2:createCanvasFromMedia(input2),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input2,idx)=>{if(isTensor3D(input2)){this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape;return}if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape.slice(1);return}let canvas=input2 instanceof env2.getEnv().Canvas?input2:createCanvasFromMedia(input2);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range6(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tidy(()=>{let inputTensors=range6(this.batchSize,0,1).map(batchIdx=>{let input2=this.getInput(batchIdx);if(input2 instanceof Tensor){let imgTensor=isTensor4D(input2)?input2:input2.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input2 instanceof env2.getEnv().Canvas)return browser_exports.fromPixels(imageToSquare(input2,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input2}`)});return stack(inputTensors.map(t=>cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input2,i)=>{if(!isMediaElement(input2)&&!isTensor3D(input2)&&!isTensor4D(input2))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input2=>isMediaElement(input2)&&awaitMediaLoaded(input2))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input2,detections){let{Canvas}=env2.getEnv(),canvas=input2;if(!(input2 instanceof Canvas)){let netInput=await toNetInput(input2);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas2({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init2){let{fetch:fetch3}=env2.getEnv(),res=await fetch3(url,init2);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return io_exports.loadWeights(manifest,modelBaseUri)}function matchDimensions(input2,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input2.width=width,input2.height=height,{width,height}}var NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable2})=>{let tensor2=tensor(variable2.dataSync());variable2.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env2.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights2=io_exports.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights2(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}};function depthwiseSeparableConv(x,params,stride){return tidy(()=>{let out=separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=add2(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return relu(add2(out1,add2(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=relu(add2(out1,add2(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return relu(add2(out1,add2(out2,add2(out3,out4))))})}function convLayer(x,params,padding="same",withRelu=!1){return tidy(()=>{let out=add2(conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}};function fullyConnectedLayer(x,params){return tidy(()=>add2(matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}};var FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input2){return tidy(()=>softmax(this.runNet(input2)))}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictExpressions(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round5(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version10="0.10.2";function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range6(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range6(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return add2(conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=maxPool(out,[3,3],[2,2],"same"),out=add2(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv2,[1,1]),out=add2(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(256)),out=relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range6(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2,pooled=avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input2){return tidy(()=>{let{age,gender}=this.runNet(input2);return{age,gender:softmax(gender)}})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictAgeAndGender(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),ages=unstack(out.age),genders=unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale3=inputSize/Math.max(height,width);return{width:width*scale3,height:height*scale3}}),batchSize=inputDimensions.length;return tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>stack([fill([68],fillX,"float32"),fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding2=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding2(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding2(batchIdx,(w,h)=>h<w);return output.mul(fill([batchSize,136],inputSize,"float32")).sub(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input2){return tidy(()=>{let out=this.runNet(input2);return this.postProcess(out,input2.inputSize,input2.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async detectLandmarks(input2){let netInput=await toNetInput(input2),landmarkTensors=tidy(()=>unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven2(i)),yCoords=landmarksArray.filter((_,i)=>!isEven2(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}};var FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}};var FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var FaceLandmarkNet=class extends FaceLandmark68Net{};function scale2(x,params){return add2(mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=conv2d(x,filters,strides,padding);return out=add2(out,bias),out=scale2(out,params.scale),withRelu?relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tidy(()=>transpose(tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tensor1d(extractWeights(numWeights)),biases=tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale3=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale3}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tidy(()=>transpose(tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale3=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale3}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=add2(out,x),out=relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=avgPool(x,2,2,"valid"),zeros4=zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=zeros(padShapeX);out=concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=zeros(padShapeY);out=concat([out,zerosH],2)}return pooled=isPad?concat([pooled,zeros4],3):pooled,out=add2(pooled,out),out=relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(scalar(256)),out=convDown(normalized,params.conv32_down);out=maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return matMul(globalAvg,params.fc)})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async computeFaceDescriptor(input2){let netInput=await toNetInput(input2),faceDescriptorTensors=tidy(()=>unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tensor1d(extractWeights(numChannels)),batch_norm_offset=tensor1d(extractWeights(numChannels)),batch_norm_mean=tensor1d(extractWeights(numChannels)),batch_norm_variance=tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function pointwiseConvLayer(x,params,strides){return tidy(()=>{let out=conv2d(x,params.filters,strides,"same");return out=add2(out,params.batch_norm_offset),clipByValue(out,0,6)})}var epsilon3=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tidy(()=>{let out=depthwiseConv2d(x,params.filters,strides,"same");return out=batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon3),clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression3(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(selected.length>=outputSize)return;let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}),selected}function getCenterCoordinatesAndSizesLayer(x){let vec=unstack(transpose(x,[1,0])),sizes=[sub(vec[2],vec[0]),sub(vec[3],vec[1])],centers=[add2(vec[0],div(sizes[0],scalar(2))),add2(vec[1],div(sizes[1],scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=unstack(transpose(x1,[1,0])),div0_out=div(mul(exp(div(vec[2],scalar(5))),sizes[0]),scalar(2)),add0_out=add2(mul(div(vec[0],scalar(10)),sizes[0]),centers[0]),div1_out=div(mul(exp(div(vec[3],scalar(5))),sizes[1]),scalar(2)),add1_out=add2(mul(div(vec[1],scalar(10)),sizes[1]),centers[1]);return transpose(stack([sub(add0_out,div0_out),sub(add1_out,div1_out),add2(add0_out,div0_out),add2(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(reshape(tile(params.extra_dim,[batchSize,1,1]),[-1,4]),reshape(boxPredictions,[-1,4]));boxes=reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=sigmoid(slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=unstack(boxes),scoresByBatch=unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}function boxPredictionLayer(x,params){return tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input2){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(512,!1),"float32"),x=sub(mul(batchTensor,scalar(.007843137718737125)),scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async locateFaces(input2,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input2),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression3(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{};var IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME2="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model";var isNumber2=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber2(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber2(a.x)&&isNumber2(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber2)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}function leaky(x){return tidy(()=>{let min6=mul(x,scalar(.10000000149011612));return add2(relu(sub(x,min6)),min6)})}function convWithBatchNorm(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=conv2d(out,params.conv.filters,[1,1],"valid"),out=sub(out,params.bn.sub),out=mul(out,params.bn.truediv),out=add2(out,params.conv.bias),leaky(out)})}function depthwiseSeparableConv2(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=add2(out,params.bias),leaky(out)})}function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub4=tensor1d(extractWeights(size)),truediv=tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub4,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub4=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub4,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(x,params){let out=convWithBatchNorm(x,params.conv0);return out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input2,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input2,inputSize){return this.forwardInput(await toNetInput(input2),inputSize)}async detect(input2,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input2),out=await this.forwardInput(netInput,inputSize),out0=tidy(()=>unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression2(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid5(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid5(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid5(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max6,curr)=>max6.classScore>curr.classScore?max6:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){return(await this.detect(input2,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME2}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function extractAllFacesAndComputeResults(parentResults,input2,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input2 instanceof Tensor?await extractFaceTensors(input2,faceBoxes):await extractFaces(input2,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input2,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input2,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404];var TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){return(await this.detect(input2,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};var nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input2,options)=>nets.ssdMobilenetv1.locateFaces(input2,options),tinyFaceDetector=(input2,options)=>nets.tinyFaceDetector.locateFaces(input2,options),tinyYolov2=(input2,options)=>nets.tinyYolov2.locateFaces(input2,options),detectFaceLandmarks=input2=>nets.faceLandmark68Net.detectLandmarks(input2),detectFaceLandmarksTiny=input2=>nets.faceLandmark68TinyNet.detectLandmarks(input2),computeFaceDescriptor=input2=>nets.faceRecognitionNet.computeFaceDescriptor(input2),recognizeFaceExpressions=input2=>nets.faceExpressionNet.predictExpressions(input2),predictAgeAndGender=input2=>nets.ageGenderNet.predictAgeAndGender(input2),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks;var PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input2){super();this.parentTask=parentTask;this.input=input2}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}};var DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input2,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input2;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{detection}=parentResult,faces=this.input instanceof Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var DetectFacesTaskBase=class extends ComposableTask{constructor(input2,options=new SsdMobilenetv1Options){super();this.input=input2;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input:input2,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input3=>nets.tinyFaceDetector.locateFaces(input3,options):options instanceof SsdMobilenetv1Options?input3=>nets.ssdMobilenetv1.locateFaces(input3,options):options instanceof TinyYolov2Options?input3=>nets.tinyYolov2.locateFaces(input3,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input2)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input2,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input2,options)}function detectAllFaces(input2,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input2,options)}async function allFacesSsdMobilenetv1(input2,minConfidence){return detectAllFaces(input2,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input2,forwardParams={}){return detectAllFaces(input2,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count2=1,createUniqueLabel=()=>`person ${count2++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json18){let labeledDescriptors=json18.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json18.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version11={faceapi:version10,node,browser};return src_exports;})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -4203,5 +4203,21 @@ return a / b;`,DIV_PACKED=`
  * limitations under the License.
  * =============================================================================
  */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
 /** @license See the LICENSE file. */
 //# sourceMappingURL=face-api.js.map
diff --git a/dist/face-api.js.map b/dist/face-api.js.map
index a8dc210..d9746c9 100644
--- a/dist/face-api.js.map
+++ b/dist/face-api.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["../src/env/isNodejs.ts", "../src/index.ts", "empty:/home/vlado/dev/face-api/node_modules/node-fetch/browser.js", "../node_modules/seedrandom/lib/alea.js", "../node_modules/seedrandom/lib/xor128.js", "../node_modules/seedrandom/lib/xorwow.js", "../node_modules/seedrandom/lib/xorshift7.js", "../node_modules/seedrandom/lib/xor4096.js", "../node_modules/seedrandom/lib/tychei.js", "empty:crypto", "../node_modules/seedrandom/seedrandom.js", "../node_modules/seedrandom/index.js", "empty:/home/vlado/dev/face-api/node_modules/string_decoder/lib/string_decoder.js", "empty:path", "empty:worker_threads", "empty:perf_hooks", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js", "../node_modules/@tensorflow/tfjs-core/src/backends/backend.ts", "../node_modules/@tensorflow/tfjs-core/src/util_base.ts", "../node_modules/@tensorflow/tfjs-core/src/environment.ts", "../node_modules/@tensorflow/tfjs-core/src/global_util.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_names.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/util.ts", "../node_modules/@tensorflow/tfjs-core/src/profiler.ts", "../node_modules/@tensorflow/tfjs-core/src/tape.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_format.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util.ts", "../node_modules/@tensorflow/tfjs-core/src/types.ts", "../node_modules/@tensorflow/tfjs-core/src/engine.ts", "../node_modules/@tensorflow/tfjs-core/src/device_util.ts", "../node_modules/@tensorflow/tfjs-core/src/flags.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util_env.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/operation.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/complex.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor_ops_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/io/types.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io_utils.ts", "../node_modules/@tensorflow/tfjs-core/src/io/router_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/io/indexed_db.ts", "../node_modules/@tensorflow/tfjs-core/src/io/local_storage.ts", "../node_modules/@tensorflow/tfjs-core/src/io/model_management.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_browser.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_node.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/buffer.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cast.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clone.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/print.ts", "../node_modules/@tensorflow/tfjs-core/src/base_side_effects.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io.ts", "../node_modules/@tensorflow/tfjs-core/src/io/browser_files.ts", "../node_modules/@tensorflow/tfjs-core/src/io/progress.ts", "../node_modules/@tensorflow/tfjs-core/src/io/weights_loader.ts", "../node_modules/@tensorflow/tfjs-core/src/io/http.ts", "../node_modules/@tensorflow/tfjs-core/src/io/passthrough.ts", "../node_modules/@tensorflow/tfjs-core/src/math.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mat_mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/one_hot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/confusion_matrix.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/browser.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/scatter_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice_util.ts", "../node_modules/@tensorflow/tfjs-core/src/serialization.ts", "../node_modules/@tensorflow/tfjs-core/src/test_util.ts", "../node_modules/@tensorflow/tfjs-core/src/version.ts", "../node_modules/@tensorflow/tfjs-core/src/globals.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floorDiv.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/abs.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add_n.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/all.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/any.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_max.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_min.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asin.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asinh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan2.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/reshape.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/basic_lstm_cell.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batch_to_space_nd.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm4d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/bincount.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/broadcast_to.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/ceil.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clip_by_value.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_1d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_4d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv1d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d_backprop_input.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d_transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d_backprop_input.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d_transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cumsum.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dense_bincount.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/depth_to_space.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/depthwise_conv2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/diag.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dilation2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/broadcast_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/where.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/zeros_like.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div_no_nan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/elu.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/erf.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/exp.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/expand_dims.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/expm1.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tile.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/eye.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/fill.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floor.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/greater.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/greater_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/imag.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_finite.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_inf.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_nan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/leaky_relu.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/less.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/less_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/linspace.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/local_response_normalization.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log1p.ts", "../node_modules/@tensorflow/tfjs-core/src/gradients.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/neg.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/softplus.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log_sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/max.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sub.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sum.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log_softmax.ts", 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-  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\n// eslint-disable-next-line no-undef\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var _scriptDir=undefined;var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":167,\"maximum\":167+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 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stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"vb\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"wb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"xb\"]).apply(null,arguments)};var dynCall_vi=Module[\"dynCall_vi\"]=function(){return(dynCall_vi=Module[\"dynCall_vi\"]=Module[\"asm\"][\"yb\"]).apply(null,arguments)};var dynCall_v=Module[\"dynCall_v\"]=function(){return(dynCall_v=Module[\"dynCall_v\"]=Module[\"asm\"][\"zb\"]).apply(null,arguments)};var dynCall_ii=Module[\"dynCall_ii\"]=function(){return(dynCall_ii=Module[\"dynCall_ii\"]=Module[\"asm\"][\"Ab\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"_pthread_self\"]=_pthread_self;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(!ENVIRONMENT_IS_PTHREAD)noExitRuntime=true;if(!ENVIRONMENT_IS_PTHREAD)run();\n\n\n  return WasmBackendModuleThreadedSimd\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModuleThreadedSimd;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModuleThreadedSimd; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n    ", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":149,\"maximum\":149+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Floor\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"LeakyRelu\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"Mean\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"Neg\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Prod\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"RealDiv\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"TopK\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  // eslint-disable-next-line no-mixed-operators\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n\n    const { readFile } = env.getEnv();\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)),\n    );\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParamsFromWeigthMap(weightMap);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParams(weights);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input);\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      (t) => t.data(),\n    )) as Float32Array[];\n\n    faceDescriptorTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) {\n      return;\n    }\n    const originalScore = c.score;\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n\n    return {\n      boxPredictionEncoding,\n      classPrediction,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    // TODO batches\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data());\n\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n    // return tf.maximum(x, min)\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024,\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config';\nexport * from './types';\n\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n"],
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"../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts"],
+  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\n// eslint-disable-next-line no-undef\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename 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if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":169,\"maximum\":169+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"Ba\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Ca\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var 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_Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sa\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Ta\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Ua\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Va\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Wa\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Xa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ya\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Za\"]).apply(null,arguments)};var 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_emscripten_sync_run_in_main_thread_5=Module[\"_emscripten_sync_run_in_main_thread_5\"]=function(){return(_emscripten_sync_run_in_main_thread_5=Module[\"_emscripten_sync_run_in_main_thread_5\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_6=Module[\"_emscripten_sync_run_in_main_thread_6\"]=function(){return(_emscripten_sync_run_in_main_thread_6=Module[\"_emscripten_sync_run_in_main_thread_6\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_7=Module[\"_emscripten_sync_run_in_main_thread_7\"]=function(){return(_emscripten_sync_run_in_main_thread_7=Module[\"_emscripten_sync_run_in_main_thread_7\"]=Module[\"asm\"][\"sb\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"tb\"]).apply(null,arguments)};var _emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=function(){return(_emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=Module[\"asm\"][\"ub\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"vb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"wb\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"xb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"yb\"]).apply(null,arguments)};var dynCall_vi=Module[\"dynCall_vi\"]=function(){return(dynCall_vi=Module[\"dynCall_vi\"]=Module[\"asm\"][\"zb\"]).apply(null,arguments)};var dynCall_v=Module[\"dynCall_v\"]=function(){return(dynCall_v=Module[\"dynCall_v\"]=Module[\"asm\"][\"Ab\"]).apply(null,arguments)};var dynCall_ii=Module[\"dynCall_ii\"]=function(){return(dynCall_ii=Module[\"dynCall_ii\"]=Module[\"asm\"][\"Bb\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"_pthread_self\"]=_pthread_self;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(!ENVIRONMENT_IS_PTHREAD)noExitRuntime=true;if(!ENVIRONMENT_IS_PTHREAD)run();\n\n\n  return WasmBackendModuleThreadedSimd\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModuleThreadedSimd;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModuleThreadedSimd; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n    ", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new 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assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function 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callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  // eslint-disable-next-line no-mixed-operators\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n\n    const { readFile } = env.getEnv();\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)),\n    );\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParamsFromWeigthMap(weightMap);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParams(weights);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input);\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      (t) => t.data(),\n    )) as Float32Array[];\n\n    faceDescriptorTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) {\n      return;\n    }\n    const originalScore = c.score;\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n\n    return {\n      boxPredictionEncoding,\n      classPrediction,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    // TODO batches\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data());\n\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n    // return tf.maximum(x, min)\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024,\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config';\nexport * from './types';\n\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n"],
+  "mappings": 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diff --git a/dist/face-api.json b/dist/face-api.json
index 59189c6..95fbfb5 100644
--- a/dist/face-api.json
+++ b/dist/face-api.json
@@ -1,7 +1,7 @@
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diff --git a/dist/face-api.node-gpu.js b/dist/face-api.node-gpu.js
index 9daa346..3436fc5 100644
--- a/dist/face-api.node-gpu.js
+++ b/dist/face-api.node-gpu.js
@@ -5,5 +5,5 @@
   author: <https://github.com/vladmandic>'
   */
 
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target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2),require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node-gpu")))}),require_isNodejs=__commonJS((exports2,module2)=>{__export(exports2,{isNodejs:()=>isNodejs2});function isNodejs2(){return typeof global=="object"&&!0&&typeof module2!="undefined"&&typeof process!="undefined"&&!!process.version}});__export(exports,{AgeGenderNet:()=>AgeGenderNet,BoundingBox:()=>BoundingBox,Box:()=>Box,ComposableTask:()=>ComposableTask,ComputeAllFaceDescriptorsTask:()=>ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase:()=>ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask:()=>ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask:()=>DetectAllFaceLandmarksTask,DetectAllFacesTask:()=>DetectAllFacesTask,DetectFaceLandmarksTaskBase:()=>DetectFaceLandmarksTaskBase,DetectFacesTaskBase:()=>DetectFacesTaskBase,DetectSingleFaceLandmarksTask:()=>DetectSingleFaceLandmarksTask,DetectSingleFaceTask:()=>DetectSingleFaceTask,Dimensions:()=>Dimensions,FACE_EXPRESSION_LABELS:()=>FACE_EXPRESSION_LABELS,FaceDetection:()=>FaceDetection,FaceDetectionNet:()=>FaceDetectionNet,FaceExpressionNet:()=>FaceExpressionNet,FaceExpressions:()=>FaceExpressions,FaceLandmark68Net:()=>FaceLandmark68Net,FaceLandmark68TinyNet:()=>FaceLandmark68TinyNet,FaceLandmarkNet:()=>FaceLandmarkNet,FaceLandmarks:()=>FaceLandmarks,FaceLandmarks5:()=>FaceLandmarks5,FaceLandmarks68:()=>FaceLandmarks68,FaceMatch:()=>FaceMatch,FaceMatcher:()=>FaceMatcher,FaceRecognitionNet:()=>FaceRecognitionNet,Gender:()=>Gender,LabeledBox:()=>LabeledBox,LabeledFaceDescriptors:()=>LabeledFaceDescriptors,NetInput:()=>NetInput,NeuralNetwork:()=>NeuralNetwork,ObjectDetection:()=>ObjectDetection,Point:()=>Point,PredictedBox:()=>PredictedBox,Rect:()=>Rect,SsdMobilenetv1:()=>SsdMobilenetv1,SsdMobilenetv1Options:()=>SsdMobilenetv1Options,TinyFaceDetector:()=>TinyFaceDetector,TinyFaceDetectorOptions:()=>TinyFaceDetectorOptions,TinyYolov2:()=>TinyYolov2,TinyYolov2Options:()=>TinyYolov2Options,allFaces:()=>allFaces,allFacesSsdMobilenetv1:()=>allFacesSsdMobilenetv1,allFacesTinyYolov2:()=>allFacesTinyYolov2,awaitMediaLoaded:()=>awaitMediaLoaded,bufferToImage:()=>bufferToImage,computeFaceDescriptor:()=>computeFaceDescriptor,createCanvas:()=>createCanvas,createCanvasFromMedia:()=>createCanvasFromMedia,createFaceDetectionNet:()=>createFaceDetectionNet,createFaceRecognitionNet:()=>createFaceRecognitionNet,createSsdMobilenetv1:()=>createSsdMobilenetv1,createTinyFaceDetector:()=>createTinyFaceDetector,createTinyYolov2:()=>createTinyYolov2,detectAllFaces:()=>detectAllFaces,detectFaceLandmarks:()=>detectFaceLandmarks,detectFaceLandmarksTiny:()=>detectFaceLandmarksTiny,detectLandmarks:()=>detectLandmarks,detectSingleFace:()=>detectSingleFace,draw:()=>draw_exports,env:()=>env,euclideanDistance:()=>euclideanDistance,extendWithAge:()=>extendWithAge,extendWithFaceDescriptor:()=>extendWithFaceDescriptor,extendWithFaceDetection:()=>extendWithFaceDetection,extendWithFaceExpressions:()=>extendWithFaceExpressions,extendWithFaceLandmarks:()=>extendWithFaceLandmarks,extendWithGender:()=>extendWithGender,extractFaceTensors:()=>extractFaceTensors,extractFaces:()=>extractFaces,fetchImage:()=>fetchImage,fetchJson:()=>fetchJson,fetchNetWeights:()=>fetchNetWeights,fetchOrThrow:()=>fetchOrThrow,getContext2dOrThrow:()=>getContext2dOrThrow,getMediaDimensions:()=>getMediaDimensions,imageTensorToCanvas:()=>imageTensorToCanvas,imageToSquare:()=>imageToSquare,inverseSigmoid:()=>inverseSigmoid,iou:()=>iou,isMediaElement:()=>isMediaElement,isMediaLoaded:()=>isMediaLoaded,isWithAge:()=>isWithAge,isWithFaceDetection:()=>isWithFaceDetection,isWithFaceExpressions:()=>isWithFaceExpressions,isWithFaceLandmarks:()=>isWithFaceLandmarks,isWithGender:()=>isWithGender,loadAgeGenderModel:()=>loadAgeGenderModel,loadFaceDetectionModel:()=>loadFaceDetectionModel,loadFaceExpressionModel:()=>loadFaceExpressionModel,loadFaceLandmarkModel:()=>loadFaceLandmarkModel,loadFaceLandmarkTinyModel:()=>loadFaceLandmarkTinyModel,loadFaceRecognitionModel:()=>loadFaceRecognitionModel,loadSsdMobilenetv1Model:()=>loadSsdMobilenetv1Model,loadTinyFaceDetectorModel:()=>loadTinyFaceDetectorModel,loadTinyYolov2Model:()=>loadTinyYolov2Model,loadWeightMap:()=>loadWeightMap,locateFaces:()=>locateFaces,matchDimensions:()=>matchDimensions,minBbox:()=>minBbox,nets:()=>nets,nonMaxSuppression:()=>nonMaxSuppression,normalize:()=>normalize,padToSquare:()=>padToSquare,predictAgeAndGender:()=>predictAgeAndGender,recognizeFaceExpressions:()=>recognizeFaceExpressions,resizeResults:()=>resizeResults,resolveInput:()=>resolveInput,shuffleArray:()=>shuffleArray,sigmoid:()=>sigmoid,ssdMobilenetv1:()=>ssdMobilenetv1,tf:()=>tf42,tinyFaceDetector:()=>tinyFaceDetector,tinyYolov2:()=>tinyYolov2,toNetInput:()=>toNetInput,utils:()=>utils_exports,validateConfig:()=>validateConfig,version:()=>version2});var tf42=__toModule(require_tfjs_esm()),draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range,round:()=>round});var tf=__toModule(require_tfjs_esm()),Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof tf.Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!==0}function isEven(num){return num%2===0}function round(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale2=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale2),Math.round(height*scale2))}function getCenterPoint(pts){return pts.reduce((sum,pt)=>sum.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range(num,start,step){return Array(num).fill(0).map((_,i)=>start+i*step)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min,x)=>x<min?x:min,Infinity),minY=ys.reduce((min,y)=>y<min?y:min,Infinity),maxX=xs.reduce((max,x)=>max<x?x:max,0),maxY=ys.reduce((max,y)=>max<y?y:max,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}var tf2=__toModule(require_tfjs_esm());function normalize(x,meanRgb){return tf2.tidy(()=>{let[r,g,b]=meanRgb,avg_r=tf2.fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=tf2.fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=tf2.fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=tf2.concat([avg_r,avg_g,avg_b],3);return tf2.sub(x,avg_rgb)})}var tf3=__toModule(require_tfjs_esm());function padToSquare(imgTensor,isCenterImage=!1){return tf3.tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,tf3.fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>tf3.cast(t,"float32"));return tf3.concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array=inputArray.slice();for(let i=array.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array[i];array[i]=array[j],array[j]=x}return array}function sigmoid(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json){let descriptors=json.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch=window.fetch;if(!fetch)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer)=>err?reject(err):resolve(buffer))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch,...fileSystem}}function isBrowser(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env2){environment=env2}function initialize(){return isBrowser()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env2){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env2;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env2.createCanvasElement||(()=>new Canvas),environment.createImageElement=env2.createImageElement||(()=>new Image),environment.ImageData=env2.ImageData||environment.ImageData,environment.Video=env2.Video||environment.Video,environment.fetch=env2.fetch||environment.fetch,environment.readFile=env2.readFile||environment.readFile}var env={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}var tf18=__toModule(require_tfjs_esm());function isMediaLoaded(media){let{Image,Video}=env.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input){let{Image,Video}=env.getEnv();return input instanceof Image?new Dimensions(input.naturalWidth,input.naturalHeight):input instanceof Video?new Dimensions(input.videoWidth,input.videoHeight):new Dimensions(input.width,input.height)}function createCanvas({width,height}){let{createCanvasElement}=env.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}var tf4=__toModule(require_tfjs_esm());async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tf4.tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await tf4.browser.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input){let{Image,Canvas,Video}=env.getEnv();return input instanceof Image||input instanceof Canvas||input instanceof Video}var tf5=__toModule(require_tfjs_esm());function imageToSquare(input,inputSize,centerImage=!1){let{Image,Canvas}=env.getEnv();if(!(input instanceof Image||input instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input),scale2=inputSize/Math.max(dims.height,dims.width),width=scale2*dims.width,height=scale2*dims.height,targetCanvas=createCanvas({width:inputSize,height:inputSize}),inputCanvas=input instanceof Canvas?input:createCanvasFromMedia(input),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input,idx)=>{if(isTensor3D(input)){this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape;return}if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape.slice(1);return}let canvas=input instanceof env.getEnv().Canvas?input:createCanvasFromMedia(input);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tf5.tidy(()=>{let inputTensors=range(this.batchSize,0,1).map(batchIdx=>{let input=this.getInput(batchIdx);if(input instanceof tf5.Tensor){let imgTensor=isTensor4D(input)?input:input.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=tf5.image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input instanceof env.getEnv().Canvas)return tf5.browser.fromPixels(imageToSquare(input,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)});return tf5.stack(inputTensors.map(t=>tf5.cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input,i)=>{if(!isMediaElement(input)&&!isTensor3D(input)&&!isTensor4D(input))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input=>isMediaElement(input)&&awaitMediaLoaded(input))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input,detections){let{Canvas}=env.getEnv(),canvas=input;if(!(input instanceof Canvas)){let netInput=await toNetInput(input);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}var tf6=__toModule(require_tfjs_esm());async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tf6.tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>tf6.slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init){let{fetch}=env.getEnv(),res=await fetch(url,init);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}var tf7=__toModule(require_tfjs_esm());function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return tf7.io.loadWeights(manifest,modelBaseUri)}function matchDimensions(input,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input.width=width,input.height=height,{width,height}}var tf15=__toModule(require_tfjs_esm()),tf8=__toModule(require_tfjs_esm()),NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof tf8.Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof tf8.Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable})=>{let tensor2=tf8.tensor(variable.dataSync());variable.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights=tf8.io.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof tf8.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}},tf10=__toModule(require_tfjs_esm()),tf9=__toModule(require_tfjs_esm());function depthwiseSeparableConv(x,params,stride){return tf9.tidy(()=>{let out=tf9.separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=tf9.add(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=tf10.relu(tf10.add(out1,tf10.add(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,tf10.add(out3,out4))))})}var tf11=__toModule(require_tfjs_esm());function convLayer(x,params,padding="same",withRelu=!1){return tf11.tidy(()=>{let out=tf11.add(tf11.conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?tf11.relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}var tf12=__toModule(require_tfjs_esm());function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tf12.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf12.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}var tf13=__toModule(require_tfjs_esm());function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tf13.tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tf13.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var tf14=__toModule(require_tfjs_esm()),SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tf14.tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tf14.tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tf14.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tf15.tidy(()=>{let batchTensor=tf15.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf15.scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=tf15.avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}},tf17=__toModule(require_tfjs_esm()),tf16=__toModule(require_tfjs_esm());function fullyConnectedLayer(x,params){return tf16.tidy(()=>tf16.add(tf16.matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf17.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}},FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input){return tf18.tidy(()=>tf18.softmax(this.runNet(input)))}async forward(input){return this.forwardInput(await toNetInput(input))}async predictExpressions(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(tf18.unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version="0.10.1",tf20=__toModule(require_tfjs_esm()),tf19=__toModule(require_tfjs_esm());function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return tf19.add(tf19.conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?tf19.relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=tf19.maxPool(out,[3,3],[2,2],"same"),out=tf19.add(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(tf19.relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv2,[1,1]),out=tf19.add(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tf19.tidy(()=>{let batchTensor=tf19.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf19.scalar(256)),out=tf19.relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=tf19.relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf20.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input,pooled=tf20.avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input){return tf20.tidy(()=>{let{age,gender}=this.runNet(input);return{age,gender:tf20.softmax(gender)}})}async forward(input){return this.forwardInput(await toNetInput(input))}async predictAgeAndGender(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),ages=tf20.unstack(out.age),genders=tf20.unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var tf21=__toModule(require_tfjs_esm()),FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale2=inputSize/Math.max(height,width);return{width:width*scale2,height:height*scale2}}),batchSize=inputDimensions.length;return tf21.tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>tf21.stack([tf21.fill([68],fillX,"float32"),tf21.fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding(batchIdx,(w,h)=>h<w);return output.mul(tf21.fill([batchSize,136],inputSize,"float32")).sub(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input){return tf21.tidy(()=>{let out=this.runNet(input);return this.postProcess(out,input.inputSize,input.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input){return this.forwardInput(await toNetInput(input))}async detectLandmarks(input){let netInput=await toNetInput(input),landmarkTensors=tf21.tidy(()=>tf21.unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven(i)),yCoords=landmarksArray.filter((_,i)=>!isEven(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var tf22=__toModule(require_tfjs_esm());function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tf22.tidy(()=>{let batchTensor=tf22.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf22.scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=tf22.avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};var tf27=__toModule(require_tfjs_esm()),tf24=__toModule(require_tfjs_esm()),tf23=__toModule(require_tfjs_esm());function scale(x,params){return tf23.add(tf23.mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=tf24.conv2d(x,filters,strides,padding);return out=tf24.add(out,bias),out=scale(out,params.scale),withRelu?tf24.relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}var tf25=__toModule(require_tfjs_esm());function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tf25.tidy(()=>tf25.transpose(tf25.tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tf25.tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tf25.tensor1d(extractWeights(numWeights)),biases=tf25.tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale2=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale2}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tf25.tidy(()=>tf25.transpose(tf25.tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale2=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale2}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf26=__toModule(require_tfjs_esm());function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=tf26.add(out,x),out=tf26.relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=tf26.avgPool(x,2,2,"valid"),zeros2=tf26.zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=tf26.zeros(padShapeX);out=tf26.concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=tf26.zeros(padShapeY);out=tf26.concat([out,zerosH],2)}return pooled=isPad?tf26.concat([pooled,zeros2],3):pooled,out=tf26.add(pooled,out),out=tf26.relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tf27.tidy(()=>{let batchTensor=tf27.cast(input.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf27.scalar(256)),out=convDown(normalized,params.conv32_down);out=tf27.maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return tf27.matMul(globalAvg,params.fc)})}async forward(input){return this.forwardInput(await toNetInput(input))}async computeFaceDescriptor(input){let netInput=await toNetInput(input),faceDescriptorTensors=tf27.tidy(()=>tf27.unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}var tf34=__toModule(require_tfjs_esm()),tf28=__toModule(require_tfjs_esm());function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tf28.tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tf28.tensor1d(extractWeights(numChannels)),batch_norm_offset=tf28.tensor1d(extractWeights(numChannels)),batch_norm_mean=tf28.tensor1d(extractWeights(numChannels)),batch_norm_variance=tf28.tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tf28.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf28.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tf28.tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf30=__toModule(require_tfjs_esm()),tf29=__toModule(require_tfjs_esm());function pointwiseConvLayer(x,params,strides){return tf29.tidy(()=>{let out=tf29.conv2d(x,params.filters,strides,"same");return out=tf29.add(out,params.batch_norm_offset),tf29.clipByValue(out,0,6)})}var epsilon=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tf30.tidy(()=>{let out=tf30.depthwiseConv2d(x,params.filters,strides,"same");return out=tf30.batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon),tf30.clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tf30.tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(!(selected.length>=outputSize)){let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}}),selected}var tf31=__toModule(require_tfjs_esm());function getCenterCoordinatesAndSizesLayer(x){let vec=tf31.unstack(tf31.transpose(x,[1,0])),sizes=[tf31.sub(vec[2],vec[0]),tf31.sub(vec[3],vec[1])],centers=[tf31.add(vec[0],tf31.div(sizes[0],tf31.scalar(2))),tf31.add(vec[1],tf31.div(sizes[1],tf31.scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=tf31.unstack(tf31.transpose(x1,[1,0])),div0_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2],tf31.scalar(5))),sizes[0]),tf31.scalar(2)),add0_out=tf31.add(tf31.mul(tf31.div(vec[0],tf31.scalar(10)),sizes[0]),centers[0]),div1_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3],tf31.scalar(5))),sizes[1]),tf31.scalar(2)),add1_out=tf31.add(tf31.mul(tf31.div(vec[1],tf31.scalar(10)),sizes[1]),centers[1]);return tf31.transpose(tf31.stack([tf31.sub(add0_out,div0_out),tf31.sub(add1_out,div1_out),tf31.add(add0_out,div0_out),tf31.add(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tf31.tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(tf31.reshape(tf31.tile(params.extra_dim,[batchSize,1,1]),[-1,4]),tf31.reshape(boxPredictions,[-1,4]));boxes=tf31.reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=tf31.sigmoid(tf31.slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=tf31.slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=tf31.reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=tf31.unstack(boxes),scoresByBatch=tf31.unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}var tf33=__toModule(require_tfjs_esm()),tf32=__toModule(require_tfjs_esm());function boxPredictionLayer(x,params){return tf32.tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=tf32.reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=tf32.reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tf33.tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=tf33.concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=tf33.concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tf34.tidy(()=>{let batchTensor=tf34.cast(input.toBatchTensor(512,!1),"float32"),x=tf34.sub(tf34.mul(batchTensor,tf34.scalar(.007843137718737125)),tf34.scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input){return this.forwardInput(await toNetInput(input))}async locateFaces(input,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression2(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",tf39=__toModule(require_tfjs_esm()),isNumber=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber(a.x)&&isNumber(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}var tf36=__toModule(require_tfjs_esm()),tf35=__toModule(require_tfjs_esm());function leaky(x){return tf35.tidy(()=>{let min=tf35.mul(x,tf35.scalar(.10000000149011612));return tf35.add(tf35.relu(tf35.sub(x,min)),min)})}function convWithBatchNorm(x,params){return tf36.tidy(()=>{let out=tf36.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf36.conv2d(out,params.conv.filters,[1,1],"valid"),out=tf36.sub(out,params.bn.sub),out=tf36.mul(out,params.bn.truediv),out=tf36.add(out,params.conv.bias),leaky(out)})}var tf37=__toModule(require_tfjs_esm());function depthwiseSeparableConv2(x,params){return tf37.tidy(()=>{let out=tf37.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf37.separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=tf37.add(out,params.bias),leaky(out)})}var tf38=__toModule(require_tfjs_esm());function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub6=tf38.tensor1d(extractWeights(size)),truediv=tf38.tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub6,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub6=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub6,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}},TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(x,params){let out=convWithBatchNorm(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tf39.tidy(()=>{let batchTensor=tf39.cast(input.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(tf39.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input,inputSize){return this.forwardInput(await toNetInput(input),inputSize)}async detect(input,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input),out=await this.forwardInput(netInput,inputSize),out0=tf39.tidy(()=>tf39.unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tf39.tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?tf39.softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):tf39.scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max,curr)=>max.classScore>curr.classScore?max:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},tf41=__toModule(require_tfjs_esm()),tf40=__toModule(require_tfjs_esm());async function extractAllFacesAndComputeResults(parentResults,input,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input instanceof tf40.Tensor?await extractFaceTensors(input,faceBoxes):await extractFaces(input,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof tf40.Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}},nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input,options)=>nets.ssdMobilenetv1.locateFaces(input,options),tinyFaceDetector=(input,options)=>nets.tinyFaceDetector.locateFaces(input,options),tinyYolov2=(input,options)=>nets.tinyYolov2.locateFaces(input,options),detectFaceLandmarks=input=>nets.faceLandmark68Net.detectLandmarks(input),detectFaceLandmarksTiny=input=>nets.faceLandmark68TinyNet.detectLandmarks(input),computeFaceDescriptor=input=>nets.faceRecognitionNet.computeFaceDescriptor(input),recognizeFaceExpressions=input=>nets.faceExpressionNet.predictExpressions(input),predictAgeAndGender=input=>nets.ageGenderNet.predictAgeAndGender(input),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input){super();this.parentTask=parentTask;this.input=input}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{detection}=parentResult,faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input,options=new SsdMobilenetv1Options){super();this.input=input;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input2=>nets.tinyFaceDetector.locateFaces(input2,options):options instanceof SsdMobilenetv1Options?input2=>nets.ssdMobilenetv1.locateFaces(input2,options):options instanceof TinyYolov2Options?input2=>nets.tinyYolov2.locateFaces(input2,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input,options)}function detectAllFaces(input,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input,options)}async function allFacesSsdMobilenetv1(input,minConfidence){return detectAllFaces(input,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input,forwardParams={}){return detectAllFaces(input,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count=1,createUniqueLabel=()=>`person ${count++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json){let labeledDescriptors=json.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser3=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version2={faceapi:version,node,browser:browser3};
+var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2);var require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node-gpu")))});var require_isNodejs=__commonJS((exports2,module2)=>{__export(exports2,{isNodejs:()=>isNodejs2});function isNodejs2(){return typeof global=="object"&&!0&&typeof module2!="undefined"&&typeof process!="undefined"&&!!process.version}});__export(exports,{AgeGenderNet:()=>AgeGenderNet,BoundingBox:()=>BoundingBox,Box:()=>Box,ComposableTask:()=>ComposableTask,ComputeAllFaceDescriptorsTask:()=>ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase:()=>ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask:()=>ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask:()=>DetectAllFaceLandmarksTask,DetectAllFacesTask:()=>DetectAllFacesTask,DetectFaceLandmarksTaskBase:()=>DetectFaceLandmarksTaskBase,DetectFacesTaskBase:()=>DetectFacesTaskBase,DetectSingleFaceLandmarksTask:()=>DetectSingleFaceLandmarksTask,DetectSingleFaceTask:()=>DetectSingleFaceTask,Dimensions:()=>Dimensions,FACE_EXPRESSION_LABELS:()=>FACE_EXPRESSION_LABELS,FaceDetection:()=>FaceDetection,FaceDetectionNet:()=>FaceDetectionNet,FaceExpressionNet:()=>FaceExpressionNet,FaceExpressions:()=>FaceExpressions,FaceLandmark68Net:()=>FaceLandmark68Net,FaceLandmark68TinyNet:()=>FaceLandmark68TinyNet,FaceLandmarkNet:()=>FaceLandmarkNet,FaceLandmarks:()=>FaceLandmarks,FaceLandmarks5:()=>FaceLandmarks5,FaceLandmarks68:()=>FaceLandmarks68,FaceMatch:()=>FaceMatch,FaceMatcher:()=>FaceMatcher,FaceRecognitionNet:()=>FaceRecognitionNet,Gender:()=>Gender,LabeledBox:()=>LabeledBox,LabeledFaceDescriptors:()=>LabeledFaceDescriptors,NetInput:()=>NetInput,NeuralNetwork:()=>NeuralNetwork,ObjectDetection:()=>ObjectDetection,Point:()=>Point,PredictedBox:()=>PredictedBox,Rect:()=>Rect,SsdMobilenetv1:()=>SsdMobilenetv1,SsdMobilenetv1Options:()=>SsdMobilenetv1Options,TinyFaceDetector:()=>TinyFaceDetector,TinyFaceDetectorOptions:()=>TinyFaceDetectorOptions,TinyYolov2:()=>TinyYolov2,TinyYolov2Options:()=>TinyYolov2Options,allFaces:()=>allFaces,allFacesSsdMobilenetv1:()=>allFacesSsdMobilenetv1,allFacesTinyYolov2:()=>allFacesTinyYolov2,awaitMediaLoaded:()=>awaitMediaLoaded,bufferToImage:()=>bufferToImage,computeFaceDescriptor:()=>computeFaceDescriptor,createCanvas:()=>createCanvas,createCanvasFromMedia:()=>createCanvasFromMedia,createFaceDetectionNet:()=>createFaceDetectionNet,createFaceRecognitionNet:()=>createFaceRecognitionNet,createSsdMobilenetv1:()=>createSsdMobilenetv1,createTinyFaceDetector:()=>createTinyFaceDetector,createTinyYolov2:()=>createTinyYolov2,detectAllFaces:()=>detectAllFaces,detectFaceLandmarks:()=>detectFaceLandmarks,detectFaceLandmarksTiny:()=>detectFaceLandmarksTiny,detectLandmarks:()=>detectLandmarks,detectSingleFace:()=>detectSingleFace,draw:()=>draw_exports,env:()=>env,euclideanDistance:()=>euclideanDistance,extendWithAge:()=>extendWithAge,extendWithFaceDescriptor:()=>extendWithFaceDescriptor,extendWithFaceDetection:()=>extendWithFaceDetection,extendWithFaceExpressions:()=>extendWithFaceExpressions,extendWithFaceLandmarks:()=>extendWithFaceLandmarks,extendWithGender:()=>extendWithGender,extractFaceTensors:()=>extractFaceTensors,extractFaces:()=>extractFaces,fetchImage:()=>fetchImage,fetchJson:()=>fetchJson,fetchNetWeights:()=>fetchNetWeights,fetchOrThrow:()=>fetchOrThrow,getContext2dOrThrow:()=>getContext2dOrThrow,getMediaDimensions:()=>getMediaDimensions,imageTensorToCanvas:()=>imageTensorToCanvas,imageToSquare:()=>imageToSquare,inverseSigmoid:()=>inverseSigmoid,iou:()=>iou,isMediaElement:()=>isMediaElement,isMediaLoaded:()=>isMediaLoaded,isWithAge:()=>isWithAge,isWithFaceDetection:()=>isWithFaceDetection,isWithFaceExpressions:()=>isWithFaceExpressions,isWithFaceLandmarks:()=>isWithFaceLandmarks,isWithGender:()=>isWithGender,loadAgeGenderModel:()=>loadAgeGenderModel,loadFaceDetectionModel:()=>loadFaceDetectionModel,loadFaceExpressionModel:()=>loadFaceExpressionModel,loadFaceLandmarkModel:()=>loadFaceLandmarkModel,loadFaceLandmarkTinyModel:()=>loadFaceLandmarkTinyModel,loadFaceRecognitionModel:()=>loadFaceRecognitionModel,loadSsdMobilenetv1Model:()=>loadSsdMobilenetv1Model,loadTinyFaceDetectorModel:()=>loadTinyFaceDetectorModel,loadTinyYolov2Model:()=>loadTinyYolov2Model,loadWeightMap:()=>loadWeightMap,locateFaces:()=>locateFaces,matchDimensions:()=>matchDimensions,minBbox:()=>minBbox,nets:()=>nets,nonMaxSuppression:()=>nonMaxSuppression,normalize:()=>normalize,padToSquare:()=>padToSquare,predictAgeAndGender:()=>predictAgeAndGender,recognizeFaceExpressions:()=>recognizeFaceExpressions,resizeResults:()=>resizeResults,resolveInput:()=>resolveInput,shuffleArray:()=>shuffleArray,sigmoid:()=>sigmoid,ssdMobilenetv1:()=>ssdMobilenetv1,tf:()=>tf42,tinyFaceDetector:()=>tinyFaceDetector,tinyYolov2:()=>tinyYolov2,toNetInput:()=>toNetInput,utils:()=>utils_exports,validateConfig:()=>validateConfig,version:()=>version2});var tf42=__toModule(require_tfjs_esm());var draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range,round:()=>round});var tf=__toModule(require_tfjs_esm());var Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof tf.Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!=0}function isEven(num){return num%2==0}function round(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale2=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale2),Math.round(height*scale2))}function getCenterPoint(pts){return pts.reduce((sum,pt)=>sum.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range(num,start,step){return Array(num).fill(0).map((_,i)=>start+i*step)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}};var Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}};var BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}};var FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min,x)=>x<min?x:min,Infinity),minY=ys.reduce((min,y)=>y<min?y:min,Infinity),maxX=xs.reduce((max,x)=>max<x?x:max,0),maxY=ys.reduce((max,y)=>max<y?y:max,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}var tf2=__toModule(require_tfjs_esm());function normalize(x,meanRgb){return tf2.tidy(()=>{let[r,g,b]=meanRgb,avg_r=tf2.fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=tf2.fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=tf2.fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=tf2.concat([avg_r,avg_g,avg_b],3);return tf2.sub(x,avg_rgb)})}var tf3=__toModule(require_tfjs_esm());function padToSquare(imgTensor,isCenterImage=!1){return tf3.tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,tf3.fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>tf3.cast(t,"float32"));return tf3.concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array=inputArray.slice();for(let i=array.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array[i];array[i]=array[j],array[j]=x}return array}function sigmoid(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}};var relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json){let descriptors=json.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch=window.fetch;if(!fetch)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer)=>err?reject(err):resolve(buffer))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch,...fileSystem}}function isBrowser(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env2){environment=env2}function initialize(){return isBrowser()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env2){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env2;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env2.createCanvasElement||(()=>new Canvas),environment.createImageElement=env2.createImageElement||(()=>new Image),environment.ImageData=env2.ImageData||environment.ImageData,environment.Video=env2.Video||environment.Video,environment.fetch=env2.fetch||environment.fetch,environment.readFile=env2.readFile||environment.readFile}var env={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}};var DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}var tf18=__toModule(require_tfjs_esm());function isMediaLoaded(media){let{Image,Video}=env.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input){let{Image,Video}=env.getEnv();return input instanceof Image?new Dimensions(input.naturalWidth,input.naturalHeight):input instanceof Video?new Dimensions(input.videoWidth,input.videoHeight):new Dimensions(input.width,input.height)}function createCanvas({width,height}){let{createCanvasElement}=env.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}var tf4=__toModule(require_tfjs_esm());async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tf4.tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await tf4.browser.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input){let{Image,Canvas,Video}=env.getEnv();return input instanceof Image||input instanceof Canvas||input instanceof Video}var tf5=__toModule(require_tfjs_esm());function imageToSquare(input,inputSize,centerImage=!1){let{Image,Canvas}=env.getEnv();if(!(input instanceof Image||input instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input),scale2=inputSize/Math.max(dims.height,dims.width),width=scale2*dims.width,height=scale2*dims.height,targetCanvas=createCanvas({width:inputSize,height:inputSize}),inputCanvas=input instanceof Canvas?input:createCanvasFromMedia(input),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input,idx)=>{if(isTensor3D(input)){this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape;return}if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape.slice(1);return}let canvas=input instanceof env.getEnv().Canvas?input:createCanvasFromMedia(input);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tf5.tidy(()=>{let inputTensors=range(this.batchSize,0,1).map(batchIdx=>{let input=this.getInput(batchIdx);if(input instanceof tf5.Tensor){let imgTensor=isTensor4D(input)?input:input.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=tf5.image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input instanceof env.getEnv().Canvas)return tf5.browser.fromPixels(imageToSquare(input,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)});return tf5.stack(inputTensors.map(t=>tf5.cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input,i)=>{if(!isMediaElement(input)&&!isTensor3D(input)&&!isTensor4D(input))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input=>isMediaElement(input)&&awaitMediaLoaded(input))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input,detections){let{Canvas}=env.getEnv(),canvas=input;if(!(input instanceof Canvas)){let netInput=await toNetInput(input);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}var tf6=__toModule(require_tfjs_esm());async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tf6.tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>tf6.slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init){let{fetch}=env.getEnv(),res=await fetch(url,init);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}var tf7=__toModule(require_tfjs_esm());function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return tf7.io.loadWeights(manifest,modelBaseUri)}function matchDimensions(input,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input.width=width,input.height=height,{width,height}}var tf15=__toModule(require_tfjs_esm());var tf8=__toModule(require_tfjs_esm());var NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof tf8.Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof tf8.Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable})=>{let tensor2=tf8.tensor(variable.dataSync());variable.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights=tf8.io.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof tf8.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}};var tf10=__toModule(require_tfjs_esm());var tf9=__toModule(require_tfjs_esm());function depthwiseSeparableConv(x,params,stride){return tf9.tidy(()=>{let out=tf9.separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=tf9.add(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=tf10.relu(tf10.add(out1,tf10.add(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,tf10.add(out3,out4))))})}var tf11=__toModule(require_tfjs_esm());function convLayer(x,params,padding="same",withRelu=!1){return tf11.tidy(()=>{let out=tf11.add(tf11.conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?tf11.relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}var tf12=__toModule(require_tfjs_esm());function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tf12.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf12.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}var tf13=__toModule(require_tfjs_esm());function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tf13.tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tf13.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var tf14=__toModule(require_tfjs_esm());var SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tf14.tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tf14.tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tf14.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tf15.tidy(()=>{let batchTensor=tf15.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf15.scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=tf15.avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}};var tf17=__toModule(require_tfjs_esm());var tf16=__toModule(require_tfjs_esm());function fullyConnectedLayer(x,params){return tf16.tidy(()=>tf16.add(tf16.matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf17.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}};var FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input){return tf18.tidy(()=>tf18.softmax(this.runNet(input)))}async forward(input){return this.forwardInput(await toNetInput(input))}async predictExpressions(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(tf18.unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version="0.10.2";var tf20=__toModule(require_tfjs_esm());var tf19=__toModule(require_tfjs_esm());function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return tf19.add(tf19.conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?tf19.relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=tf19.maxPool(out,[3,3],[2,2],"same"),out=tf19.add(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(tf19.relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv2,[1,1]),out=tf19.add(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tf19.tidy(()=>{let batchTensor=tf19.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf19.scalar(256)),out=tf19.relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=tf19.relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf20.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input,pooled=tf20.avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input){return tf20.tidy(()=>{let{age,gender}=this.runNet(input);return{age,gender:tf20.softmax(gender)}})}async forward(input){return this.forwardInput(await toNetInput(input))}async predictAgeAndGender(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),ages=tf20.unstack(out.age),genders=tf20.unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var tf21=__toModule(require_tfjs_esm());var FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale2=inputSize/Math.max(height,width);return{width:width*scale2,height:height*scale2}}),batchSize=inputDimensions.length;return tf21.tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>tf21.stack([tf21.fill([68],fillX,"float32"),tf21.fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding(batchIdx,(w,h)=>h<w);return output.mul(tf21.fill([batchSize,136],inputSize,"float32")).sub(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input){return tf21.tidy(()=>{let out=this.runNet(input);return this.postProcess(out,input.inputSize,input.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input){return this.forwardInput(await toNetInput(input))}async detectLandmarks(input){let netInput=await toNetInput(input),landmarkTensors=tf21.tidy(()=>tf21.unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven(i)),yCoords=landmarksArray.filter((_,i)=>!isEven(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}};var FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var tf22=__toModule(require_tfjs_esm());function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tf22.tidy(()=>{let batchTensor=tf22.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf22.scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=tf22.avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}};var FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var FaceLandmarkNet=class extends FaceLandmark68Net{};var tf27=__toModule(require_tfjs_esm());var tf24=__toModule(require_tfjs_esm());var tf23=__toModule(require_tfjs_esm());function scale(x,params){return tf23.add(tf23.mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=tf24.conv2d(x,filters,strides,padding);return out=tf24.add(out,bias),out=scale(out,params.scale),withRelu?tf24.relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}var tf25=__toModule(require_tfjs_esm());function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tf25.tidy(()=>tf25.transpose(tf25.tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tf25.tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tf25.tensor1d(extractWeights(numWeights)),biases=tf25.tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale2=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale2}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tf25.tidy(()=>tf25.transpose(tf25.tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale2=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale2}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf26=__toModule(require_tfjs_esm());function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=tf26.add(out,x),out=tf26.relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=tf26.avgPool(x,2,2,"valid"),zeros2=tf26.zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=tf26.zeros(padShapeX);out=tf26.concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=tf26.zeros(padShapeY);out=tf26.concat([out,zerosH],2)}return pooled=isPad?tf26.concat([pooled,zeros2],3):pooled,out=tf26.add(pooled,out),out=tf26.relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tf27.tidy(()=>{let batchTensor=tf27.cast(input.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf27.scalar(256)),out=convDown(normalized,params.conv32_down);out=tf27.maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return tf27.matMul(globalAvg,params.fc)})}async forward(input){return this.forwardInput(await toNetInput(input))}async computeFaceDescriptor(input){let netInput=await toNetInput(input),faceDescriptorTensors=tf27.tidy(()=>tf27.unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}var tf34=__toModule(require_tfjs_esm());var tf28=__toModule(require_tfjs_esm());function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tf28.tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tf28.tensor1d(extractWeights(numChannels)),batch_norm_offset=tf28.tensor1d(extractWeights(numChannels)),batch_norm_mean=tf28.tensor1d(extractWeights(numChannels)),batch_norm_variance=tf28.tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tf28.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf28.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tf28.tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf30=__toModule(require_tfjs_esm());var tf29=__toModule(require_tfjs_esm());function pointwiseConvLayer(x,params,strides){return tf29.tidy(()=>{let out=tf29.conv2d(x,params.filters,strides,"same");return out=tf29.add(out,params.batch_norm_offset),tf29.clipByValue(out,0,6)})}var epsilon=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tf30.tidy(()=>{let out=tf30.depthwiseConv2d(x,params.filters,strides,"same");return out=tf30.batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon),tf30.clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tf30.tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(selected.length>=outputSize)return;let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}),selected}var tf31=__toModule(require_tfjs_esm());function getCenterCoordinatesAndSizesLayer(x){let vec=tf31.unstack(tf31.transpose(x,[1,0])),sizes=[tf31.sub(vec[2],vec[0]),tf31.sub(vec[3],vec[1])],centers=[tf31.add(vec[0],tf31.div(sizes[0],tf31.scalar(2))),tf31.add(vec[1],tf31.div(sizes[1],tf31.scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=tf31.unstack(tf31.transpose(x1,[1,0])),div0_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2],tf31.scalar(5))),sizes[0]),tf31.scalar(2)),add0_out=tf31.add(tf31.mul(tf31.div(vec[0],tf31.scalar(10)),sizes[0]),centers[0]),div1_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3],tf31.scalar(5))),sizes[1]),tf31.scalar(2)),add1_out=tf31.add(tf31.mul(tf31.div(vec[1],tf31.scalar(10)),sizes[1]),centers[1]);return tf31.transpose(tf31.stack([tf31.sub(add0_out,div0_out),tf31.sub(add1_out,div1_out),tf31.add(add0_out,div0_out),tf31.add(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tf31.tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(tf31.reshape(tf31.tile(params.extra_dim,[batchSize,1,1]),[-1,4]),tf31.reshape(boxPredictions,[-1,4]));boxes=tf31.reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=tf31.sigmoid(tf31.slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=tf31.slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=tf31.reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=tf31.unstack(boxes),scoresByBatch=tf31.unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}var tf33=__toModule(require_tfjs_esm());var tf32=__toModule(require_tfjs_esm());function boxPredictionLayer(x,params){return tf32.tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=tf32.reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=tf32.reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tf33.tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=tf33.concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=tf33.concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tf34.tidy(()=>{let batchTensor=tf34.cast(input.toBatchTensor(512,!1),"float32"),x=tf34.sub(tf34.mul(batchTensor,tf34.scalar(.007843137718737125)),tf34.scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input){return this.forwardInput(await toNetInput(input))}async locateFaces(input,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression2(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{};var IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model";var tf39=__toModule(require_tfjs_esm());var isNumber=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber(a.x)&&isNumber(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}var tf36=__toModule(require_tfjs_esm());var tf35=__toModule(require_tfjs_esm());function leaky(x){return tf35.tidy(()=>{let min=tf35.mul(x,tf35.scalar(.10000000149011612));return tf35.add(tf35.relu(tf35.sub(x,min)),min)})}function convWithBatchNorm(x,params){return tf36.tidy(()=>{let out=tf36.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf36.conv2d(out,params.conv.filters,[1,1],"valid"),out=tf36.sub(out,params.bn.sub),out=tf36.mul(out,params.bn.truediv),out=tf36.add(out,params.conv.bias),leaky(out)})}var tf37=__toModule(require_tfjs_esm());function depthwiseSeparableConv2(x,params){return tf37.tidy(()=>{let out=tf37.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf37.separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=tf37.add(out,params.bias),leaky(out)})}var tf38=__toModule(require_tfjs_esm());function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub6=tf38.tensor1d(extractWeights(size)),truediv=tf38.tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub6,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub6=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub6,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(x,params){let out=convWithBatchNorm(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tf39.tidy(()=>{let batchTensor=tf39.cast(input.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(tf39.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input,inputSize){return this.forwardInput(await toNetInput(input),inputSize)}async detect(input,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input),out=await this.forwardInput(netInput,inputSize),out0=tf39.tidy(()=>tf39.unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tf39.tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?tf39.softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):tf39.scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max,curr)=>max.classScore>curr.classScore?max:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var tf41=__toModule(require_tfjs_esm());var tf40=__toModule(require_tfjs_esm());async function extractAllFacesAndComputeResults(parentResults,input,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input instanceof tf40.Tensor?await extractFaceTensors(input,faceBoxes):await extractFaces(input,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof tf40.Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404];var TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};var nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input,options)=>nets.ssdMobilenetv1.locateFaces(input,options),tinyFaceDetector=(input,options)=>nets.tinyFaceDetector.locateFaces(input,options),tinyYolov2=(input,options)=>nets.tinyYolov2.locateFaces(input,options),detectFaceLandmarks=input=>nets.faceLandmark68Net.detectLandmarks(input),detectFaceLandmarksTiny=input=>nets.faceLandmark68TinyNet.detectLandmarks(input),computeFaceDescriptor=input=>nets.faceRecognitionNet.computeFaceDescriptor(input),recognizeFaceExpressions=input=>nets.faceExpressionNet.predictExpressions(input),predictAgeAndGender=input=>nets.ageGenderNet.predictAgeAndGender(input),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks;var PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input){super();this.parentTask=parentTask;this.input=input}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}};var DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{detection}=parentResult,faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var DetectFacesTaskBase=class extends ComposableTask{constructor(input,options=new SsdMobilenetv1Options){super();this.input=input;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input2=>nets.tinyFaceDetector.locateFaces(input2,options):options instanceof SsdMobilenetv1Options?input2=>nets.ssdMobilenetv1.locateFaces(input2,options):options instanceof TinyYolov2Options?input2=>nets.tinyYolov2.locateFaces(input2,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input,options)}function detectAllFaces(input,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input,options)}async function allFacesSsdMobilenetv1(input,minConfidence){return detectAllFaces(input,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input,forwardParams={}){return detectAllFaces(input,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count=1,createUniqueLabel=()=>`person ${count++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json){let labeledDescriptors=json.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser3=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version2={faceapi:version,node,browser:browser3};
 //# sourceMappingURL=face-api.node-gpu.js.map
diff --git a/dist/face-api.node-gpu.js.map b/dist/face-api.node-gpu.js.map
index b7ae046..bfba17f 100644
--- a/dist/face-api.node-gpu.js.map
+++ b/dist/face-api.node-gpu.js.map
@@ -2,6 +2,6 @@
   "version": 3,
   "sources": ["../src/tfjs/tf-node-gpu.ts", "../src/env/isNodejs.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts"],
   "sourcesContent": ["/* eslint-disable import/no-extraneous-dependencies */\n/* eslint-disable node/no-unpublished-import */\n\nexport * from '@tensorflow/tfjs-node-gpu';\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\n// eslint-disable-next-line no-undef\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  // eslint-disable-next-line no-mixed-operators\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n\n    const { readFile } = env.getEnv();\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)),\n    );\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParamsFromWeigthMap(weightMap);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParams(weights);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input);\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      (t) => t.data(),\n    )) as Float32Array[];\n\n    faceDescriptorTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    // TODO batches\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data());\n\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) {\n      return;\n    }\n    const originalScore = c.score;\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n\n    return {\n      boxPredictionEncoding,\n      classPrediction,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024,\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n    // return tf.maximum(x, min)\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config';\nexport * from './types';\n\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n"],
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index 753e35d..0ecc469 100644
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-          "bytesInOutput": 1900
+          "bytesInOutput": 1905
         },
         "src/ssdMobilenetv1/extractParams.ts": {
-          "bytesInOutput": 5963
+          "bytesInOutput": 5967
         },
         "src/ssdMobilenetv1/extractParamsFromWeigthMap.ts": {
           "bytesInOutput": 4319
         },
         "src/ssdMobilenetv1/mobileNetV1.ts": {
-          "bytesInOutput": 1110
+          "bytesInOutput": 1111
         },
         "src/ssdMobilenetv1/pointwiseConvLayer.ts": {
-          "bytesInOutput": 235
+          "bytesInOutput": 239
         },
         "src/ssdMobilenetv1/nonMaxSuppression.ts": {
-          "bytesInOutput": 1502
+          "bytesInOutput": 1504
         },
         "src/ssdMobilenetv1/outputLayer.ts": {
           "bytesInOutput": 1590
         },
         "src/ssdMobilenetv1/predictionLayer.ts": {
-          "bytesInOutput": 1418
+          "bytesInOutput": 1419
         },
         "src/ssdMobilenetv1/boxPredictionLayer.ts": {
-          "bytesInOutput": 350
+          "bytesInOutput": 354
         },
         "src/ssdMobilenetv1/SsdMobilenetv1Options.ts": {
-          "bytesInOutput": 554
+          "bytesInOutput": 555
         },
         "src/ssdMobilenetv1/index.ts": {
-          "bytesInOutput": 234
+          "bytesInOutput": 235
         },
         "src/tinyYolov2/const.ts": {
-          "bytesInOutput": 485
+          "bytesInOutput": 490
         },
         "src/tinyYolov2/TinyYolov2Base.ts": {
-          "bytesInOutput": 5885
+          "bytesInOutput": 5894
         },
         "src/tinyYolov2/config.ts": {
-          "bytesInOutput": 1164
+          "bytesInOutput": 1168
         },
         "src/tinyYolov2/convWithBatchNorm.ts": {
-          "bytesInOutput": 322
+          "bytesInOutput": 323
         },
         "src/tinyYolov2/leaky.ts": {
-          "bytesInOutput": 178
+          "bytesInOutput": 182
         },
         "src/tinyYolov2/depthwiseSeparableConv.ts": {
           "bytesInOutput": 293
@@ -2557,37 +2557,37 @@
           "bytesInOutput": 139
         },
         "src/tinyFaceDetector/TinyFaceDetectorOptions.ts": {
-          "bytesInOutput": 132
+          "bytesInOutput": 133
         },
         "src/globalApi/ComposableTask.ts": {
-          "bytesInOutput": 154
+          "bytesInOutput": 159
         },
         "src/globalApi/DetectFaceLandmarksTasks.ts": {
-          "bytesInOutput": 1912
+          "bytesInOutput": 1926
         },
         "src/globalApi/extractFacesAndComputeResults.ts": {
-          "bytesInOutput": 812
+          "bytesInOutput": 816
         },
         "src/tinyFaceDetector/const.ts": {
-          "bytesInOutput": 215
+          "bytesInOutput": 216
         },
         "src/tinyFaceDetector/TinyFaceDetector.ts": {
-          "bytesInOutput": 624
+          "bytesInOutput": 629
         },
         "src/globalApi/nets.ts": {
-          "bytesInOutput": 1515
+          "bytesInOutput": 1520
         },
         "src/globalApi/PredictFaceExpressionsTask.ts": {
-          "bytesInOutput": 1691
+          "bytesInOutput": 1700
         },
         "src/globalApi/PredictAgeAndGenderTask.ts": {
-          "bytesInOutput": 1785
+          "bytesInOutput": 1794
         },
         "src/globalApi/ComputeFaceDescriptorsTasks.ts": {
-          "bytesInOutput": 1390
+          "bytesInOutput": 1399
         },
         "src/globalApi/DetectFacesTasks.ts": {
-          "bytesInOutput": 2221
+          "bytesInOutput": 2225
         },
         "src/globalApi/detectFaces.ts": {
           "bytesInOutput": 224
@@ -2611,7 +2611,7 @@
           "bytesInOutput": 824
         }
       },
-      "bytes": 132274
+      "bytes": 132477
     }
   }
 }
diff --git a/dist/face-api.node.js b/dist/face-api.node.js
index fd87916..96aa75a 100644
--- a/dist/face-api.node.js
+++ b/dist/face-api.node.js
@@ -5,5 +5,5 @@
   author: <https://github.com/vladmandic>'
   */
 
-var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2),require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node")))}),require_isNodejs=__commonJS((exports2,module2)=>{__export(exports2,{isNodejs:()=>isNodejs2});function isNodejs2(){return typeof global=="object"&&!0&&typeof module2!="undefined"&&typeof process!="undefined"&&!!process.version}});__export(exports,{AgeGenderNet:()=>AgeGenderNet,BoundingBox:()=>BoundingBox,Box:()=>Box,ComposableTask:()=>ComposableTask,ComputeAllFaceDescriptorsTask:()=>ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase:()=>ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask:()=>ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask:()=>DetectAllFaceLandmarksTask,DetectAllFacesTask:()=>DetectAllFacesTask,DetectFaceLandmarksTaskBase:()=>DetectFaceLandmarksTaskBase,DetectFacesTaskBase:()=>DetectFacesTaskBase,DetectSingleFaceLandmarksTask:()=>DetectSingleFaceLandmarksTask,DetectSingleFaceTask:()=>DetectSingleFaceTask,Dimensions:()=>Dimensions,FACE_EXPRESSION_LABELS:()=>FACE_EXPRESSION_LABELS,FaceDetection:()=>FaceDetection,FaceDetectionNet:()=>FaceDetectionNet,FaceExpressionNet:()=>FaceExpressionNet,FaceExpressions:()=>FaceExpressions,FaceLandmark68Net:()=>FaceLandmark68Net,FaceLandmark68TinyNet:()=>FaceLandmark68TinyNet,FaceLandmarkNet:()=>FaceLandmarkNet,FaceLandmarks:()=>FaceLandmarks,FaceLandmarks5:()=>FaceLandmarks5,FaceLandmarks68:()=>FaceLandmarks68,FaceMatch:()=>FaceMatch,FaceMatcher:()=>FaceMatcher,FaceRecognitionNet:()=>FaceRecognitionNet,Gender:()=>Gender,LabeledBox:()=>LabeledBox,LabeledFaceDescriptors:()=>LabeledFaceDescriptors,NetInput:()=>NetInput,NeuralNetwork:()=>NeuralNetwork,ObjectDetection:()=>ObjectDetection,Point:()=>Point,PredictedBox:()=>PredictedBox,Rect:()=>Rect,SsdMobilenetv1:()=>SsdMobilenetv1,SsdMobilenetv1Options:()=>SsdMobilenetv1Options,TinyFaceDetector:()=>TinyFaceDetector,TinyFaceDetectorOptions:()=>TinyFaceDetectorOptions,TinyYolov2:()=>TinyYolov2,TinyYolov2Options:()=>TinyYolov2Options,allFaces:()=>allFaces,allFacesSsdMobilenetv1:()=>allFacesSsdMobilenetv1,allFacesTinyYolov2:()=>allFacesTinyYolov2,awaitMediaLoaded:()=>awaitMediaLoaded,bufferToImage:()=>bufferToImage,computeFaceDescriptor:()=>computeFaceDescriptor,createCanvas:()=>createCanvas,createCanvasFromMedia:()=>createCanvasFromMedia,createFaceDetectionNet:()=>createFaceDetectionNet,createFaceRecognitionNet:()=>createFaceRecognitionNet,createSsdMobilenetv1:()=>createSsdMobilenetv1,createTinyFaceDetector:()=>createTinyFaceDetector,createTinyYolov2:()=>createTinyYolov2,detectAllFaces:()=>detectAllFaces,detectFaceLandmarks:()=>detectFaceLandmarks,detectFaceLandmarksTiny:()=>detectFaceLandmarksTiny,detectLandmarks:()=>detectLandmarks,detectSingleFace:()=>detectSingleFace,draw:()=>draw_exports,env:()=>env,euclideanDistance:()=>euclideanDistance,extendWithAge:()=>extendWithAge,extendWithFaceDescriptor:()=>extendWithFaceDescriptor,extendWithFaceDetection:()=>extendWithFaceDetection,extendWithFaceExpressions:()=>extendWithFaceExpressions,extendWithFaceLandmarks:()=>extendWithFaceLandmarks,extendWithGender:()=>extendWithGender,extractFaceTensors:()=>extractFaceTensors,extractFaces:()=>extractFaces,fetchImage:()=>fetchImage,fetchJson:()=>fetchJson,fetchNetWeights:()=>fetchNetWeights,fetchOrThrow:()=>fetchOrThrow,getContext2dOrThrow:()=>getContext2dOrThrow,getMediaDimensions:()=>getMediaDimensions,imageTensorToCanvas:()=>imageTensorToCanvas,imageToSquare:()=>imageToSquare,inverseSigmoid:()=>inverseSigmoid,iou:()=>iou,isMediaElement:()=>isMediaElement,isMediaLoaded:()=>isMediaLoaded,isWithAge:()=>isWithAge,isWithFaceDetection:()=>isWithFaceDetection,isWithFaceExpressions:()=>isWithFaceExpressions,isWithFaceLandmarks:()=>isWithFaceLandmarks,isWithGender:()=>isWithGender,loadAgeGenderModel:()=>loadAgeGenderModel,loadFaceDetectionModel:()=>loadFaceDetectionModel,loadFaceExpressionModel:()=>loadFaceExpressionModel,loadFaceLandmarkModel:()=>loadFaceLandmarkModel,loadFaceLandmarkTinyModel:()=>loadFaceLandmarkTinyModel,loadFaceRecognitionModel:()=>loadFaceRecognitionModel,loadSsdMobilenetv1Model:()=>loadSsdMobilenetv1Model,loadTinyFaceDetectorModel:()=>loadTinyFaceDetectorModel,loadTinyYolov2Model:()=>loadTinyYolov2Model,loadWeightMap:()=>loadWeightMap,locateFaces:()=>locateFaces,matchDimensions:()=>matchDimensions,minBbox:()=>minBbox,nets:()=>nets,nonMaxSuppression:()=>nonMaxSuppression,normalize:()=>normalize,padToSquare:()=>padToSquare,predictAgeAndGender:()=>predictAgeAndGender,recognizeFaceExpressions:()=>recognizeFaceExpressions,resizeResults:()=>resizeResults,resolveInput:()=>resolveInput,shuffleArray:()=>shuffleArray,sigmoid:()=>sigmoid,ssdMobilenetv1:()=>ssdMobilenetv1,tf:()=>tf42,tinyFaceDetector:()=>tinyFaceDetector,tinyYolov2:()=>tinyYolov2,toNetInput:()=>toNetInput,utils:()=>utils_exports,validateConfig:()=>validateConfig,version:()=>version2});var tf42=__toModule(require_tfjs_esm()),draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range,round:()=>round});var tf=__toModule(require_tfjs_esm()),Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof tf.Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!==0}function isEven(num){return num%2===0}function round(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale2=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale2),Math.round(height*scale2))}function getCenterPoint(pts){return pts.reduce((sum,pt)=>sum.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range(num,start,step){return Array(num).fill(0).map((_,i)=>start+i*step)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min,x)=>x<min?x:min,Infinity),minY=ys.reduce((min,y)=>y<min?y:min,Infinity),maxX=xs.reduce((max,x)=>max<x?x:max,0),maxY=ys.reduce((max,y)=>max<y?y:max,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}var tf2=__toModule(require_tfjs_esm());function normalize(x,meanRgb){return tf2.tidy(()=>{let[r,g,b]=meanRgb,avg_r=tf2.fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=tf2.fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=tf2.fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=tf2.concat([avg_r,avg_g,avg_b],3);return tf2.sub(x,avg_rgb)})}var tf3=__toModule(require_tfjs_esm());function padToSquare(imgTensor,isCenterImage=!1){return tf3.tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,tf3.fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>tf3.cast(t,"float32"));return tf3.concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array=inputArray.slice();for(let i=array.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array[i];array[i]=array[j],array[j]=x}return array}function sigmoid(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json){let descriptors=json.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch=window.fetch;if(!fetch)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer)=>err?reject(err):resolve(buffer))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch,...fileSystem}}function isBrowser(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env2){environment=env2}function initialize(){return isBrowser()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env2){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env2;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env2.createCanvasElement||(()=>new Canvas),environment.createImageElement=env2.createImageElement||(()=>new Image),environment.ImageData=env2.ImageData||environment.ImageData,environment.Video=env2.Video||environment.Video,environment.fetch=env2.fetch||environment.fetch,environment.readFile=env2.readFile||environment.readFile}var env={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}var tf18=__toModule(require_tfjs_esm());function isMediaLoaded(media){let{Image,Video}=env.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input){let{Image,Video}=env.getEnv();return input instanceof Image?new Dimensions(input.naturalWidth,input.naturalHeight):input instanceof Video?new Dimensions(input.videoWidth,input.videoHeight):new Dimensions(input.width,input.height)}function createCanvas({width,height}){let{createCanvasElement}=env.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}var tf4=__toModule(require_tfjs_esm());async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tf4.tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await tf4.browser.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input){let{Image,Canvas,Video}=env.getEnv();return input instanceof Image||input instanceof Canvas||input instanceof Video}var tf5=__toModule(require_tfjs_esm());function imageToSquare(input,inputSize,centerImage=!1){let{Image,Canvas}=env.getEnv();if(!(input instanceof Image||input instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input),scale2=inputSize/Math.max(dims.height,dims.width),width=scale2*dims.width,height=scale2*dims.height,targetCanvas=createCanvas({width:inputSize,height:inputSize}),inputCanvas=input instanceof Canvas?input:createCanvasFromMedia(input),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input,idx)=>{if(isTensor3D(input)){this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape;return}if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape.slice(1);return}let canvas=input instanceof env.getEnv().Canvas?input:createCanvasFromMedia(input);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tf5.tidy(()=>{let inputTensors=range(this.batchSize,0,1).map(batchIdx=>{let input=this.getInput(batchIdx);if(input instanceof tf5.Tensor){let imgTensor=isTensor4D(input)?input:input.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=tf5.image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input instanceof env.getEnv().Canvas)return tf5.browser.fromPixels(imageToSquare(input,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)});return tf5.stack(inputTensors.map(t=>tf5.cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input,i)=>{if(!isMediaElement(input)&&!isTensor3D(input)&&!isTensor4D(input))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input=>isMediaElement(input)&&awaitMediaLoaded(input))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input,detections){let{Canvas}=env.getEnv(),canvas=input;if(!(input instanceof Canvas)){let netInput=await toNetInput(input);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}var tf6=__toModule(require_tfjs_esm());async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tf6.tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>tf6.slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init){let{fetch}=env.getEnv(),res=await fetch(url,init);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}var tf7=__toModule(require_tfjs_esm());function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return tf7.io.loadWeights(manifest,modelBaseUri)}function matchDimensions(input,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input.width=width,input.height=height,{width,height}}var tf15=__toModule(require_tfjs_esm()),tf8=__toModule(require_tfjs_esm()),NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof tf8.Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof tf8.Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable})=>{let tensor2=tf8.tensor(variable.dataSync());variable.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights=tf8.io.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof tf8.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}},tf10=__toModule(require_tfjs_esm()),tf9=__toModule(require_tfjs_esm());function depthwiseSeparableConv(x,params,stride){return tf9.tidy(()=>{let out=tf9.separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=tf9.add(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=tf10.relu(tf10.add(out1,tf10.add(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,tf10.add(out3,out4))))})}var tf11=__toModule(require_tfjs_esm());function convLayer(x,params,padding="same",withRelu=!1){return tf11.tidy(()=>{let out=tf11.add(tf11.conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?tf11.relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}var tf12=__toModule(require_tfjs_esm());function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tf12.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf12.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}var tf13=__toModule(require_tfjs_esm());function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tf13.tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tf13.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var tf14=__toModule(require_tfjs_esm()),SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tf14.tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tf14.tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tf14.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tf15.tidy(()=>{let batchTensor=tf15.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf15.scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=tf15.avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}},tf17=__toModule(require_tfjs_esm()),tf16=__toModule(require_tfjs_esm());function fullyConnectedLayer(x,params){return tf16.tidy(()=>tf16.add(tf16.matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf17.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}},FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input){return tf18.tidy(()=>tf18.softmax(this.runNet(input)))}async forward(input){return this.forwardInput(await toNetInput(input))}async predictExpressions(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(tf18.unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version="0.10.1",tf20=__toModule(require_tfjs_esm()),tf19=__toModule(require_tfjs_esm());function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return tf19.add(tf19.conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?tf19.relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=tf19.maxPool(out,[3,3],[2,2],"same"),out=tf19.add(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(tf19.relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv2,[1,1]),out=tf19.add(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tf19.tidy(()=>{let batchTensor=tf19.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf19.scalar(256)),out=tf19.relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=tf19.relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf20.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input,pooled=tf20.avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input){return tf20.tidy(()=>{let{age,gender}=this.runNet(input);return{age,gender:tf20.softmax(gender)}})}async forward(input){return this.forwardInput(await toNetInput(input))}async predictAgeAndGender(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),ages=tf20.unstack(out.age),genders=tf20.unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var tf21=__toModule(require_tfjs_esm()),FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale2=inputSize/Math.max(height,width);return{width:width*scale2,height:height*scale2}}),batchSize=inputDimensions.length;return tf21.tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>tf21.stack([tf21.fill([68],fillX,"float32"),tf21.fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding(batchIdx,(w,h)=>h<w);return output.mul(tf21.fill([batchSize,136],inputSize,"float32")).sub(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input){return tf21.tidy(()=>{let out=this.runNet(input);return this.postProcess(out,input.inputSize,input.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input){return this.forwardInput(await toNetInput(input))}async detectLandmarks(input){let netInput=await toNetInput(input),landmarkTensors=tf21.tidy(()=>tf21.unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven(i)),yCoords=landmarksArray.filter((_,i)=>!isEven(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var tf22=__toModule(require_tfjs_esm());function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tf22.tidy(()=>{let batchTensor=tf22.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf22.scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=tf22.avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};var tf27=__toModule(require_tfjs_esm()),tf24=__toModule(require_tfjs_esm()),tf23=__toModule(require_tfjs_esm());function scale(x,params){return tf23.add(tf23.mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=tf24.conv2d(x,filters,strides,padding);return out=tf24.add(out,bias),out=scale(out,params.scale),withRelu?tf24.relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}var tf25=__toModule(require_tfjs_esm());function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tf25.tidy(()=>tf25.transpose(tf25.tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tf25.tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tf25.tensor1d(extractWeights(numWeights)),biases=tf25.tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale2=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale2}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tf25.tidy(()=>tf25.transpose(tf25.tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale2=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale2}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf26=__toModule(require_tfjs_esm());function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=tf26.add(out,x),out=tf26.relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=tf26.avgPool(x,2,2,"valid"),zeros2=tf26.zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=tf26.zeros(padShapeX);out=tf26.concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=tf26.zeros(padShapeY);out=tf26.concat([out,zerosH],2)}return pooled=isPad?tf26.concat([pooled,zeros2],3):pooled,out=tf26.add(pooled,out),out=tf26.relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tf27.tidy(()=>{let batchTensor=tf27.cast(input.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf27.scalar(256)),out=convDown(normalized,params.conv32_down);out=tf27.maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return tf27.matMul(globalAvg,params.fc)})}async forward(input){return this.forwardInput(await toNetInput(input))}async computeFaceDescriptor(input){let netInput=await toNetInput(input),faceDescriptorTensors=tf27.tidy(()=>tf27.unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}var tf34=__toModule(require_tfjs_esm()),tf28=__toModule(require_tfjs_esm());function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tf28.tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tf28.tensor1d(extractWeights(numChannels)),batch_norm_offset=tf28.tensor1d(extractWeights(numChannels)),batch_norm_mean=tf28.tensor1d(extractWeights(numChannels)),batch_norm_variance=tf28.tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tf28.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf28.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tf28.tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf30=__toModule(require_tfjs_esm()),tf29=__toModule(require_tfjs_esm());function pointwiseConvLayer(x,params,strides){return tf29.tidy(()=>{let out=tf29.conv2d(x,params.filters,strides,"same");return out=tf29.add(out,params.batch_norm_offset),tf29.clipByValue(out,0,6)})}var epsilon=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tf30.tidy(()=>{let out=tf30.depthwiseConv2d(x,params.filters,strides,"same");return out=tf30.batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon),tf30.clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tf30.tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(!(selected.length>=outputSize)){let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}}),selected}var tf31=__toModule(require_tfjs_esm());function getCenterCoordinatesAndSizesLayer(x){let vec=tf31.unstack(tf31.transpose(x,[1,0])),sizes=[tf31.sub(vec[2],vec[0]),tf31.sub(vec[3],vec[1])],centers=[tf31.add(vec[0],tf31.div(sizes[0],tf31.scalar(2))),tf31.add(vec[1],tf31.div(sizes[1],tf31.scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=tf31.unstack(tf31.transpose(x1,[1,0])),div0_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2],tf31.scalar(5))),sizes[0]),tf31.scalar(2)),add0_out=tf31.add(tf31.mul(tf31.div(vec[0],tf31.scalar(10)),sizes[0]),centers[0]),div1_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3],tf31.scalar(5))),sizes[1]),tf31.scalar(2)),add1_out=tf31.add(tf31.mul(tf31.div(vec[1],tf31.scalar(10)),sizes[1]),centers[1]);return tf31.transpose(tf31.stack([tf31.sub(add0_out,div0_out),tf31.sub(add1_out,div1_out),tf31.add(add0_out,div0_out),tf31.add(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tf31.tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(tf31.reshape(tf31.tile(params.extra_dim,[batchSize,1,1]),[-1,4]),tf31.reshape(boxPredictions,[-1,4]));boxes=tf31.reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=tf31.sigmoid(tf31.slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=tf31.slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=tf31.reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=tf31.unstack(boxes),scoresByBatch=tf31.unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}var tf33=__toModule(require_tfjs_esm()),tf32=__toModule(require_tfjs_esm());function boxPredictionLayer(x,params){return tf32.tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=tf32.reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=tf32.reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tf33.tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=tf33.concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=tf33.concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tf34.tidy(()=>{let batchTensor=tf34.cast(input.toBatchTensor(512,!1),"float32"),x=tf34.sub(tf34.mul(batchTensor,tf34.scalar(.007843137718737125)),tf34.scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input){return this.forwardInput(await toNetInput(input))}async locateFaces(input,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression2(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",tf39=__toModule(require_tfjs_esm()),isNumber=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber(a.x)&&isNumber(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}var tf36=__toModule(require_tfjs_esm()),tf35=__toModule(require_tfjs_esm());function leaky(x){return tf35.tidy(()=>{let min=tf35.mul(x,tf35.scalar(.10000000149011612));return tf35.add(tf35.relu(tf35.sub(x,min)),min)})}function convWithBatchNorm(x,params){return tf36.tidy(()=>{let out=tf36.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf36.conv2d(out,params.conv.filters,[1,1],"valid"),out=tf36.sub(out,params.bn.sub),out=tf36.mul(out,params.bn.truediv),out=tf36.add(out,params.conv.bias),leaky(out)})}var tf37=__toModule(require_tfjs_esm());function depthwiseSeparableConv2(x,params){return tf37.tidy(()=>{let out=tf37.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf37.separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=tf37.add(out,params.bias),leaky(out)})}var tf38=__toModule(require_tfjs_esm());function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub6=tf38.tensor1d(extractWeights(size)),truediv=tf38.tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub6,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub6=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub6,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}},TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(x,params){let out=convWithBatchNorm(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tf39.tidy(()=>{let batchTensor=tf39.cast(input.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(tf39.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input,inputSize){return this.forwardInput(await toNetInput(input),inputSize)}async detect(input,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input),out=await this.forwardInput(netInput,inputSize),out0=tf39.tidy(()=>tf39.unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tf39.tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?tf39.softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):tf39.scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max,curr)=>max.classScore>curr.classScore?max:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},tf41=__toModule(require_tfjs_esm()),tf40=__toModule(require_tfjs_esm());async function extractAllFacesAndComputeResults(parentResults,input,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input instanceof tf40.Tensor?await extractFaceTensors(input,faceBoxes):await extractFaces(input,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof tf40.Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}},nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input,options)=>nets.ssdMobilenetv1.locateFaces(input,options),tinyFaceDetector=(input,options)=>nets.tinyFaceDetector.locateFaces(input,options),tinyYolov2=(input,options)=>nets.tinyYolov2.locateFaces(input,options),detectFaceLandmarks=input=>nets.faceLandmark68Net.detectLandmarks(input),detectFaceLandmarksTiny=input=>nets.faceLandmark68TinyNet.detectLandmarks(input),computeFaceDescriptor=input=>nets.faceRecognitionNet.computeFaceDescriptor(input),recognizeFaceExpressions=input=>nets.faceExpressionNet.predictExpressions(input),predictAgeAndGender=input=>nets.ageGenderNet.predictAgeAndGender(input),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input){super();this.parentTask=parentTask;this.input=input}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!!parentResult){let{detection}=parentResult,faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input,options=new SsdMobilenetv1Options){super();this.input=input;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input2=>nets.tinyFaceDetector.locateFaces(input2,options):options instanceof SsdMobilenetv1Options?input2=>nets.ssdMobilenetv1.locateFaces(input2,options):options instanceof TinyYolov2Options?input2=>nets.tinyYolov2.locateFaces(input2,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input,options)}function detectAllFaces(input,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input,options)}async function allFacesSsdMobilenetv1(input,minConfidence){return detectAllFaces(input,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input,forwardParams={}){return detectAllFaces(input,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count=1,createUniqueLabel=()=>`person ${count++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json){let labeledDescriptors=json.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser3=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version2={faceapi:version,node,browser:browser3};
+var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2);var require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node")))});var require_isNodejs=__commonJS((exports2,module2)=>{__export(exports2,{isNodejs:()=>isNodejs2});function isNodejs2(){return typeof global=="object"&&!0&&typeof module2!="undefined"&&typeof process!="undefined"&&!!process.version}});__export(exports,{AgeGenderNet:()=>AgeGenderNet,BoundingBox:()=>BoundingBox,Box:()=>Box,ComposableTask:()=>ComposableTask,ComputeAllFaceDescriptorsTask:()=>ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase:()=>ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask:()=>ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask:()=>DetectAllFaceLandmarksTask,DetectAllFacesTask:()=>DetectAllFacesTask,DetectFaceLandmarksTaskBase:()=>DetectFaceLandmarksTaskBase,DetectFacesTaskBase:()=>DetectFacesTaskBase,DetectSingleFaceLandmarksTask:()=>DetectSingleFaceLandmarksTask,DetectSingleFaceTask:()=>DetectSingleFaceTask,Dimensions:()=>Dimensions,FACE_EXPRESSION_LABELS:()=>FACE_EXPRESSION_LABELS,FaceDetection:()=>FaceDetection,FaceDetectionNet:()=>FaceDetectionNet,FaceExpressionNet:()=>FaceExpressionNet,FaceExpressions:()=>FaceExpressions,FaceLandmark68Net:()=>FaceLandmark68Net,FaceLandmark68TinyNet:()=>FaceLandmark68TinyNet,FaceLandmarkNet:()=>FaceLandmarkNet,FaceLandmarks:()=>FaceLandmarks,FaceLandmarks5:()=>FaceLandmarks5,FaceLandmarks68:()=>FaceLandmarks68,FaceMatch:()=>FaceMatch,FaceMatcher:()=>FaceMatcher,FaceRecognitionNet:()=>FaceRecognitionNet,Gender:()=>Gender,LabeledBox:()=>LabeledBox,LabeledFaceDescriptors:()=>LabeledFaceDescriptors,NetInput:()=>NetInput,NeuralNetwork:()=>NeuralNetwork,ObjectDetection:()=>ObjectDetection,Point:()=>Point,PredictedBox:()=>PredictedBox,Rect:()=>Rect,SsdMobilenetv1:()=>SsdMobilenetv1,SsdMobilenetv1Options:()=>SsdMobilenetv1Options,TinyFaceDetector:()=>TinyFaceDetector,TinyFaceDetectorOptions:()=>TinyFaceDetectorOptions,TinyYolov2:()=>TinyYolov2,TinyYolov2Options:()=>TinyYolov2Options,allFaces:()=>allFaces,allFacesSsdMobilenetv1:()=>allFacesSsdMobilenetv1,allFacesTinyYolov2:()=>allFacesTinyYolov2,awaitMediaLoaded:()=>awaitMediaLoaded,bufferToImage:()=>bufferToImage,computeFaceDescriptor:()=>computeFaceDescriptor,createCanvas:()=>createCanvas,createCanvasFromMedia:()=>createCanvasFromMedia,createFaceDetectionNet:()=>createFaceDetectionNet,createFaceRecognitionNet:()=>createFaceRecognitionNet,createSsdMobilenetv1:()=>createSsdMobilenetv1,createTinyFaceDetector:()=>createTinyFaceDetector,createTinyYolov2:()=>createTinyYolov2,detectAllFaces:()=>detectAllFaces,detectFaceLandmarks:()=>detectFaceLandmarks,detectFaceLandmarksTiny:()=>detectFaceLandmarksTiny,detectLandmarks:()=>detectLandmarks,detectSingleFace:()=>detectSingleFace,draw:()=>draw_exports,env:()=>env,euclideanDistance:()=>euclideanDistance,extendWithAge:()=>extendWithAge,extendWithFaceDescriptor:()=>extendWithFaceDescriptor,extendWithFaceDetection:()=>extendWithFaceDetection,extendWithFaceExpressions:()=>extendWithFaceExpressions,extendWithFaceLandmarks:()=>extendWithFaceLandmarks,extendWithGender:()=>extendWithGender,extractFaceTensors:()=>extractFaceTensors,extractFaces:()=>extractFaces,fetchImage:()=>fetchImage,fetchJson:()=>fetchJson,fetchNetWeights:()=>fetchNetWeights,fetchOrThrow:()=>fetchOrThrow,getContext2dOrThrow:()=>getContext2dOrThrow,getMediaDimensions:()=>getMediaDimensions,imageTensorToCanvas:()=>imageTensorToCanvas,imageToSquare:()=>imageToSquare,inverseSigmoid:()=>inverseSigmoid,iou:()=>iou,isMediaElement:()=>isMediaElement,isMediaLoaded:()=>isMediaLoaded,isWithAge:()=>isWithAge,isWithFaceDetection:()=>isWithFaceDetection,isWithFaceExpressions:()=>isWithFaceExpressions,isWithFaceLandmarks:()=>isWithFaceLandmarks,isWithGender:()=>isWithGender,loadAgeGenderModel:()=>loadAgeGenderModel,loadFaceDetectionModel:()=>loadFaceDetectionModel,loadFaceExpressionModel:()=>loadFaceExpressionModel,loadFaceLandmarkModel:()=>loadFaceLandmarkModel,loadFaceLandmarkTinyModel:()=>loadFaceLandmarkTinyModel,loadFaceRecognitionModel:()=>loadFaceRecognitionModel,loadSsdMobilenetv1Model:()=>loadSsdMobilenetv1Model,loadTinyFaceDetectorModel:()=>loadTinyFaceDetectorModel,loadTinyYolov2Model:()=>loadTinyYolov2Model,loadWeightMap:()=>loadWeightMap,locateFaces:()=>locateFaces,matchDimensions:()=>matchDimensions,minBbox:()=>minBbox,nets:()=>nets,nonMaxSuppression:()=>nonMaxSuppression,normalize:()=>normalize,padToSquare:()=>padToSquare,predictAgeAndGender:()=>predictAgeAndGender,recognizeFaceExpressions:()=>recognizeFaceExpressions,resizeResults:()=>resizeResults,resolveInput:()=>resolveInput,shuffleArray:()=>shuffleArray,sigmoid:()=>sigmoid,ssdMobilenetv1:()=>ssdMobilenetv1,tf:()=>tf42,tinyFaceDetector:()=>tinyFaceDetector,tinyYolov2:()=>tinyYolov2,toNetInput:()=>toNetInput,utils:()=>utils_exports,validateConfig:()=>validateConfig,version:()=>version2});var tf42=__toModule(require_tfjs_esm());var draw_exports={};__export(draw_exports,{AnchorPosition:()=>AnchorPosition,DrawBox:()=>DrawBox,DrawBoxOptions:()=>DrawBoxOptions,DrawFaceLandmarks:()=>DrawFaceLandmarks,DrawFaceLandmarksOptions:()=>DrawFaceLandmarksOptions,DrawTextField:()=>DrawTextField,DrawTextFieldOptions:()=>DrawTextFieldOptions,drawContour:()=>drawContour,drawDetections:()=>drawDetections,drawFaceExpressions:()=>drawFaceExpressions,drawFaceLandmarks:()=>drawFaceLandmarks});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range,round:()=>round});var tf=__toModule(require_tfjs_esm());var Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof tf.Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!=0}function isEven(num){return num%2==0}function round(num,prec=2){let f=10**prec;return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale2=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale2),Math.round(height*scale2))}function getCenterPoint(pts){return pts.reduce((sum,pt)=>sum.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range(num,start,step){return Array(num).fill(0).map((_,i)=>start+i*step)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!Number.isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&num>=0&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}};var Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);if(!allowNegativeDimensions&&(box.width<0||box.height<0))throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),isRect=[box.x,box.y,box.width,box.height].every(isValidNumber);if(!isRect&&!isBbox)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);let[x,y,width,height]=isRect?[box.x,box.y,box.width,box.height]:[box.left,box.top,box.right-box.left,box.bottom-box.top];Box.assertIsValidBox({x,y,width,height},"Box.constructor",allowNegativeDimensions),this._x=x,this._y=y,this._width=width,this._height=height}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Point(this.left,this.top)}get topRight(){return new Point(this.right,this.top)}get bottomLeft(){return new Point(this.left,this.bottom)}get bottomRight(){return new Point(this.right,this.bottom)}round(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.round(val));return new Box({x,y,width,height})}floor(){let[x,y,width,height]=[this.x,this.y,this.width,this.height].map(val=>Math.floor(val));return new Box({x,y,width,height})}toSquare(){let{x,y,width,height}=this,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,scaleY=isDimensions(s)?s.height:s;return new Box({x:this.x*scaleX,y:this.y*scaleY,width:this.width*scaleX,height:this.height*scaleY})}pad(padX,padY){let[x,y,width,height]=[this.x-padX/2,this.y-padY/2,this.width+padX,this.height+padY];return new Box({x,y,width,height})}clipAtImageBorders(imgWidth,imgHeight){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{dy,edy,dx,edx,y,ey,x,ex,w,h}}calibrate(region){return new Box({left:this.left+region.left*this.width,top:this.top+region.top*this.height,right:this.right+region.right*this.width,bottom:this.bottom+region.bottom*this.height}).toSquare().round()}};var BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{constructor(score,classScore,className,relativeBox,imageDims){this._imageDims=new Dimensions(imageDims.width,imageDims.height),this._score=score,this._classScore=classScore,this._className=className,this._box=new Box(relativeBox).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Box(this._box).rescale(this.imageDims.reverse())}forSize(width,height){return new ObjectDetection(this.score,this.classScore,this.className,this.relativeBox,{width,height})}};var FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min,x)=>x<min?x:min,Infinity),minY=ys.reduce((min,y)=>y<min?y:min,Infinity),maxX=xs.reduce((max,x)=>max<x?x:max,0),maxY=ys.reduce((max,y)=>max<y?y:max,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}var tf2=__toModule(require_tfjs_esm());function normalize(x,meanRgb){return tf2.tidy(()=>{let[r,g,b]=meanRgb,avg_r=tf2.fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=tf2.fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=tf2.fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=tf2.concat([avg_r,avg_g,avg_b],3);return tf2.sub(x,avg_rgb)})}var tf3=__toModule(require_tfjs_esm());function padToSquare(imgTensor,isCenterImage=!1){return tf3.tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmountLocal=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmountLocal,tf3.fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],tensorsToStack=[isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>tf3.cast(t,"float32"));return tf3.concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array=inputArray.slice();for(let i=array.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array[i];array[i]=array[j],array[j]=x}return array}function sigmoid(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}};var relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{width,height}=imgDims;this._imgDims=new Dimensions(width,height),this._shift=shift,this._positions=relativeFaceLandmarkPositions.map(pt=>pt.mul(new Point(width,height)).add(shift))}get shift(){return new Point(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(pt=>pt.sub(this._shift).div(new Point(this.imageWidth,this.imageHeight)))}forSize(width,height){return new this.constructor(this.relativePositions,{width,height})}shiftBy(x,y){return new this.constructor(this.relativePositions,this._imgDims,new Point(x,y))}shiftByPoint(pt){return this.shiftBy(pt.x,pt.y)}align(detection,options={}){if(detection){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}={useDlibAlignment:!1,minBoxPadding:.2,...options};return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),y=Math.floor(Math.max(0,refPoint.y-relY*size));return new Rect(x,y,Math.min(size,this.imageWidth+x),Math.min(size,this.imageHeight+y))}alignMinBbox(padding){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class extends FaceLandmarks{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(getCenterPoint)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{constructor(label,descriptors){if(typeof label!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(descriptors)||descriptors.some(desc=>!(desc instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=label,this._descriptors=descriptors}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(d=>Array.from(d))}}static fromJSON(json){let descriptors=json.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!isValidProbablitiy(box.score)||!isValidProbablitiy(box.classScore))throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){return{...sourceObj,...{detection}}}function createBrowserEnv(){let fetch=window.fetch;if(!fetch)throw new Error("fetch - missing fetch implementation for browser environment");let readFile=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}return{readFile:fs?filePath=>new Promise((resolve,reject)=>{fs.readFile(filePath,(err,buffer)=>err?reject(err):resolve(buffer))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)}}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=()=>{if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=()=>{if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch=global.fetch,fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch,...fileSystem}}function isBrowser(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var import_isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env2){environment=env2}function initialize(){return isBrowser()?setEnv(createBrowserEnv()):import_isNodejs.isNodejs()?setEnv(createNodejsEnv()):null}function monkeyPatch(env2){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env2;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env2.createCanvasElement||(()=>new Canvas),environment.createImageElement=env2.createImageElement||(()=>new Image),environment.ImageData=env2.ImageData||environment.ImageData,environment.Video=env2.Video||environment.Video,environment.fetch=env2.fetch||environment.fetch,environment.readFile=env2.readFile||environment.readFile}var env={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser,isNodejs:import_isNodejs.isNodejs};initialize();function resolveInput(arg){return!env.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}var AnchorPosition;(function(AnchorPosition2){AnchorPosition2.TOP_LEFT="TOP_LEFT",AnchorPosition2.TOP_RIGHT="TOP_RIGHT",AnchorPosition2.BOTTOM_LEFT="BOTTOM_LEFT",AnchorPosition2.BOTTOM_RIGHT="BOTTOM_RIGHT"})(AnchorPosition||(AnchorPosition={}));var DrawTextFieldOptions=class{constructor(options={}){let{anchorPosition,backgroundColor,fontColor,fontSize,fontStyle,padding}=options;this.anchorPosition=anchorPosition||AnchorPosition.TOP_LEFT,this.backgroundColor=backgroundColor||"rgba(0, 0, 0, 0.5)",this.fontColor=fontColor||"rgba(255, 255, 255, 1)",this.fontSize=fontSize||14,this.fontStyle=fontStyle||"Georgia",this.padding=padding||4}},DrawTextField=class{constructor(text,anchor,options={}){this.text=typeof text=="string"?[text]:text instanceof DrawTextField?text.text:text,this.anchor=anchor,this.options=new DrawTextFieldOptions(options)}measureWidth(ctx){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}};var DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions({...defaultDrawLabelOptions,...drawLabelOptions})}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){(Array.isArray(detections)?detections:[detections]).forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}var tf18=__toModule(require_tfjs_esm());function isMediaLoaded(media){let{Image,Video}=env.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env.getEnv().Canvas||isMediaLoaded(media))return resolve(null);function onError(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),reject(e))}function onLoad(e){!e.currentTarget||(e.currentTarget.removeEventListener("load",onLoad),e.currentTarget.removeEventListener("error",onError),resolve(e))}media.addEventListener("load",onLoad),media.addEventListener("error",onError)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{buf instanceof Blob||reject(new Error("bufferToImage - expected buf to be of type: Blob"));let reader=new FileReader;reader.onload=()=>{typeof reader.result!="string"&&reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));let img=env.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input){let{Image,Video}=env.getEnv();return input instanceof Image?new Dimensions(input.naturalWidth,input.naturalHeight):input instanceof Video?new Dimensions(input.videoWidth,input.videoHeight):new Dimensions(input.width,input.height)}function createCanvas({width,height}){let{createCanvasElement}=env.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}var tf4=__toModule(require_tfjs_esm());async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tf4.tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await tf4.browser.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input){let{Image,Canvas,Video}=env.getEnv();return input instanceof Image||input instanceof Canvas||input instanceof Video}var tf5=__toModule(require_tfjs_esm());function imageToSquare(input,inputSize,centerImage=!1){let{Image,Canvas}=env.getEnv();if(!(input instanceof Image||input instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input),scale2=inputSize/Math.max(dims.height,dims.width),width=scale2*dims.width,height=scale2*dims.height,targetCanvas=createCanvas({width:inputSize,height:inputSize}),inputCanvas=input instanceof Canvas?input:createCanvasFromMedia(input),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(inputs))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);this._treatAsBatchInput=treatAsBatchInput,this._batchSize=inputs.length,inputs.forEach((input,idx)=>{if(isTensor3D(input)){this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape;return}if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input,this._inputDimensions[idx]=input.shape.slice(1);return}let canvas=input instanceof env.getEnv().Canvas?input:createCanvasFromMedia(input);this._canvases[idx]=canvas,this._inputDimensions[idx]=[canvas.height,canvas.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return range(this.batchSize,0,1).map((_,batchIdx)=>this.getReshapedInputDimensions(batchIdx))}getInput(batchIdx){return this.canvases[batchIdx]||this.imageTensors[batchIdx]}getInputDimensions(batchIdx){return this._inputDimensions[batchIdx]}getInputHeight(batchIdx){return this._inputDimensions[batchIdx][0]}getInputWidth(batchIdx){return this._inputDimensions[batchIdx][1]}getReshapedInputDimensions(batchIdx){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tf5.tidy(()=>{let inputTensors=range(this.batchSize,0,1).map(batchIdx=>{let input=this.getInput(batchIdx);if(input instanceof tf5.Tensor){let imgTensor=isTensor4D(input)?input:input.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=tf5.image.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input instanceof env.getEnv().Canvas)return tf5.browser.fromPixels(imageToSquare(input,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)});return tf5.stack(inputTensors.map(t=>tf5.cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3)})}};async function toNetInput(inputs){if(inputs instanceof NetInput)return inputs;let inputArgArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArgArray.length)throw new Error("toNetInput - empty array passed as input");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input,i)=>{if(!isMediaElement(input)&&!isTensor3D(input)&&!isTensor4D(input))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input=>isMediaElement(input)&&awaitMediaLoaded(input))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input,detections){let{Canvas}=env.getEnv(),canvas=input;if(!(input instanceof Canvas)){let netInput=await toNetInput(input);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas);return detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height)).map(({x,y,width,height})=>{let faceImg=createCanvas({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}var tf6=__toModule(require_tfjs_esm());async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tf6.tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0);return detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)).map(({x,y,width,height})=>tf6.slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]))})}async function fetchOrThrow(url,init){let{fetch}=env.getEnv(),res=await fetch(url,init);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),blob=await res.blob();if(!blob.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}var tf7=__toModule(require_tfjs_esm());function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return tf7.io.loadWeights(manifest,modelBaseUri)}function matchDimensions(input,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input.width=width,input.height=height,{width,height}}var tf15=__toModule(require_tfjs_esm());var tf8=__toModule(require_tfjs_esm());var NeuralNetwork=class{constructor(){this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(paramPath){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor2}getParamList(){return this._paramMappings.map(({paramPath})=>({path:paramPath,tensor:this.getParamFromPath(paramPath)}))}getTrainableParams(){return this.getParamList().filter(param=>param.tensor instanceof tf8.Variable)}getFrozenParams(){return this.getParamList().filter(param=>!(param.tensor instanceof tf8.Variable))}variable(){this.getFrozenParams().forEach(({path,tensor:tensor2})=>{this.reassignParamFromPath(path,tensor2.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable})=>{let tensor2=tf8.tensor(variable.dataSync());variable.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor2})=>Array.from(tensor2.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let weightMap=await loadWeightMap(uri,this.getDefaultModelName());this.loadFromWeightMap(weightMap)}async loadFromDisk(filePath){if(filePath&&typeof filePath!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile}=env.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(fp=>readFile(fp).then(buf=>buf.buffer))),loadWeights=tf8.io.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{paramMappings,params}=this.extractParams(weights);this._paramMappings=paramMappings,this._params=params}traversePropertyPath(paramPath){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let result=paramPath.split("/").reduce((res,objProp2)=>{if(!res.nextObj.hasOwnProperty(objProp2))throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);return{obj:res.nextObj,objProp:objProp2,nextObj:res.nextObj[objProp2]}},{nextObj:this.params}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof tf8.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}};var tf10=__toModule(require_tfjs_esm());var tf9=__toModule(require_tfjs_esm());function depthwiseSeparableConv(x,params,stride){return tf9.tidy(()=>{let out=tf9.separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=tf9.add(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=tf10.relu(tf10.add(out1,tf10.add(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,tf10.add(out3,out4))))})}var tf11=__toModule(require_tfjs_esm());function convLayer(x,params,padding="same",withRelu=!1){return tf11.tidy(()=>{let out=tf11.add(tf11.conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?tf11.relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}var tf12=__toModule(require_tfjs_esm());function extractConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,filterSize,mappedPrefix)=>{let filters=tf12.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf12.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}var tf13=__toModule(require_tfjs_esm());function extractFCParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let fc_weights=tf13.tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tf13.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var tf14=__toModule(require_tfjs_esm());var SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return(channelsIn,channelsOut,mappedPrefix)=>{let depthwise_filter=tf14.tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tf14.tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tf14.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return prefix=>{let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return(originalPath,paramRank,mappedPath)=>{let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return prefix=>{let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tf15.tidy(()=>{let batchTensor=tf15.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf15.scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=tf15.avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}};var tf17=__toModule(require_tfjs_esm());var tf16=__toModule(require_tfjs_esm());function fullyConnectedLayer(x,params){return tf16.tidy(()=>tf16.add(tf16.matMul(x,params.weights),params.bias))}function extractParams2(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),fc=extractFCParamsFactory(extractWeights,paramMappings)(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap2(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf17.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams2(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap2(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{constructor(probabilities){if(probabilities.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);FACE_EXPRESSION_LABELS.forEach((expression,idx)=>{this[expression]=probabilities[idx]})}asSortedArray(){return FACE_EXPRESSION_LABELS.map(expression=>({expression,probability:this[expression]})).sort((e0,e1)=>e1.probability-e0.probability)}};var FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input){return tf18.tidy(()=>tf18.softmax(this.runNet(input)))}async forward(input){return this.forwardInput(await toNetInput(input))}async predictExpressions(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(tf18.unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){return{...sourceObj,...{expressions}}}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){(Array.isArray(faceExpressions)?faceExpressions:[faceExpressions]).forEach(e=>{let expr=e instanceof FaceExpressions?e:isWithFaceExpressions(e)?e.expressions:void 0;if(!expr)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let resultsToDisplay=expr.asSortedArray().filter(exprLocal=>exprLocal.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0);new DrawTextField(resultsToDisplay.map(exprLocal=>`${exprLocal.expression} (${round(exprLocal.probability)})`),anchor).draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims);return{...sourceObj,...{landmarks,unshiftedLandmarks,alignedRect}}}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,lineWidth,lineColor,pointSize,pointColor}=options;this.drawLines=drawLines,this.drawPoints=drawPoints,this.lineWidth=lineWidth||1,this.pointSize=pointSize||2,this.lineColor=lineColor||"rgba(0, 255, 255, 1)",this.pointColor=pointColor||"rgba(255, 0, 255, 1)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{drawLines,drawPoints,lineWidth,lineColor,pointSize,pointColor}=this.options;if(drawLines&&this.faceLandmarks instanceof FaceLandmarks68&&(ctx.strokeStyle=lineColor,ctx.lineWidth=lineWidth,drawContour(ctx,this.faceLandmarks.getJawOutline()),drawContour(ctx,this.faceLandmarks.getLeftEyeBrow()),drawContour(ctx,this.faceLandmarks.getRightEyeBrow()),drawContour(ctx,this.faceLandmarks.getNose()),drawContour(ctx,this.faceLandmarks.getLeftEye(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let drawPoint=pt=>{ctx.beginPath(),ctx.arc(pt.x,pt.y,pointSize,0,2*Math.PI),ctx.fill()};this.faceLandmarks.positions.forEach(drawPoint)}}};function drawFaceLandmarks(canvasArg,faceLandmarks){(Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks]).forEach(f=>{let landmarks=f instanceof FaceLandmarks?f:isWithFaceLandmarks(f)?f.landmarks:void 0;if(!landmarks)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new DrawFaceLandmarks(landmarks).draw(canvasArg)})}var version="0.10.2";var tf20=__toModule(require_tfjs_esm());var tf19=__toModule(require_tfjs_esm());function extractorsFactory2(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams3(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory2(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory2(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap3(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory2(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return tf19.add(tf19.conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?tf19.relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=tf19.maxPool(out,[3,3],[2,2],"same"),out=tf19.add(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(tf19.relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv2,[1,1]),out=tf19.add(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tf19.tidy(()=>{let batchTensor=tf19.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf19.scalar(256)),out=tf19.relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=tf19.relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap3(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams3(weights,this._numMainBlocks)}};function extractParams4(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap4(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf20.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input,pooled=tf20.avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input){return tf20.tidy(()=>{let{age,gender}=this.runNet(input);return{age,gender:tf20.softmax(gender)}})}async forward(input){return this.forwardInput(await toNetInput(input))}async predictAgeAndGender(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),ages=tf20.unstack(out.age),genders=tf20.unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams4(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap4(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var tf21=__toModule(require_tfjs_esm());var FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale2=inputSize/Math.max(height,width);return{width:width*scale2,height:height*scale2}}),batchSize=inputDimensions.length;return tf21.tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>tf21.stack([tf21.fill([68],fillX,"float32"),tf21.fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding(batchIdx,(w,h)=>h<w);return output.mul(tf21.fill([batchSize,136],inputSize,"float32")).sub(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))))})}forwardInput(input){return tf21.tidy(()=>{let out=this.runNet(input);return this.postProcess(out,input.inputSize,input.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input){return this.forwardInput(await toNetInput(input))}async detectLandmarks(input){let netInput=await toNetInput(input),landmarkTensors=tf21.tidy(()=>tf21.unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven(i)),yCoords=landmarksArray.filter((_,i)=>!isEven(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}};var FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var tf22=__toModule(require_tfjs_esm());function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tf22.tidy(()=>{let batchTensor=tf22.cast(input.toBatchTensor(112,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf22.scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=tf22.avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input){return this.forwardInput(await toNetInput(input))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}};var FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var FaceLandmarkNet=class extends FaceLandmark68Net{};var tf27=__toModule(require_tfjs_esm());var tf24=__toModule(require_tfjs_esm());var tf23=__toModule(require_tfjs_esm());function scale(x,params){return tf23.add(tf23.mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=tf24.conv2d(x,filters,strides,padding);return out=tf24.add(out,bias),out=scale(out,params.scale),withRelu?tf24.relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}var tf25=__toModule(require_tfjs_esm());function extractorsFactory3(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),depth=weights.length/(numFilters*filterSize*filterSize);if(isFloat(depth))throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);return tf25.tidy(()=>tf25.transpose(tf25.tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tf25.tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tf25.tensor1d(extractWeights(numWeights)),biases=tf25.tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale2=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale2}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams5(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory3(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tf25.tidy(()=>tf25.transpose(tf25.tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc},paramMappings}}function extractorsFactory4(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale2=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale2}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap5(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory4(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),{fc}=weightMap;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf26=__toModule(require_tfjs_esm());function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=tf26.add(out,x),out=tf26.relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=tf26.avgPool(x,2,2,"valid"),zeros2=tf26.zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3];if(pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2]){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=tf26.zeros(padShapeX);out=tf26.concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=tf26.zeros(padShapeY);out=tf26.concat([out,zerosH],2)}return pooled=isPad?tf26.concat([pooled,zeros2],3):pooled,out=tf26.add(pooled,out),out=tf26.relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tf27.tidy(()=>{let batchTensor=tf27.cast(input.toBatchTensor(150,!0),"float32"),normalized=normalize(batchTensor,[122.782,117.001,104.298]).div(tf27.scalar(256)),out=convDown(normalized,params.conv32_down);out=tf27.maxPool(out,3,2,"valid"),out=residual(out,params.conv32_1),out=residual(out,params.conv32_2),out=residual(out,params.conv32_3),out=residualDown(out,params.conv64_down),out=residual(out,params.conv64_1),out=residual(out,params.conv64_2),out=residual(out,params.conv64_3),out=residualDown(out,params.conv128_down),out=residual(out,params.conv128_1),out=residual(out,params.conv128_2),out=residualDown(out,params.conv256_down),out=residual(out,params.conv256_1),out=residual(out,params.conv256_2),out=residualDown(out,params.conv256_down_out);let globalAvg=out.mean([1,2]);return tf27.matMul(globalAvg,params.fc)})}async forward(input){return this.forwardInput(await toNetInput(input))}async computeFaceDescriptor(input){let netInput=await toNetInput(input),faceDescriptorTensors=tf27.tidy(()=>tf27.unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap)}extractParams(weights){return extractParams5(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){return{...sourceObj,...{descriptor}}}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){return{...sourceObj,...{age}}}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){return{...sourceObj,...{gender,genderProbability}}}var tf34=__toModule(require_tfjs_esm());var tf28=__toModule(require_tfjs_esm());function extractorsFactory5(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tf28.tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tf28.tensor1d(extractWeights(numChannels)),batch_norm_offset=tf28.tensor1d(extractWeights(numChannels)),batch_norm_mean=tf28.tensor1d(extractWeights(numChannels)),batch_norm_variance=tf28.tensor1d(extractWeights(numChannels));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/batch_norm_scale`},{paramPath:`${mappedPrefix}/batch_norm_offset`},{paramPath:`${mappedPrefix}/batch_norm_mean`},{paramPath:`${mappedPrefix}/batch_norm_variance`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tf28.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf28.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),conv_13=extractConvPairParams(1024,1024,"mobilenetv1/conv_13");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,conv_8,conv_9,conv_10,conv_11,conv_12,conv_13}}function extractPredictionLayerParams(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0,conv_1,conv_2,conv_3,conv_4,conv_5,conv_6,conv_7,box_predictor_0:{box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1:{box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2:{box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3:{box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4:{box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},box_predictor_5:{box_encoding_predictor:box_encoding_5_predictor,class_predictor:class_predictor_5}}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParams6(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory5(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),output_layer={extra_dim:tf28.tensor3d(extractWeights(5118*4),[1,5118,4])};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`,`prediction_layer/box_predictor_${idx}/class_predictor`);return{box_encoding_predictor,class_predictor}}function extractPredictionLayerParams(){return{conv_0:extractPointwiseConvParams("Prediction",0,"prediction_layer/conv_0"),conv_1:extractPointwiseConvParams("Prediction",1,"prediction_layer/conv_1"),conv_2:extractPointwiseConvParams("Prediction",2,"prediction_layer/conv_2"),conv_3:extractPointwiseConvParams("Prediction",3,"prediction_layer/conv_3"),conv_4:extractPointwiseConvParams("Prediction",4,"prediction_layer/conv_4"),conv_5:extractPointwiseConvParams("Prediction",5,"prediction_layer/conv_5"),conv_6:extractPointwiseConvParams("Prediction",6,"prediction_layer/conv_6"),conv_7:extractPointwiseConvParams("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:extractBoxPredictorParams(0),box_predictor_1:extractBoxPredictorParams(1),box_predictor_2:extractBoxPredictorParams(2),box_predictor_3:extractBoxPredictorParams(3),box_predictor_4:extractBoxPredictorParams(4),box_predictor_5:extractBoxPredictorParams(5)}}return{extractMobilenetV1Params,extractPredictionLayerParams}}function extractParamsFromWeigthMap6(weightMap){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory6(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf30=__toModule(require_tfjs_esm());var tf29=__toModule(require_tfjs_esm());function pointwiseConvLayer(x,params,strides){return tf29.tidy(()=>{let out=tf29.conv2d(x,params.filters,strides,"same");return out=tf29.add(out,params.batch_norm_offset),tf29.clipByValue(out,0,6)})}var epsilon=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tf30.tidy(()=>{let out=tf30.depthwiseConv2d(x,params.filters,strides,"same");return out=tf30.batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon),tf30.clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tf30.tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]);if([params.conv_1,params.conv_2,params.conv_3,params.conv_4,params.conv_5,params.conv_6,params.conv_7,params.conv_8,params.conv_9,params.conv_10,params.conv_11,params.conv_12,params.conv_13].forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function nonMaxSuppression2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(selected.length>=outputSize)return;let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou2=IOU(boxes,c.boxIndex,selected[j]);if(iou2!==0&&(c.score*=suppressFunc(iou2),c.score<=scoreThreshold))break}originalScore===c.score&&selected.push(c.boxIndex)}),selected}var tf31=__toModule(require_tfjs_esm());function getCenterCoordinatesAndSizesLayer(x){let vec=tf31.unstack(tf31.transpose(x,[1,0])),sizes=[tf31.sub(vec[2],vec[0]),tf31.sub(vec[3],vec[1])],centers=[tf31.add(vec[0],tf31.div(sizes[0],tf31.scalar(2))),tf31.add(vec[1],tf31.div(sizes[1],tf31.scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=tf31.unstack(tf31.transpose(x1,[1,0])),div0_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2],tf31.scalar(5))),sizes[0]),tf31.scalar(2)),add0_out=tf31.add(tf31.mul(tf31.div(vec[0],tf31.scalar(10)),sizes[0]),centers[0]),div1_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3],tf31.scalar(5))),sizes[1]),tf31.scalar(2)),add1_out=tf31.add(tf31.mul(tf31.div(vec[1],tf31.scalar(10)),sizes[1]),centers[1]);return tf31.transpose(tf31.stack([tf31.sub(add0_out,div0_out),tf31.sub(add1_out,div1_out),tf31.add(add0_out,div0_out),tf31.add(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tf31.tidy(()=>{let batchSize=boxPredictions.shape[0],boxes=decodeBoxesLayer(tf31.reshape(tf31.tile(params.extra_dim,[batchSize,1,1]),[-1,4]),tf31.reshape(boxPredictions,[-1,4]));boxes=tf31.reshape(boxes,[batchSize,boxes.shape[0]/batchSize,4]);let scoresAndClasses=tf31.sigmoid(tf31.slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=tf31.slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=tf31.reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=tf31.unstack(boxes),scoresByBatch=tf31.unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}var tf33=__toModule(require_tfjs_esm());var tf32=__toModule(require_tfjs_esm());function boxPredictionLayer(x,params){return tf32.tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=tf32.reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=tf32.reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tf33.tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=tf33.concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=tf33.concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tf34.tidy(()=>{let batchTensor=tf34.cast(input.toBatchTensor(512,!1),"float32"),x=tf34.sub(tf34.mul(batchTensor,tf34.scalar(.007843137718737125)),tf34.scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input){return this.forwardInput(await toNetInput(input))}async locateFaces(input,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),indices=nonMaxSuppression2(boxes,scoresData,maxResults,.5,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[left,right]=[Math.max(0,boxesData[idx][1]),Math.min(1,boxesData[idx][3])].map(val=>val*padX);return new FaceDetection(scoresData[idx],new Rect(left,top,right-left,bottom-top),{height:netInput.getInputHeight(0),width:netInput.getInputWidth(0)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap6(weightMap)}extractParams(weights){return extractParams6(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{};var IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.874946),new Point(2.42204,2.65704),new Point(4.30971,7.04493),new Point(10.246,4.59428),new Point(12.6868,11.8741)],BOX_ANCHORS_SEPARABLE=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model";var tf39=__toModule(require_tfjs_esm());var isNumber=arg=>typeof arg=="number";function validateConfig(config){if(!config)throw new Error(`invalid config: ${config}`);if(typeof config.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);if(!isNumber(config.iouThreshold)||config.iouThreshold<0||config.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);if(!Array.isArray(config.classes)||!config.classes.length||!config.classes.every(c=>typeof c=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);if(!Array.isArray(config.anchors)||!config.anchors.length||!config.anchors.map(a=>a||{}).every(a=>isNumber(a.x)&&isNumber(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);if(config.meanRgb&&(!Array.isArray(config.meanRgb)||config.meanRgb.length!==3||!config.meanRgb.every(isNumber)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)}var tf36=__toModule(require_tfjs_esm());var tf35=__toModule(require_tfjs_esm());function leaky(x){return tf35.tidy(()=>{let min=tf35.mul(x,tf35.scalar(.10000000149011612));return tf35.add(tf35.relu(tf35.sub(x,min)),min)})}function convWithBatchNorm(x,params){return tf36.tidy(()=>{let out=tf36.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf36.conv2d(out,params.conv.filters,[1,1],"valid"),out=tf36.sub(out,params.bn.sub),out=tf36.mul(out,params.bn.truediv),out=tf36.add(out,params.conv.bias),leaky(out)})}var tf37=__toModule(require_tfjs_esm());function depthwiseSeparableConv2(x,params){return tf37.tidy(()=>{let out=tf37.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf37.separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=tf37.add(out,params.bias),leaky(out)})}var tf38=__toModule(require_tfjs_esm());function extractorsFactory7(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub6=tf38.tensor1d(extractWeights(size)),truediv=tf38.tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub6,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams7(weights,config,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory7(extractWeights,paramMappings),params;if(config.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),conv8=extractConvParams(s8,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub6=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub6,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap7(weightMap,config){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory8(weightMap,paramMappings),params;if(config.withSeparableConvs){let numFilters=config.filterSizes&&config.filterSizes.length||9;params={conv0:config.isFirstLayerConv2d?extractConvParams("conv0"):extractSeparableConvParams("conv0"),conv1:extractSeparableConvParams("conv1"),conv2:extractSeparableConvParams("conv2"),conv3:extractSeparableConvParams("conv3"),conv4:extractSeparableConvParams("conv4"),conv5:extractSeparableConvParams("conv5"),conv6:numFilters>7?extractSeparableConvParams("conv6"):void 0,conv7:numFilters>8?extractSeparableConvParams("conv7"):void 0,conv8:extractConvParams("conv8")}}else params={conv0:extractConvWithBatchNormParams("conv0"),conv1:extractConvWithBatchNormParams("conv1"),conv2:extractConvWithBatchNormParams("conv2"),conv3:extractConvWithBatchNormParams("conv3"),conv4:extractConvWithBatchNormParams("conv4"),conv5:extractConvWithBatchNormParams("conv5"),conv6:extractConvWithBatchNormParams("conv6"),conv7:extractConvWithBatchNormParams("conv7"),conv8:extractConvParams("conv8")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var TinyYolov2Base2=class extends NeuralNetwork{constructor(config){super("TinyYolov2");validateConfig(config),this._config=config}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(x,params){let out=convWithBatchNorm(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv2(x,params.conv0);return out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv1),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv2),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv3),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv4),out=tf39.maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv2(out,params.conv5),out=tf39.maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv2(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv2(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tf39.tidy(()=>{let batchTensor=tf39.cast(input.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(tf39.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input,inputSize){return this.forwardInput(await toNetInput(input),inputSize)}async detect(input,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input),out=await this.forwardInput(netInput,inputSize),out0=tf39.tidy(()=>tf39.unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]);return nonMaxSuppression(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0).map(idx=>new ObjectDetection(scores[idx],classScores[idx],classNames[idx],boxes[idx],inputDimensions))}getDefaultModelName(){return""}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap7(weightMap,this.config)}extractParams(weights){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,numFilters=filterSizes?filterSizes.length:void 0;if(numFilters!==7&&numFilters!==8&&numFilters!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);return extractParams7(weights,this.config,this.boxEncodingSize,filterSizes)}async extractBoxes(outputTensor,inputBlobDimensions,scoreThreshold){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tf39.tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?tf39.softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):tf39.scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),boxesData=await boxesTensor.array();for(let row=0;row<numCells;row++)for(let col=0;col<numCells;col++)for(let anchor=0;anchor<numBoxes;anchor++){let score=sigmoid(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,widthLocal=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,heightLocal=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-widthLocal/2,y=ctY-heightLocal/2,pos={row,col,anchor},{classScore,label}=this.withClassScores?await this.extractPredictedClass(classScoresTensor,pos):{classScore:1,label:0};results.push({box:new BoundingBox(x,y,x+widthLocal,y+heightLocal),score,classScore:score*classScore,label,...pos})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,classesData=await classesTensor.array();return Array(this.config.classes.length).fill(0).map((_,i)=>classesData[row][col][anchor][i]).map((classScore,label)=>({classScore,label})).reduce((max,curr)=>max.classScore>curr.classScore?max:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config={withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"],...withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0}};super(config)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DEFAULT_MODEL_NAME_SEPARABLE_CONV:DEFAULT_MODEL_NAME}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var tf41=__toModule(require_tfjs_esm());var tf40=__toModule(require_tfjs_esm());async function extractAllFacesAndComputeResults(parentResults,input,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input instanceof tf40.Tensor?await extractFaceTensors(input,faceBoxes):await extractFaces(input,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof tf40.Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,BOX_ANCHORS2=[new Point(1.603231,2.094468),new Point(6.041143,7.080126),new Point(2.882459,3.518061),new Point(4.266906,5.178857),new Point(9.041765,10.66308)],MEAN_RGB=[117.001,114.697,97.404];var TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config)}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){return(await this.detect(input,forwardParams)).map(det=>new FaceDetection(det.score,det.relativeBox,{width:det.imageWidth,height:det.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};var nets={ssdMobilenetv1:new SsdMobilenetv1,tinyFaceDetector:new TinyFaceDetector,tinyYolov2:new TinyYolov2,faceLandmark68Net:new FaceLandmark68Net,faceLandmark68TinyNet:new FaceLandmark68TinyNet,faceRecognitionNet:new FaceRecognitionNet,faceExpressionNet:new FaceExpressionNet,ageGenderNet:new AgeGenderNet},ssdMobilenetv1=(input,options)=>nets.ssdMobilenetv1.locateFaces(input,options),tinyFaceDetector=(input,options)=>nets.tinyFaceDetector.locateFaces(input,options),tinyYolov2=(input,options)=>nets.tinyYolov2.locateFaces(input,options),detectFaceLandmarks=input=>nets.faceLandmark68Net.detectLandmarks(input),detectFaceLandmarksTiny=input=>nets.faceLandmark68TinyNet.detectLandmarks(input),computeFaceDescriptor=input=>nets.faceRecognitionNet.computeFaceDescriptor(input),recognizeFaceExpressions=input=>nets.faceExpressionNet.predictExpressions(input),predictAgeAndGender=input=>nets.ageGenderNet.predictAgeAndGender(input),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks;var PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,faceExpressionsByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.faceExpressionNet.predictExpressions(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>extendWithFaceExpressions(parentResult,faceExpressionsByFace[i]))}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this,this.input)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,ageAndGenderByFace=await extractAllFacesAndComputeResults(parentResults,this.input,async faces=>Promise.all(faces.map(face=>nets.ageGenderNet.predictAgeAndGender(face))),this.extractedFaces);return parentResults.map((parentResult,i)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{age,gender,genderProbability}=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.ageGenderNet.predictAgeAndGender(face),this.extractedFaces);return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this,this.input)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input){super();this.parentTask=parentTask;this.input=input}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask;return(await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}))).map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}};var DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),parentResults.map((parentResult,i)=>extendWithFaceLandmarks(parentResult,faceLandmarksByFace[i]))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{detection}=parentResult,faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}};var DetectFacesTaskBase=class extends ComposableTask{constructor(input,options=new SsdMobilenetv1Options){super();this.input=input;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input2=>nets.tinyFaceDetector.locateFaces(input2,options):options instanceof SsdMobilenetv1Options?input2=>nets.ssdMobilenetv1.locateFaces(input2,options):options instanceof TinyYolov2Options?input2=>nets.tinyYolov2.locateFaces(input2,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input)}runAndExtendWithFaceDetections(){return new Promise(async resolve=>{let detections=await this.run();resolve(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async resolve=>{let detection=await this.run();resolve(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input,options)}function detectAllFaces(input,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input,options)}async function allFacesSsdMobilenetv1(input,minConfidence){return detectAllFaces(input,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input,forwardParams={}){return detectAllFaces(input,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+diff**2,0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count=1,createUniqueLabel=()=>`person ${count++}`;this._labeledDescriptors=inputArray.map(desc=>{if(desc instanceof LabeledFaceDescriptors)return desc;if(desc instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc]);if(desc.descriptor&&desc.descriptor instanceof Float32Array)return new LabeledFaceDescriptors(createUniqueLabel(),[desc.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(queryDescriptor,descriptors){return descriptors.map(d=>euclideanDistance(d,queryDescriptor)).reduce((d1,d2)=>d1+d2,0)/(descriptors.length||1)}matchDescriptor(queryDescriptor){return this.labeledDescriptors.map(({descriptors,label})=>new FaceMatch(label,this.computeMeanDistance(queryDescriptor,descriptors))).reduce((best,curr)=>best.distance<curr.distance?best:curr)}findBestMatch(queryDescriptor){let bestMatch=this.matchDescriptor(queryDescriptor);return bestMatch.distance<this.distanceThreshold?bestMatch:new FaceMatch("unknown",bestMatch.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(ld=>ld.toJSON())}}static fromJSON(json){let labeledDescriptors=json.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{width,height}=new Dimensions(dimensions.width,dimensions.height);if(width<=0||height<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width,height})}`);if(Array.isArray(results))return results.map(obj=>resizeResults(obj,{width,height}));if(isWithFaceLandmarks(results)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var node=typeof process!="undefined",browser3=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version2={faceapi:version,node,browser:browser3};
 //# sourceMappingURL=face-api.node.js.map
diff --git a/dist/face-api.node.js.map b/dist/face-api.node.js.map
index 973ded2..159d761 100644
--- a/dist/face-api.node.js.map
+++ b/dist/face-api.node.js.map
@@ -2,6 +2,6 @@
   "version": 3,
   "sources": ["../src/tfjs/tf-node.ts", "../src/env/isNodejs.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts"],
   "sourcesContent": ["/* eslint-disable import/no-extraneous-dependencies */\n/* eslint-disable node/no-unpublished-import */\n\nexport * from '@tensorflow/tfjs-node';\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\n// eslint-disable-next-line no-undef\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  // eslint-disable-next-line no-mixed-operators\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n\n    const { readFile } = env.getEnv();\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)),\n    );\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParamsFromWeigthMap(weightMap);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params,\n    } = this.extractParams(weights);\n\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeigthMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input);\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      (t) => t.data(),\n    )) as Float32Array[];\n\n    faceDescriptorTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    // TODO batches\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data());\n\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) {\n      return;\n    }\n    const originalScore = c.score;\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n\n    return {\n      boxPredictionEncoding,\n      classPrediction,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024,\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n    // return tf.maximum(x, min)\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config';\nexport * from './types';\n\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n"],
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         },
         "src/ssdMobilenetv1/extractParams.ts": {
-          "bytesInOutput": 5963
+          "bytesInOutput": 5967
         },
         "src/ssdMobilenetv1/extractParamsFromWeigthMap.ts": {
           "bytesInOutput": 4319
         },
         "src/ssdMobilenetv1/mobileNetV1.ts": {
-          "bytesInOutput": 1110
+          "bytesInOutput": 1111
         },
         "src/ssdMobilenetv1/pointwiseConvLayer.ts": {
-          "bytesInOutput": 235
+          "bytesInOutput": 239
         },
         "src/ssdMobilenetv1/nonMaxSuppression.ts": {
-          "bytesInOutput": 1502
+          "bytesInOutput": 1504
         },
         "src/ssdMobilenetv1/outputLayer.ts": {
           "bytesInOutput": 1590
         },
         "src/ssdMobilenetv1/predictionLayer.ts": {
-          "bytesInOutput": 1418
+          "bytesInOutput": 1419
         },
         "src/ssdMobilenetv1/boxPredictionLayer.ts": {
-          "bytesInOutput": 350
+          "bytesInOutput": 354
         },
         "src/ssdMobilenetv1/SsdMobilenetv1Options.ts": {
-          "bytesInOutput": 554
+          "bytesInOutput": 555
         },
         "src/ssdMobilenetv1/index.ts": {
-          "bytesInOutput": 234
+          "bytesInOutput": 235
         },
         "src/tinyYolov2/const.ts": {
-          "bytesInOutput": 485
+          "bytesInOutput": 490
         },
         "src/tinyYolov2/TinyYolov2Base.ts": {
-          "bytesInOutput": 5885
+          "bytesInOutput": 5894
         },
         "src/tinyYolov2/config.ts": {
-          "bytesInOutput": 1164
+          "bytesInOutput": 1168
         },
         "src/tinyYolov2/convWithBatchNorm.ts": {
-          "bytesInOutput": 322
+          "bytesInOutput": 323
         },
         "src/tinyYolov2/leaky.ts": {
-          "bytesInOutput": 178
+          "bytesInOutput": 182
         },
         "src/tinyYolov2/depthwiseSeparableConv.ts": {
           "bytesInOutput": 293
@@ -2557,37 +2557,37 @@
           "bytesInOutput": 139
         },
         "src/tinyFaceDetector/TinyFaceDetectorOptions.ts": {
-          "bytesInOutput": 132
+          "bytesInOutput": 133
         },
         "src/globalApi/ComposableTask.ts": {
-          "bytesInOutput": 154
+          "bytesInOutput": 159
         },
         "src/globalApi/DetectFaceLandmarksTasks.ts": {
-          "bytesInOutput": 1912
+          "bytesInOutput": 1926
         },
         "src/globalApi/extractFacesAndComputeResults.ts": {
-          "bytesInOutput": 812
+          "bytesInOutput": 816
         },
         "src/tinyFaceDetector/const.ts": {
-          "bytesInOutput": 215
+          "bytesInOutput": 216
         },
         "src/tinyFaceDetector/TinyFaceDetector.ts": {
-          "bytesInOutput": 624
+          "bytesInOutput": 629
         },
         "src/globalApi/nets.ts": {
-          "bytesInOutput": 1515
+          "bytesInOutput": 1520
         },
         "src/globalApi/PredictFaceExpressionsTask.ts": {
-          "bytesInOutput": 1691
+          "bytesInOutput": 1700
         },
         "src/globalApi/PredictAgeAndGenderTask.ts": {
-          "bytesInOutput": 1785
+          "bytesInOutput": 1794
         },
         "src/globalApi/ComputeFaceDescriptorsTasks.ts": {
-          "bytesInOutput": 1390
+          "bytesInOutput": 1399
         },
         "src/globalApi/DetectFacesTasks.ts": {
-          "bytesInOutput": 2221
+          "bytesInOutput": 2225
         },
         "src/globalApi/detectFaces.ts": {
           "bytesInOutput": 224
@@ -2611,7 +2611,7 @@
           "bytesInOutput": 824
         }
       },
-      "bytes": 132266
+      "bytes": 132469
     }
   }
 }
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
index b57715b..05da935 100644
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
@@ -5,43 +5,43 @@
   author: <https://github.com/vladmandic>'
   */
 
-var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module),require_browser=__commonJS(()=>{}),require_alea=__commonJS((exports,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}function Mash(){var n=4022871197,mash=function(data){data=data.toString();for(var i=0;i<data.length;i++){n+=data.charCodeAt(i);var h=.02519603282416938*n;n=h>>>0,h-=n,h*=n,n=h>>>0,h-=n,n+=h*4294967296}return(n>>>0)*23283064365386963e-26};return mash}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.alea=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor128=__commonJS((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.x=0,me.y=0,me.z=0,me.w=0,me.next=function(){var t=me.x^me.x<<11;return me.x=me.y,me.y=me.z,me.z=me.w,me.w^=me.w>>>19^t^t>>>8},seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorwow=__commonJS((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,k==strseed.length&&(me.d=me.x<<10^me.x>>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift7=__commonJS((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j<seed2.length;++j)X[j&7]=X[j&7]<<15^seed2.charCodeAt(j)+X[j+1&7]<<13;for(;X.length<8;)X.push(0);for(j=0;j<8&&X[j]===0;++j);for(j==8?w=X[7]=-1:w=X[j],me2.x=X,me2.i=0,j=256;j>0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor4096=__commonJS((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j<limit;++j)seed2&&(v^=seed2.charCodeAt((j+32)%seed2.length)),j===0&&(w=v),v^=v<<10,v^=v>>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS(()=>{}),require_seedrandom=__commonJS((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&&copy(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten4(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten4(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser=global2.navigator,plugins=browser&&browser.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool3)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool3),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)}),require_seedrandom2=__commonJS((exports,module)=>{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5;sr.xor128=xor128;sr.xorwow=xorwow;sr.xorshift7=xorshift7;sr.xor4096=xor4096;sr.tychei=tychei;module.exports=sr}),require_string_decoder=__commonJS(()=>{}),require_path=__commonJS(()=>{}),require_worker_threads=__commonJS(()=>{}),require_perf_hooks=__commonJS(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data=read(f,"binary"),assert3(typeof data=="object"),data)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&(typeof performance=="undefined"&&(performance=require_perf_hooks().performance));var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:167,maximum:167+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack2===0&&(stack2=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack2!==0&&stackRestore(stack2),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,str="";!(idx>=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i<str.length;++i){var u=str.charCodeAt(i);u>=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i<pthreadPoolSize;++i)PThread.allocateUnusedWorker();PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i],pthread=worker.pthread;PThread.freeThreadData(pthread),worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!!pthread){if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data){worker.onmessage({data})}),worker.on("error",function(data){worker.onerror(data)}),worker.on("exit",function(data){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()<t;);}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop,STACK_MAX=stackMax,stackRestore(stackTop)}Module.establishStackSpace=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module.getNoExitRuntime=getNoExitRuntime;function ___assert_fail(condition,filename,line,func2){abort("Assertion failed: "+UTF8ToString(condition)+", at: "+[filename?UTF8ToString(filename):"unknown filename",line,func2?UTF8ToString(func2):"unknown function"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;ENVIRONMENT_IS_NODE?_emscripten_get_now=function(){var t=process.hrtime();return t[0]*1e3+t[1]/1e6}:ENVIRONMENT_IS_PTHREAD?_emscripten_get_now=function(){return performance.now()-Module.__performance_now_clock_drift}:typeof dateNow!="undefined"?_emscripten_get_now=dateNow:_emscripten_get_now=function(){return performance.now()};function setErrNo(value){return GROWABLE_HEAP_I32()[___errno_location()>>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i<numCallArgs;i++)GROWABLE_HEAP_F64()[b+i]=arguments[2+i];var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);return stackRestore(stack2),ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgs.array||(readAsmConstArgs.array=[]);var args=readAsmConstArgs.array;args.length=0;for(var ch;ch=GROWABLE_HEAP_U8()[sigPtr++];)ch===100||ch===102?(buf=buf+7&~7,args.push(GROWABLE_HEAP_F64()[buf>>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i<numCallArgs;i++)_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i];var isEmAsmConst=index<0,func2=isEmAsmConst?ASM_CONSTS[-index-1]:proxiedFunctionTable[index];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1],varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2],constArgs=readAsmConstArgs(sigPtr,varargPtr);return func2.apply(null,constArgs)}return func2.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{return wasmMemory.grow(size-buffer2.byteLength+65535>>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i)JSEvents.deferredCalls[i].targetFunction==targetFunction&&(JSEvents.deferredCalls.splice(i,1),--i)},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!!JSEvents.canPerformEventHandlerRequests())for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1),--i,call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)&&JSEvents._removeHandler(i--)},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture),JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function(event){++JSEvents.inEventHandler,JSEvents.currentEventHandler=eventHandler,JSEvents.runDeferredCalls(),eventHandler.handlerFunc(event),JSEvents.runDeferredCalls(),--JSEvents.inEventHandler};if(eventHandler.callbackfunc)eventHandler.eventListenerFunc=jsEventHandler,eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture),JSEvents.eventHandlers.push(eventHandler),JSEvents.registerRemoveEventListeners();else for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString&&JSEvents._removeHandler(i--)},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave(),varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1);for(var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)},recordError:function(errorCode){GL.lastError||(GL.lastError=errorCode)},getNewId:function(table){for(var ret=GL.counter++,i=table.length;i<ret;i++)table[i]=null;return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count2,string,length){for(var source="",i=0;i<count2;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p2,i),name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1),name.slice(-1)=="]"&&(name=name.slice(0,name.lastIndexOf("[")));var loc=GLctx.getUniformLocation(p2,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id],GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+"["+j+"]";loc=GLctx.getUniformLocation(p2,n),id=GL.getNewId(GL.uniforms),GL.uniforms[id]=loc}}}}},__emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={},a=attributes>>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i<iovcnt;i++){for(var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j]);num+=len}return GROWABLE_HEAP_I32()[pnum>>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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__ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATPOSTRUN__=[],runtimeInitialized=!1,runtimeExited=!1;function preRun(){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=!0}function postRun(){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). 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fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return 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Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D";var kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config]=value,[backend2]=key.split("_");backend2===backendName&&result.push(config)}return result}function registerKernel(config){let{kernelName,backendName}=config,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config)}function registerGradient(config){let{kernelName}=config;gradRegistry.has(kernelName)&&(env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`)),gradRegistry.set(kernelName,config)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now(){return env().platform.now()}function fetch2(path,requestInits){return env().platform.fetch(path,requestInits)}function encodeString(s,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.encode(s,encoding)}function decodeString(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.decode(bytes,encoding)}var Profiler=class{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad(name,25),rank=result.rank,size=result.size,shape=rightPad(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape.length;i++){let node=tape[i],nodeInputs=node.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node=tape[i],nodeInputs=node.inputs;for(let j=0;j<node.outputs.length;j++)if(tensorsLeadToY[node.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape.length;i++){let node=tape[i];if(nodesFromX[node.id]&&nodesToY[node.id]){let prunedInputs={};for(let inputName in node.inputs){let nodeInput=node.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node);prunedNode.inputs=prunedInputs,prunedNode.outputs=node.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients(tensorAccumulatedGradientMap,filteredTape,tidy2,add5){for(let i=filteredTape.length-1;i>=0;i--){let node=filteredTape[i],dys=[];if(node.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);let inputGradients=node.gradient(dys);for(let inputName in node.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
+var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);var require_browser=__commonJS(()=>{});var require_alea=__commonJS((exports,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}function Mash(){var n=4022871197,mash=function(data){data=data.toString();for(var i=0;i<data.length;i++){n+=data.charCodeAt(i);var h=.02519603282416938*n;n=h>>>0,h-=n,h*=n,n=h>>>0,h-=n,n+=h*4294967296}return(n>>>0)*23283064365386963e-26};return mash}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.alea=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)});var require_xor128=__commonJS((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.x=0,me.y=0,me.z=0,me.w=0,me.next=function(){var t=me.x^me.x<<11;return me.x=me.y,me.y=me.z,me.z=me.w,me.w^=me.w>>>19^t^t>>>8},seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)});var require_xorwow=__commonJS((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,k==strseed.length&&(me.d=me.x<<10^me.x>>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)});var require_xorshift7=__commonJS((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j<seed2.length;++j)X[j&7]=X[j&7]<<15^seed2.charCodeAt(j)+X[j+1&7]<<13;for(;X.length<8;)X.push(0);for(j=0;j<8&&X[j]===0;++j);for(j==8?w=X[7]=-1:w=X[j],me2.x=X,me2.i=0,j=256;j>0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)});var require_xor4096=__commonJS((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j<limit;++j)seed2&&(v^=seed2.charCodeAt((j+32)%seed2.length)),j===0&&(w=v),v^=v<<10,v^=v>>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)});var require_tychei=__commonJS((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&&copy(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)});var require_crypto=__commonJS(()=>{});var require_seedrandom=__commonJS((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&&copy(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten4(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten4(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser=global2.navigator,plugins=browser&&browser.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool3)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool3),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)});var require_seedrandom2=__commonJS((exports,module)=>{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5;sr.xor128=xor128;sr.xorwow=xorwow;sr.xorshift7=xorshift7;sr.xor4096=xor4096;sr.tychei=tychei;module.exports=sr});var require_string_decoder=__commonJS(()=>{});var require_path=__commonJS(()=>{});var require_worker_threads=__commonJS(()=>{});var require_perf_hooks=__commonJS(()=>{});var require_tfjs_backend_wasm_threaded_simd=__commonJS((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data=read(f,"binary"),assert3(typeof data=="object"),data)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack2===0&&(stack2=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack2!==0&&stackRestore(stack2),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,str="";!(idx>=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i<str.length;++i){var u=str.charCodeAt(i);u>=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i<pthreadPoolSize;++i)PThread.allocateUnusedWorker();PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i],pthread=worker.pthread;PThread.freeThreadData(pthread),worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!!pthread){if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data){worker.onmessage({data})}),worker.on("error",function(data){worker.onerror(data)}),worker.on("exit",function(data){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()<t;);}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop,STACK_MAX=stackMax,stackRestore(stackTop)}Module.establishStackSpace=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module.getNoExitRuntime=getNoExitRuntime;function ___assert_fail(condition,filename,line,func2){abort("Assertion failed: "+UTF8ToString(condition)+", at: "+[filename?UTF8ToString(filename):"unknown filename",line,func2?UTF8ToString(func2):"unknown function"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;ENVIRONMENT_IS_NODE?_emscripten_get_now=function(){var t=process.hrtime();return t[0]*1e3+t[1]/1e6}:ENVIRONMENT_IS_PTHREAD?_emscripten_get_now=function(){return performance.now()-Module.__performance_now_clock_drift}:typeof dateNow!="undefined"?_emscripten_get_now=dateNow:_emscripten_get_now=function(){return performance.now()};function setErrNo(value){return GROWABLE_HEAP_I32()[___errno_location()>>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i<numCallArgs;i++)GROWABLE_HEAP_F64()[b+i]=arguments[2+i];var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);return stackRestore(stack2),ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgs.array||(readAsmConstArgs.array=[]);var args=readAsmConstArgs.array;args.length=0;for(var ch;ch=GROWABLE_HEAP_U8()[sigPtr++];)ch===100||ch===102?(buf=buf+7&~7,args.push(GROWABLE_HEAP_F64()[buf>>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i<numCallArgs;i++)_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i];var isEmAsmConst=index<0,func2=isEmAsmConst?ASM_CONSTS[-index-1]:proxiedFunctionTable[index];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1],varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2],constArgs=readAsmConstArgs(sigPtr,varargPtr);return func2.apply(null,constArgs)}return func2.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{return wasmMemory.grow(size-buffer2.byteLength+65535>>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i)JSEvents.deferredCalls[i].targetFunction==targetFunction&&(JSEvents.deferredCalls.splice(i,1),--i)},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!!JSEvents.canPerformEventHandlerRequests())for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1),--i,call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)&&JSEvents._removeHandler(i--)},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture),JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function(event){++JSEvents.inEventHandler,JSEvents.currentEventHandler=eventHandler,JSEvents.runDeferredCalls(),eventHandler.handlerFunc(event),JSEvents.runDeferredCalls(),--JSEvents.inEventHandler};if(eventHandler.callbackfunc)eventHandler.eventListenerFunc=jsEventHandler,eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture),JSEvents.eventHandlers.push(eventHandler),JSEvents.registerRemoveEventListeners();else for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString&&JSEvents._removeHandler(i--)},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave(),varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1);for(var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)},recordError:function(errorCode){GL.lastError||(GL.lastError=errorCode)},getNewId:function(table){for(var ret=GL.counter++,i=table.length;i<ret;i++)table[i]=null;return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count2,string,length){for(var source="",i=0;i<count2;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p2,i),name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1),name.slice(-1)=="]"&&(name=name.slice(0,name.lastIndexOf("[")));var loc=GLctx.getUniformLocation(p2,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id],GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+"["+j+"]";loc=GLctx.getUniformLocation(p2,n),id=GL.getNewId(GL.uniforms),GL.uniforms[id]=loc}}}}},__emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={},a=attributes>>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i<iovcnt;i++){for(var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j]);num+=len}return GROWABLE_HEAP_I32()[pnum>>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var 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stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array2,buffer3){HEAP8.set(array2,buffer3)}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATPOSTRUN__=[],runtimeInitialized=!1,runtimeExited=!1;function preRun(){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=!0}function postRun(){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). 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fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){for(var num=0,i=0;i<iovcnt;i++){for(var ptr=HEAP32[iov+i*8>>2],len=HEAP32[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,HEAPU8[ptr+j]);num+=len}return HEAP32[pnum>>2]=num,0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}var asmLibraryArg={emscripten_notify_memory_growth:_emscripten_notify_memory_growth,fd_close:_fd_close,fd_seek:_fd_seek,fd_write:_fd_write,proc_exit:_proc_exit,roundf:_roundf},asm=createWasm();Module.asm=asm;var 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global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){let ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){let globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{let singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}var Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D";var kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config]=value,[backend2]=key.split("_");backend2===backendName&&result.push(config)}return result}function registerKernel(config){let{kernelName,backendName}=config,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config)}function registerGradient(config){let{kernelName}=config;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now(){return env().platform.now()}function fetch2(path,requestInits){return env().platform.fetch(path,requestInits)}function encodeString(s,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.encode(s,encoding)}function decodeString(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.decode(bytes,encoding)}var Profiler=class{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad(name,25),rank=result.rank,size=result.size,shape=rightPad(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape.length;i++){let node=tape[i],nodeInputs=node.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node=tape[i],nodeInputs=node.inputs;for(let j=0;j<node.outputs.length;j++)if(tensorsLeadToY[node.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape.length;i++){let node=tape[i];if(nodesFromX[node.id]&&nodesToY[node.id]){let prunedInputs={};for(let inputName in node.inputs){let nodeInput=node.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node);prunedNode.inputs=prunedInputs,prunedNode.outputs=node.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients(tensorAccumulatedGradientMap,filteredTape,tidy2,add5){for(let i=filteredTape.length-1;i>=0;i--){let node=filteredTape[i],dys=[];if(node.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);let inputGradients=node.gradient(dys);for(let inputName in node.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
 `)),lines.join(`
 `)}function computeMaxSizePerColumn(vals,shape,dtype,strides){let n=sizeFromShape(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples(vals):vals;if(rank>1)for(let row=0;row<n/numCols;row++){let offset=row*numCols;for(let j=0;j<numCols;j++)padPerCol[j]=Math.max(padPerCol[j],valToString(valuesOrTuples[offset+j],0,dtype).length)}return padPerCol}function valToString(val,pad3,dtype){let valStr;return Array.isArray(val)?valStr=`${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`:isString(val)?valStr=`'${val}'`:dtype==="bool"?valStr=boolNumToString(val):valStr=parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),rightPad(valStr,pad3)}function boolNumToString(v){return v===0?"false":"true"}function subTensorToString(vals,shape,dtype,strides,padPerCol,isLast=!0){let storagePerElement=dtype==="complex64"?2:1,size=shape[0],rank=shape.length;if(rank===0){if(dtype==="complex64"){let complexTuple=createComplexTuples(vals);return[valToString(complexTuple[0],0,dtype)]}return dtype==="bool"?[boolNumToString(vals[0])]:[vals[0].toString()]}if(rank===1){if(size>FORMAT_LIMIT_NUM_VALS){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples(firstVals),lastVals=createComplexTuples(lastVals)),["["+firstVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS){for(let i=0;i<FORMAT_NUM_FIRST_LAST_VALS;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,!1))}lines.push("...");for(let i=size-FORMAT_NUM_FIRST_LAST_VALS;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}}else for(let i=0;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}let sep=rank===2?",":"";lines[0]="["+lines[0]+sep;for(let i=1;i<lines.length-1;i++)lines[i]=" "+lines[i]+sep;let newLineSep=`,
 `;for(let i=2;i<rank;i++)newLineSep+=`
-`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape(shape),values!=null){let n=values.length;assert(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn().makeTensor(this.values,this.shape,this.dtype)}},trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(fn){trackerFn=fn}function setOpHandler(handler){opHandler=handler}function setDeprecationWarningFn(fn){deprecationWarningFn=fn}var Tensor=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape(shape),this.strides=computeStrides(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray(this.shape,vals)}arraySync(){return toNestedArray(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor2,tensorList){return tensorList.some(x=>x.id===tensor2.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container,list,seen){if(container!=null){if(container instanceof Tensor){list.push(container);return}if(!!isIterable(container)){let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(k=>k.name)))}}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance==null){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i];if(await this.initializeBackend(backendName).success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName);if(!(asyncInit?await success:success))return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler(this.backendInstance),!0}setupRegisteredKernels(){getKernelsForBackend(this.backendName).forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){getKernelsForBackend(backendName).forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend2=registryFactoryEntry.factory();if(backend2&&!(backend2 instanceof KernelBackend)&&typeof backend2.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend2.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend2,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend2,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend2,backend2.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend2=>backend2.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3,kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(outInfo=>{if(outInfo.rank!=null)return outInfo;let{dataId,shape,dtype}=outInfo;return this.makeTensorFromDataId(dataId,shape,dtype)});if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{if(forwardFunc==null)throw new Error(`Error running ${kernelName}: Neither modular kernel nor forward func passed`);let saveFunc=tensors=>{!isTapeOn||(saved=tensors.map(tensor2=>this.keep(this.clone(tensor2))))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){return tensors.map(tensor2=>this.keep(this.clone(tensor2)))}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend2){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend2=backend2||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend2.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend2),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend2){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend2),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend2){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend2||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!!this.state.tensorInfo.has(a.dataId)){this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId);info.refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor2=this.state.activeScope.track[i];!tensor2.kept&&!tensorsToTrackInParentSet.has(tensor2.id)&&tensor2.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor2=>{!tensor2.kept&&tensor2.scopeId===oldScope.id&&this.track(tensor2)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node=>{for(let tensor2 of node.saved)tensor2.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){return this.state.tensorInfo.get(dataId).backend.readSync(dataId)}read(dataId){return this.state.tensorInfo.get(dataId).backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment=new Environment(ns);ns._tfengine=new Engine(environment)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernel(Add,inputs)}var device_util_exports={};__export(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s 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is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency(val[i],subShape,indices.concat(i))}function assertDtype(expectedDtype,actualDType,argName,functionName){if(expectedDtype!=="string_or_numeric"){if(expectedDtype==null)throw new Error("Expected dtype cannot be null.");if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}}function convertToTensor(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor)return assertDtype(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],!0);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);return arg.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName,parseAsDtype))}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real4,imag4){let $real=convertToTensor(real4,"real","complex"),$imag=convertToTensor(imag4,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let inputs={real:$real,imag:$imag};return ENGINE.runKernel(Complex,inputs)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape(shape.slice(i)):!0;assert(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};var NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor2=>tensor2.name):Object.keys(tensors);for(let i=0;i<names.length;++i){let name=names[i],t=Array.isArray(tensors)?tensors[i].tensor:tensors[name];if(t.dtype!=="float32"&&t.dtype!=="int32"&&t.dtype!=="bool"&&t.dtype!=="string"&&t.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);let spec={name,shape:t.shape,dtype:t.dtype};if(t.dtype==="string"){let utf8bytes=new Promise(async resolve=>{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2<vals.length;i2++){let val=vals[i2],bytesOfLength=new Uint8Array(new Uint32Array([val.length]).buffer);bytes.set(bytesOfLength,offset),offset+=NUM_BYTES_STRING_LENGTH,bytes.set(val,offset),offset+=val.length}resolve(bytes)});dataPromises.push(utf8bytes)}else dataPromises.push(t.data());group!=null&&(spec.group=group),specs.push(spec)}let tensorValues=await Promise.all(dataPromises);return{data:concatenateTypedArrays(tensorValues),specs}}function decodeWeights(buffer2,specs){let out={},float16Decode,offset=0;for(let spec of specs){let name=spec.name,dtype=spec.dtype,shape=spec.shape,size=sizeFromShape(shape),values;if("quantization"in spec){let quantization=spec.quantization;if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){if(!("min"in quantization&&"scale"in quantization))throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`)}else if(quantization.dtype==="float16"){if(dtype!=="float32")throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`)}else throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let quantizationSizeFactor=DTYPE_VALUE_SIZE_MAP[quantization.dtype],byteBuffer=buffer2.slice(offset,offset+size*quantizationSizeFactor),quantizedArray=quantization.dtype==="uint8"?new Uint8Array(byteBuffer):new Uint16Array(byteBuffer);if(dtype==="float32")if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){values=new Float32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=v*quantization.scale+quantization.min}}else if(quantization.dtype==="float16")float16Decode===void 0&&(float16Decode=getFloat16Decoder()),values=float16Decode(quantizedArray);else throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);else if(dtype==="int32"){if(quantization.dtype!=="uint8"&&quantization.dtype!=="uint16")throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);values=new Int32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=Math.round(v*quantization.scale+quantization.min)}}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*quantizationSizeFactor}else if(dtype==="string"){let size2=sizeFromShape(spec.shape);values=[];for(let i=0;i<size2;i++){let byteLength=new Uint32Array(buffer2.slice(offset,offset+NUM_BYTES_STRING_LENGTH))[0];offset+=NUM_BYTES_STRING_LENGTH;let bytes=new Uint8Array(buffer2.slice(offset,offset+byteLength));values.push(bytes),offset+=byteLength}}else{let dtypeFactor=DTYPE_VALUE_SIZE_MAP[dtype],byteBuffer=buffer2.slice(offset,offset+size*dtypeFactor);if(dtype==="float32")values=new Float32Array(byteBuffer);else if(dtype==="int32")values=new Int32Array(byteBuffer);else if(dtype==="bool")values=new Uint8Array(byteBuffer);else if(dtype==="complex64"){values=new Float32Array(byteBuffer);let real4=new Float32Array(values.length/2),image3=new Float32Array(values.length/2);for(let i=0;i<real4.length;i++)real4[i]=values[i*2],image3[i]=values[i*2+1];let realTensor=tensor(real4,shape,"float32"),imageTensor=tensor(image3,shape,"float32");out[name]=complex(realTensor,imageTensor),realTensor.dispose(),imageTensor.dispose()}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*dtypeFactor}dtype!=="complex64"&&(out[name]=tensor(values,shape,dtype))}return out}function concatenateTypedArrays(xs){if(xs===null)throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);let totalByteLength=0,normalizedXs=[];xs.forEach(x=>{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer2){if(useNodeBuffer)return Buffer.from(buffer2).toString("base64");let buf=new Uint8Array(buffer2),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer(str){if(useNodeBuffer){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer2=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer2.set([s.charCodeAt(i)],i);return buffer2.buffer}function concatenateArrayBuffers(buffers){if(buffers.length===1)return buffers[0];let totalByteLength=0;buffers.forEach(buffer2=>{totalByteLength+=buffer2.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer2=>{temp.set(new Uint8Array(buffer2),offset),offset+=buffer2.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)===0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer2=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer2);for(let index=0;index<quantizedArray.length;index++){let float16Bits=quantizedArray[index],float32Bits=mantisaTable[offsetTable[float16Bits>>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer2)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[];return(handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters).forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions);var DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),getRequest=modelTx.objectStore(MODEL_STORE_NAME).get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let putModelRequest=modelTx.objectStore(MODEL_STORE_NAME).put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&(!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME))?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),getAllInfoRequest=tx.objectStore(INFO_STORE_NAME).getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let deleteModelRequest=modelTx.objectStore(MODEL_STORE_NAME).delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}};var PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData));let result={format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy};return modelArtifacts.signature!=null&&(result.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(result.modelInitializer=modelArtifacts.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(result)),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,metadata.signature!=null&&(out.signature=metadata.signature),metadata.userDefinedMetadata!=null&&(out.userDefinedMetadata=metadata.userDefinedMetadata),metadata.modelInitializer!=null&&(out.modelInitializer=metadata.modelInitializer)}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&(!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME))?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme2(path);let keys=getModelKeys(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}};var URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry.instance==null&&(ModelStoreManagerRegistry.instance=new ModelStoreManagerRegistry),ModelStoreManagerRegistry.instance}static registerManager(scheme,manager){assert(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url);return ModelStoreManagerRegistry.getManager(schemeAndPath.scheme).removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!1)}async function moveModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!0)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}now(){return performance.now()}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernel(Cast,inputs,attrs)}var cast=op({cast_});function clone_(x){let inputs={x:convertToTensor(x,"x","clone","string_or_numeric")};return ENGINE.runKernel(Identity,inputs)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer);let modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);if(perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1){let result={modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy};modelJSON.signature!=null&&(result.signature=modelJSON.signature),modelJSON.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelJSON.userDefinedMetadata),modelJSON.modelInitializer!=null&&(result.modelInitializer=modelJSON.modelInitializer),resolve(result)}},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&(!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME))?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,bufferPromises=(loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction)).map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1;return loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction)}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){return weightsLoaderFactory(fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}))(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
-Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2<numBuffers;i2++)groupBytes+=buffers[bufferIndexOffset+i2].byteLength;let groupBuffer=new ArrayBuffer(groupBytes),groupByteBuffer=new Uint8Array(groupBuffer),groupBufferOffset=0;for(let i2=0;i2<numBuffers;i2++){let buffer2=new Uint8Array(buffers[bufferIndexOffset+i2]);groupByteBuffer.set(buffer2,groupBufferOffset),groupBufferOffset+=buffer2.byteLength}groupWeightsToFetch[i].forEach(weightsEntry=>{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_});var browser_exports={};__export(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}return tensor3d(values,[height,width,numChannels],"int32")}async function toPixels(img,canvas){let $img=convertToTensor(img,"img","toPixels");if(!(img instanceof Tensor)){let originalImgTensor=$img;$img=cast(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;i<indicesShape.length-1;++i)nResult*=indicesShape[i];let inputShape=tensor2.shape,resultShape=indicesShape.slice();resultShape.pop();let sliceSize=1;for(let i=sliceRank;i<tensorRank;++i)sliceSize*=inputShape[i],resultShape.push(inputShape[i]);let strides=[...computeStrides(tensor2.shape).map(stride=>stride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank<batchDim)throw new Error(shapeError+` update.rank < ${batchDim}. `);if(shape.length<sliceDim+(updates.rank-batchDim))throw new Error(shapeError+` Output shape length < ${sliceDim+(updates.rank-batchDim)}`);if(updates.rank!==batchDim+shape.length-sliceDim)throw new Error(shapeError+` update.rank != ${batchDim+shape.length-sliceDim}`);for(let d=0;d<batchDim;++d)if(updates.shape[d]!==indices.shape[d])throw new Error(shapeError+` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);for(let d=0;d<updates.rank-batchDim;++d)if(updates.shape[d+batchDim]!==shape[d+sliceDim])throw new Error(shapeError+` updates.shape[${d+batchDim}] (${updates.shape[d+batchDim]}) != shape[${d+batchDim}] (${shape[d+batchDim]})`)}function validateInput(updates,indices,shape){if(indices.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(updates.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);if(indices.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);if(shape.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);if(shape.length===0){if(indices.size===0)throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);if(updates.size===0)throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`)}validateUpdateShape(shape,indices,updates)}function calculateShapes(updates,indices,shape){let indicesRank=indices.shape.length,sliceRank=indicesRank>1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;i<totalNd;++i)sliceSize*=shape[i];let safeSliceDim=sliceRank<1?1:sliceRank,numUpdates=sizeFromShape(indices.shape)/safeSliceDim,strides=[...computeStrides(shape.slice(0,sliceRank)),1],outputSize=sizeFromShape(shape);return{sliceRank,numUpdates,sliceSize,strides,outputSize}}var slice_util_exports={};__export(slice_util_exports,{assertParamsValid:()=>assertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i<size.length;i++)if(size[i]>1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i<size.length;i++)if(begin[i]>0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i<begin.length-1;i++)flatOffset+=begin[i]*strides[i];return flatOffset}function parseSliceParams(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config){return new cls(config)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon3){return epsilon3==null&&(epsilon3=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon3))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
+`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape(shape),values!=null){let n=values.length;assert(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn().makeTensor(this.values,this.shape,this.dtype)}},trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(fn){trackerFn=fn}function setOpHandler(handler){opHandler=handler}function setDeprecationWarningFn(fn){deprecationWarningFn=fn}var Tensor=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape(shape),this.strides=computeStrides(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray(this.shape,vals)}arraySync(){return toNestedArray(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor2,tensorList){return tensorList.some(x=>x.id===tensor2.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container,list,seen){if(container==null)return;if(container instanceof Tensor){list.push(container);return}if(!isIterable(container))return;let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(k=>k.name)))}}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i];if(await this.initializeBackend(backendName).success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName);if(!(asyncInit?await success:success))return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler(this.backendInstance),!0}setupRegisteredKernels(){getKernelsForBackend(this.backendName).forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){getKernelsForBackend(backendName).forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend2=registryFactoryEntry.factory();if(backend2&&!(backend2 instanceof KernelBackend)&&typeof backend2.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend2.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend2,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend2,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend2,backend2.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend2=>backend2.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3;this.backendName==null&&this.backend;let kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(outInfo=>{if(outInfo.rank!=null)return outInfo;let{dataId,shape,dtype}=outInfo;return this.makeTensorFromDataId(dataId,shape,dtype)});if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{if(forwardFunc==null)throw new Error(`Error running ${kernelName}: Neither modular kernel nor forward func passed`);let saveFunc=tensors=>{!isTapeOn||(saved=tensors.map(tensor2=>this.keep(this.clone(tensor2))))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){return tensors.map(tensor2=>this.keep(this.clone(tensor2)))}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend2){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend2=backend2||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend2.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend2),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend2){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend2),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend2){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend2||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId);info.refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor2=this.state.activeScope.track[i];!tensor2.kept&&!tensorsToTrackInParentSet.has(tensor2.id)&&tensor2.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor2=>{!tensor2.kept&&tensor2.scopeId===oldScope.id&&this.track(tensor2)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node=>{for(let tensor2 of node.saved)tensor2.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){return this.state.tensorInfo.get(dataId).backend.readSync(dataId)}read(dataId){return this.state.tensorInfo.get(dataId).backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment=new Environment(ns);ns._tfengine=new Engine(environment)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernel(Add,inputs)}var device_util_exports={};__export(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0,4))}return!1}function isBrowser(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var ENV2=env();ENV2.registerFlag("DEBUG",()=>!1,debugValue=>{debugValue&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency(val[i],subShape,indices.concat(i))}function assertDtype(expectedDtype,actualDType,argName,functionName){if(expectedDtype!=="string_or_numeric"){if(expectedDtype==null)throw new Error("Expected dtype cannot be null.");if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}}function convertToTensor(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor)return assertDtype(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],!0);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);return arg.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName,parseAsDtype))}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real4,imag4){let $real=convertToTensor(real4,"real","complex"),$imag=convertToTensor(imag4,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let inputs={real:$real,imag:$imag};return ENGINE.runKernel(Complex,inputs)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape(shape.slice(i)):!0;assert(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};var NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor2=>tensor2.name):Object.keys(tensors);for(let i=0;i<names.length;++i){let name=names[i],t=Array.isArray(tensors)?tensors[i].tensor:tensors[name];if(t.dtype!=="float32"&&t.dtype!=="int32"&&t.dtype!=="bool"&&t.dtype!=="string"&&t.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);let spec={name,shape:t.shape,dtype:t.dtype};if(t.dtype==="string"){let utf8bytes=new Promise(async resolve=>{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2<vals.length;i2++){let val=vals[i2],bytesOfLength=new Uint8Array(new Uint32Array([val.length]).buffer);bytes.set(bytesOfLength,offset),offset+=NUM_BYTES_STRING_LENGTH,bytes.set(val,offset),offset+=val.length}resolve(bytes)});dataPromises.push(utf8bytes)}else dataPromises.push(t.data());group!=null&&(spec.group=group),specs.push(spec)}let tensorValues=await Promise.all(dataPromises);return{data:concatenateTypedArrays(tensorValues),specs}}function decodeWeights(buffer2,specs){let out={},float16Decode,offset=0;for(let spec of specs){let name=spec.name,dtype=spec.dtype,shape=spec.shape,size=sizeFromShape(shape),values;if("quantization"in spec){let quantization=spec.quantization;if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){if(!("min"in quantization&&"scale"in quantization))throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`)}else if(quantization.dtype==="float16"){if(dtype!=="float32")throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`)}else throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let quantizationSizeFactor=DTYPE_VALUE_SIZE_MAP[quantization.dtype],byteBuffer=buffer2.slice(offset,offset+size*quantizationSizeFactor),quantizedArray=quantization.dtype==="uint8"?new Uint8Array(byteBuffer):new Uint16Array(byteBuffer);if(dtype==="float32")if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){values=new Float32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=v*quantization.scale+quantization.min}}else if(quantization.dtype==="float16")float16Decode===void 0&&(float16Decode=getFloat16Decoder()),values=float16Decode(quantizedArray);else throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);else if(dtype==="int32"){if(quantization.dtype!=="uint8"&&quantization.dtype!=="uint16")throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);values=new Int32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=Math.round(v*quantization.scale+quantization.min)}}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*quantizationSizeFactor}else if(dtype==="string"){let size2=sizeFromShape(spec.shape);values=[];for(let i=0;i<size2;i++){let byteLength=new Uint32Array(buffer2.slice(offset,offset+NUM_BYTES_STRING_LENGTH))[0];offset+=NUM_BYTES_STRING_LENGTH;let bytes=new Uint8Array(buffer2.slice(offset,offset+byteLength));values.push(bytes),offset+=byteLength}}else{let dtypeFactor=DTYPE_VALUE_SIZE_MAP[dtype],byteBuffer=buffer2.slice(offset,offset+size*dtypeFactor);if(dtype==="float32")values=new Float32Array(byteBuffer);else if(dtype==="int32")values=new Int32Array(byteBuffer);else if(dtype==="bool")values=new Uint8Array(byteBuffer);else if(dtype==="complex64"){values=new Float32Array(byteBuffer);let real4=new Float32Array(values.length/2),image3=new Float32Array(values.length/2);for(let i=0;i<real4.length;i++)real4[i]=values[i*2],image3[i]=values[i*2+1];let realTensor=tensor(real4,shape,"float32"),imageTensor=tensor(image3,shape,"float32");out[name]=complex(realTensor,imageTensor),realTensor.dispose(),imageTensor.dispose()}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*dtypeFactor}dtype!=="complex64"&&(out[name]=tensor(values,shape,dtype))}return out}function concatenateTypedArrays(xs){if(xs===null)throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);let totalByteLength=0,normalizedXs=[];xs.forEach(x=>{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer2){if(useNodeBuffer)return Buffer.from(buffer2).toString("base64");let buf=new Uint8Array(buffer2),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer(str){if(useNodeBuffer){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer2=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer2.set([s.charCodeAt(i)],i);return buffer2.buffer}function concatenateArrayBuffers(buffers){if(buffers.length===1)return buffers[0];let totalByteLength=0;buffers.forEach(buffer2=>{totalByteLength+=buffer2.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer2=>{temp.set(new Uint8Array(buffer2),offset),offset+=buffer2.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)==0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer2=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer2);for(let index=0;index<quantizedArray.length;index++){let float16Bits=quantizedArray[index],float32Bits=mantisaTable[offsetTable[float16Bits>>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer2)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[];return(handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters).forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions);var DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),getRequest=modelTx.objectStore(MODEL_STORE_NAME).get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let putModelRequest=modelTx.objectStore(MODEL_STORE_NAME).put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),getAllInfoRequest=tx.objectStore(INFO_STORE_NAME).getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let deleteModelRequest=modelTx.objectStore(MODEL_STORE_NAME).delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}};var PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData));let result={format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy};return modelArtifacts.signature!=null&&(result.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(result.modelInitializer=modelArtifacts.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(result)),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,metadata.signature!=null&&(out.signature=metadata.signature),metadata.userDefinedMetadata!=null&&(out.userDefinedMetadata=metadata.userDefinedMetadata),metadata.modelInitializer!=null&&(out.modelInitializer=metadata.modelInitializer)}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme2(path);let keys=getModelKeys(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}};var URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry.instance==null&&(ModelStoreManagerRegistry.instance=new ModelStoreManagerRegistry),ModelStoreManagerRegistry.instance}static registerManager(scheme,manager){assert(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url);return ModelStoreManagerRegistry.getManager(schemeAndPath.scheme).removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!1)}async function moveModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!0)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}now(){return performance.now()}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch;var PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernel(Cast,inputs,attrs)}var cast=op({cast_});function clone_(x){let inputs={x:convertToTensor(x,"x","clone","string_or_numeric")};return ENGINE.runKernel(Identity,inputs)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer);let modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);if(perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1){let result={modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy};modelJSON.signature!=null&&(result.signature=modelJSON.signature),modelJSON.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelJSON.userDefinedMetadata),modelJSON.modelInitializer!=null&&(result.modelInitializer=modelJSON.modelInitializer),resolve(result)}},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,bufferPromises=(loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction)).map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1;return loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction)}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){return weightsLoaderFactory(fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}))(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
+Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2<numBuffers;i2++)groupBytes+=buffers[bufferIndexOffset+i2].byteLength;let groupBuffer=new ArrayBuffer(groupBytes),groupByteBuffer=new Uint8Array(groupBuffer),groupBufferOffset=0;for(let i2=0;i2<numBuffers;i2++){let buffer2=new Uint8Array(buffers[bufferIndexOffset+i2]);groupByteBuffer.set(buffer2,groupBufferOffset),groupBufferOffset+=buffer2.byteLength}groupWeightsToFetch[i].forEach(weightsEntry=>{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_});var browser_exports={};__export(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1,isImageBitmap=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else if(typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap)isImageBitmap=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}return tensor3d(values,[height,width,numChannels],"int32")}async function toPixels(img,canvas){let $img=convertToTensor(img,"img","toPixels");if(!(img instanceof Tensor)){let originalImgTensor=$img;$img=cast(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_});var gather_nd_util_exports={};__export(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;i<indicesShape.length-1;++i)nResult*=indicesShape[i];let inputShape=tensor2.shape,resultShape=indicesShape.slice();resultShape.pop();let sliceSize=1;for(let i=sliceRank;i<tensorRank;++i)sliceSize*=inputShape[i],resultShape.push(inputShape[i]);let strides=[...computeStrides(tensor2.shape).map(stride=>stride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank<batchDim)throw new Error(shapeError+` update.rank < ${batchDim}. `);if(shape.length<sliceDim+(updates.rank-batchDim))throw new Error(shapeError+` Output shape length < ${sliceDim+(updates.rank-batchDim)}`);if(updates.rank!==batchDim+shape.length-sliceDim)throw new Error(shapeError+` update.rank != ${batchDim+shape.length-sliceDim}`);for(let d=0;d<batchDim;++d)if(updates.shape[d]!==indices.shape[d])throw new Error(shapeError+` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);for(let d=0;d<updates.rank-batchDim;++d)if(updates.shape[d+batchDim]!==shape[d+sliceDim])throw new Error(shapeError+` updates.shape[${d+batchDim}] (${updates.shape[d+batchDim]}) != shape[${d+batchDim}] (${shape[d+batchDim]})`)}function validateInput(updates,indices,shape){if(indices.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(updates.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);if(indices.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);if(shape.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);if(shape.length===0){if(indices.size===0)throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);if(updates.size===0)throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`)}validateUpdateShape(shape,indices,updates)}function calculateShapes(updates,indices,shape){let indicesRank=indices.shape.length,sliceRank=indicesRank>1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;i<totalNd;++i)sliceSize*=shape[i];let safeSliceDim=sliceRank<1?1:sliceRank,numUpdates=sizeFromShape(indices.shape)/safeSliceDim,strides=[...computeStrides(shape.slice(0,sliceRank)),1],outputSize=sizeFromShape(shape);return{sliceRank,numUpdates,sliceSize,strides,outputSize}}var slice_util_exports={};__export(slice_util_exports,{assertParamsValid:()=>assertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i<size.length;i++)if(size[i]>1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i<size.length;i++)if(begin[i]>0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i<begin.length-1;i++)flatOffset+=begin[i]*strides[i];return flatOffset}function parseSliceParams(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config){return new cls(config)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon3){return epsilon3==null&&(epsilon3=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon3))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
 Actual:   ${actualFlat}.
 Expected: ${expectedFlat}.`);for(let i=0;i<expectedFlat.length;++i){let a=actualFlat[i],e=expectedFlat[i];if(!predicate(a,e))throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
 Actual:   ${actualFlat}.
-Expected: ${expectedFlat}.`)}}function expectPromiseToFail(fn,done){fn().then(()=>done.fail(),()=>done())}function expectArraysEqual(actual,expected){let exp4=typeof expected=="string"||typeof expected=="number"||typeof expected=="boolean"?[expected]:expected;return isString(actual)||isString(actual[0])||isString(expected)||isString(expected[0])?expectArraysPredicate(actual,exp4,(a,b)=>a==b):expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,0))}function expectNumbersClose(a,e,epsilon3){if(epsilon3==null&&(epsilon3=testEpsilon()),!areClose(a,e,epsilon3))throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`)}function areClose(a,e,epsilon3){return!isFinite(a)&&!isFinite(e)?!0:!(isNaN(a)||isNaN(e)||Math.abs(a-e)>epsilon3)}function expectValuesInRange(actual,low,high){for(let i=0;i<actual.length;i++)if(actual[i]<low||actual[i]>high)throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`)}function expectArrayBuffersEqual(actual,expected){expect(new Float32Array(actual)).toEqual(new Float32Array(expected))}function encodeStrings(a){for(let i=0;i<a.length;i++){let val=a[i];Array.isArray(val)?encodeStrings(val):a[i]=encodeString(val)}return a}var version="2.8.2";function enableProdMode(){env().set("PROD",!0)}function enableDebugMode(){env().set("DEBUG",!0)}function disableDeprecationWarnings(){env().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function deprecationWarn(msg){env().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn(deprecationWarn);function disposeVariables(){ENGINE.disposeVariables()}function engine(){return ENGINE}function memory(){return ENGINE.memory()}function profile(f){return ENGINE.profile(f)}function tidy(nameOrFn,fn){return ENGINE.tidy(nameOrFn,fn)}function dispose(container){getTensorsInContainer(container).forEach(tensor2=>tensor2.dispose())}function keep(result){return ENGINE.keep(result)}function time(f){return ENGINE.time(f)}function setBackend(backendName){return ENGINE.setBackend(backendName)}function ready(){return ENGINE.ready()}function getBackend(){return ENGINE.backendName}function removeBackend(name){ENGINE.removeBackend(name)}function findBackend(name){return ENGINE.findBackend(name)}function findBackendFactory(name){return ENGINE.findBackendFactory(name)}function registerBackend(name,factory,priority=1){return ENGINE.registerBackend(name,factory,priority)}function backend(){return ENGINE.backend}function setPlatform(platformName,platform){env().setPlatform(platformName,platform)}function add_(a,b){let $a=convertToTensor(a,"a","add"),$b=convertToTensor(b,"b","add");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Add,inputs)}var add2=op({add_});function floorDiv_(a,b){let $a=convertToTensor(a,"a","floorDiv"),$b=convertToTensor(b,"b","floorDiv");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(FloorDiv,inputs)}var floorDiv=op({floorDiv_});function div_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");if([$a,$b]=makeTypesMatch($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv($a,$b);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(RealDiv,inputs,attrs)}var div=op({div_});function mul_(a,b){let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Multiply,inputs)}var mul=op({mul_});function abs_(x){let $x=convertToTensor(x,"x","abs");if($x.dtype==="complex64"){let inputs={x:$x};return ENGINE.runKernel(ComplexAbs,inputs)}else{let inputs={x:$x};return ENGINE.runKernel(Abs,inputs)}}var abs=op({abs_});function acos_(x){let inputs={x:convertToTensor(x,"x","acos")};return ENGINE.runKernel(Acos,inputs)}var acos=op({acos_});function acosh_(x){let inputs={x:convertToTensor(x,"x","acosh")};return ENGINE.runKernel(Acosh,inputs)}var acosh=op({acosh_});function addN_(tensors){assert(Array.isArray(tensors),()=>"The argument passed to tf.addN() must be a list of tensors"),assert(tensors.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);let $tensors=tensors.map((t,i)=>convertToTensor(t,`tensors${i}`,"addN")),firstTensor=$tensors[0];$tensors.forEach(t=>{if(t.dtype!==firstTensor.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),$tensors.forEach(t=>{if(!arraysEqual(t.shape,firstTensor.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let inputs=$tensors;return ENGINE.runKernel(AddN,inputs)}var addN=op({addN_});function all_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","all","bool")},attrs={axis,keepDims};return ENGINE.runKernel(All,inputs,attrs)}var all=op({all_});function any_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","any","bool")},attrs={axis,keepDims};return ENGINE.runKernel(Any,inputs,attrs)}var any=op({any_});function argMax_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMax")},attrs={axis};return ENGINE.runKernel(ArgMax,inputs,attrs)}var argMax=op({argMax_});function argMin_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMin")},attrs={axis};return ENGINE.runKernel(ArgMin,inputs,attrs)}var argMin=op({argMin_});function asin_(x){let inputs={x:convertToTensor(x,"x","asin")};return ENGINE.runKernel(Asin,inputs)}var asin=op({asin_});function asinh_(x){let inputs={x:convertToTensor(x,"x","asinh")};return ENGINE.runKernel(Asinh,inputs)}var asinh=op({asinh_});function atan_(x){let inputs={x:convertToTensor(x,"x","atan")};return ENGINE.runKernel(Atan,inputs)}var atan=op({atan_});function atan2_(a,b){let $a=convertToTensor(a,"a","atan2"),$b=convertToTensor(b,"b","atan2");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Atan2,inputs)}var atan2=op({atan2_});function atanh_(x){let inputs={x:convertToTensor(x,"x","atanh")};return ENGINE.runKernel(Atanh,inputs)}var atanh=op({atanh_});function computeDilation2DInfo(inputShape,filterShape,strides,pad3,dataFormat="NHWC",dilations){let inputChannels=inputShape[3],$filterShape=[...filterShape,inputChannels],$dataFormat=convertConv2DDataFormat(dataFormat);return computeConv2DInfo(inputShape,$filterShape,strides,dilations,pad3,null,null,$dataFormat)}function computePool2DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,!1,dataFormat)}function computePool3DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,!1,$dataFormat,roundingMode)}function computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam(strides),[dilationHeight,dilationWidth]=parseTupleParam(dilations),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam(dilations),effectiveFilterDepth=getEffectiveFilterSize(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputRows,outputCols]}function computeOutputShape4D(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=round((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode),outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape2D([inHeight,inWidth],filterHeight,strideHeight,pad3,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad3==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad3=="object"){let top=dataFormat==="channelsLast"?pad3[1][0]:pad3[2][0],bottom=dataFormat==="channelsLast"?pad3[1][1]:pad3[2][1],left=dataFormat==="channelsLast"?pad3[2][0]:pad3[3][0],right=dataFormat==="channelsLast"?pad3[2][1]:pad3[3][1];padInfo={top,bottom,left,right,type:top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT"},outHeight=round((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=round((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,front:pad3,back:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape4D([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad3,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad3==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outDepth,outHeight,outWidth}}function round(value,roundingMode){if(!roundingMode)return Math.trunc(value);switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne(param){let[dimA,dimB,dimC]=parseTupleParam(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne(strides,dilations){return tupleValuesAreOne(strides)||tupleValuesAreOne(dilations)}function convertConv2DDataFormat(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function reshape_(x,shape){let inputs={x:convertToTensor(x,"x","reshape","string_or_numeric")},attrs={shape};return ENGINE.runKernel(Reshape,inputs,attrs)}var reshape=op({reshape_});function avgPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","avgPool","float32"),dilations=1;assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool,inputs,attrs);return res=cast(res,$x.dtype),reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool=op({avgPool_});function avgPool3d_(x,filterSize,strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","avgPool3d","float32"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(AvgPool3D,inputs,attrs);return res=cast(res,x5D.dtype),reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3d=op({avgPool3d_});function concat_(tensors,axis=0){assert(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray(tensors,"tensors","concat","string_or_numeric");if($tensors[0].dtype==="complex64"&&$tensors.forEach(tensor2=>{if(tensor2.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
-          with dtype ${tensor2.dtype}. `)}),$tensors.length===1)return clone($tensors[0]);let inputs=$tensors,attr={axis};return ENGINE.runKernel(Concat,inputs,attr)}var concat=op({concat_});function sigmoid_(x){let inputs={x:convertToTensor(x,"x","sigmoid")};return ENGINE.runKernel(Sigmoid,inputs)}var sigmoid=op({sigmoid_});function slice_(x,begin,size){let $x=convertToTensor(x,"x","slice","string_or_numeric");if($x.rank===0)throw new Error("Slicing scalar is not possible");let inputs={x:$x},attrs={begin,size};return ENGINE.runKernel(Slice,inputs,attrs)}var slice=op({slice_});function tanh_(x){let inputs={x:convertToTensor(x,"x","tanh")};return ENGINE.runKernel(Tanh,inputs)}var tanh2=op({tanh_});function basicLSTMCell_(forgetBias,lstmKernel,lstmBias,data,c,h){let $forgetBias=convertToTensor(forgetBias,"forgetBias","basicLSTMCell"),$lstmKernel=convertToTensor(lstmKernel,"lstmKernel","basicLSTMCell"),$lstmBias=convertToTensor(lstmBias,"lstmBias","basicLSTMCell"),$data=convertToTensor(data,"data","basicLSTMCell"),$c=convertToTensor(c,"c","basicLSTMCell"),$h=convertToTensor(h,"h","basicLSTMCell"),combined=concat([$data,$h],1),weighted=matMul(combined,$lstmKernel),res=add2(weighted,$lstmBias),batchSize=res.shape[0],sliceCols=res.shape[1]/4,sliceSize=[batchSize,sliceCols],i=slice(res,[0,0],sliceSize),j=slice(res,[0,sliceCols],sliceSize),f=slice(res,[0,sliceCols*2],sliceSize),o=slice(res,[0,sliceCols*3],sliceSize),newC=add2(mul(sigmoid(i),tanh2(j)),mul($c,sigmoid(add2($forgetBias,f)))),newH=mul(tanh2(newC),sigmoid(o));return[newC,newH]}var basicLSTMCell=op({basicLSTMCell_});function batchToSpaceND_(x,blockShape,crops){let $x=convertToTensor(x,"x","batchToSpaceND"),prod5=blockShape.reduce((a,b)=>a*b);assert($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert($x.shape[0]%prod5===0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`);let inputs={x:$x},attrs={blockShape,crops};return ENGINE.runKernel(BatchToSpaceND,inputs,attrs)}var batchToSpaceND=op({batchToSpaceND_});function xAs4D(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape(x,[1,1,1,x.size]):x.rank===2?x4D=reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_(x,mean4,variance,offset,scale2,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let inputs={x:xAs4D($x),scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE.runKernel(FusedBatchNorm,inputs,attrs);return reshape(res,$x.shape)}var batchNorm=op({batchNorm_});function batchNorm2d_(x,mean4,variance,offset,scale2,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`),assert($mean.rank===2||$mean.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===2||$variance.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===2||$scale.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===2||$offset.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm2d=op({batchNorm2d_});function batchNorm3d_(x,mean4,variance,offset,scale2,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`),assert($mean.rank===3||$mean.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===3||$variance.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===3||$scale.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===3||$offset.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm3d=op({batchNorm3d_});function batchNorm4d_(x,mean4,variance,offset,scale2,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`),assert($mean.rank===4||$mean.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===4||$variance.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===4||$scale.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===4||$offset.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm4d=op({batchNorm4d_});function bincount_(x,weights,size){let $x=convertToTensor(x,"x","bincount"),$weights=convertToTensor(weights,"weights","bincount");assert($x.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${$x.dtype}`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size};return ENGINE.runKernel(Bincount,inputs,attrs)}var bincount=op({bincount_});function broadcastTo_(x,shape){let input2=convertToTensor(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);if(reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0).length===0)return clone(input2);let inputs={x:input2},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var broadcastTo=op({broadcastTo_});function ceil_(x){let inputs={x:convertToTensor(x,"x","ceil")};return ENGINE.runKernel(Ceil,inputs)}var ceil=op({ceil_});function clipByValue_(x,clipValueMin,clipValueMax){let $x=convertToTensor(x,"x","clipByValue");assert(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE.runKernel(ClipByValue,inputs,attrs)}var clipByValue=op({clipByValue_});function concat1d_(tensors){return concat(tensors,0)}var concat1d=op({concat1d_});function concat2d_(tensors,axis){return concat(tensors,axis)}var concat2d=op({concat2d_});function concat3d_(tensors,axis){return concat(tensors,axis)}var concat3d=op({concat3d_});function concat4d_(tensors,axis){return concat(tensors,axis)}var concat4d=op({concat4d_});function conv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(Conv2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d=op({conv2d_});function conv1d_(x,filter,stride,pad3,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor(x,"x","conv1d"),$filter=convertToTensor(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape($x,[1,$x.shape[0],$x.shape[1]])),assert(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert(eitherStridesOrDilationsAreOne(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),res=conv2d(input4D,filter4D,[1,stride],pad3,"NHWC",[1,dilation],dimRoundingMode);return reshapedTo3D?reshape(res,[res.shape[2],res.shape[3]]):reshape(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d=op({conv1d_});function conv2DBackpropInput_(xShape,dy,filter,strides,pad3,dataFormat="NHWC",dimRoundingMode){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE.runKernel(Conv2DBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput=op({conv2DBackpropInput_});function conv2dTranspose_(x,filter,outputShape,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","conv2dTranspose"),$filter=convertToTensor(filter,"filter","conv2dTranspose");return conv2DBackpropInput(outputShape,$x,$filter,strides,pad3,"NHWC",dimRoundingMode)}var conv2dTranspose=op({conv2dTranspose_});function conv3d_(x,filter,strides,pad3,dataFormat="NDHWC",dilations=[1,1,1]){let $x=convertToTensor(x,"x","conv3d"),$filter=convertToTensor(filter,"filter","conv3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`),assert($filter.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`),assert(x5D.shape[4]===$filter.shape[3],()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NDHWC",()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);let inputs={x:x5D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations},res=ENGINE.runKernel(Conv3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3d=op({conv3d_});function conv3DBackpropInput_(xShape,dy,filter,strides,pad3){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let inputs={dy:dy5D,filter},attrs={pad:pad3,strides,inputShape:xShape5D},res=ENGINE.runKernel(Conv3DBackpropInputV2,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput=op({conv3DBackpropInput_});function conv3dTranspose_(x,filter,outputShape,strides,pad3){let $x=convertToTensor(x,"x","conv3dTranspose"),$filter=convertToTensor(filter,"filter","conv3dTranspose");return conv3DBackpropInput(outputShape,$x,$filter,strides,pad3)}var conv3dTranspose=op({conv3dTranspose_});function cos_(x){let inputs={x:convertToTensor(x,"x","cos")};return ENGINE.runKernel(Cos,inputs)}var cos=op({cos_});function cosh_(x){let inputs={x:convertToTensor(x,"x","cosh")};return ENGINE.runKernel(Cosh,inputs)}var cosh=op({cosh_});function cumsum_(x,axis=0,exclusive=!1,reverse5=!1){let inputs={x:convertToTensor(x,"x","cumsum")},attrs={axis,exclusive,reverse:reverse5};return ENGINE.runKernel(Cumsum,inputs,attrs)}var cumsum=op({cumsum_});function denseBincount_(x,weights,size,binaryOutput=!1){let $x=convertToTensor(x,"x","denseBincount"),$weights=convertToTensor(weights,"weights","denseBincount");assert($x.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`),assert($x.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size,binaryOutput};return ENGINE.runKernel(DenseBincount,inputs,attrs)}var denseBincount=op({denseBincount_});function depthToSpace_(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+Expected: ${expectedFlat}.`)}}function expectPromiseToFail(fn,done){fn().then(()=>done.fail(),()=>done())}function expectArraysEqual(actual,expected){let exp4=typeof expected=="string"||typeof expected=="number"||typeof expected=="boolean"?[expected]:expected;return isString(actual)||isString(actual[0])||isString(expected)||isString(expected[0])?expectArraysPredicate(actual,exp4,(a,b)=>a==b):expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,0))}function expectNumbersClose(a,e,epsilon3){if(epsilon3==null&&(epsilon3=testEpsilon()),!areClose(a,e,epsilon3))throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`)}function areClose(a,e,epsilon3){return!isFinite(a)&&!isFinite(e)?!0:!(isNaN(a)||isNaN(e)||Math.abs(a-e)>epsilon3)}function expectValuesInRange(actual,low,high){for(let i=0;i<actual.length;i++)if(actual[i]<low||actual[i]>high)throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`)}function expectArrayBuffersEqual(actual,expected){expect(new Float32Array(actual)).toEqual(new Float32Array(expected))}function encodeStrings(a){for(let i=0;i<a.length;i++){let val=a[i];Array.isArray(val)?encodeStrings(val):a[i]=encodeString(val)}return a}var version="2.8.3";function enableProdMode(){env().set("PROD",!0)}function enableDebugMode(){env().set("DEBUG",!0)}function disableDeprecationWarnings(){env().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function deprecationWarn(msg){env().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn(deprecationWarn);function disposeVariables(){ENGINE.disposeVariables()}function engine(){return ENGINE}function memory(){return ENGINE.memory()}function profile(f){return ENGINE.profile(f)}function tidy(nameOrFn,fn){return ENGINE.tidy(nameOrFn,fn)}function dispose(container){getTensorsInContainer(container).forEach(tensor2=>tensor2.dispose())}function keep(result){return ENGINE.keep(result)}function time(f){return ENGINE.time(f)}function setBackend(backendName){return ENGINE.setBackend(backendName)}function ready(){return ENGINE.ready()}function getBackend(){return ENGINE.backendName}function removeBackend(name){ENGINE.removeBackend(name)}function findBackend(name){return ENGINE.findBackend(name)}function findBackendFactory(name){return ENGINE.findBackendFactory(name)}function registerBackend(name,factory,priority=1){return ENGINE.registerBackend(name,factory,priority)}function backend(){return ENGINE.backend}function setPlatform(platformName,platform){env().setPlatform(platformName,platform)}function add_(a,b){let $a=convertToTensor(a,"a","add"),$b=convertToTensor(b,"b","add");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Add,inputs)}var add2=op({add_});function floorDiv_(a,b){let $a=convertToTensor(a,"a","floorDiv"),$b=convertToTensor(b,"b","floorDiv");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(FloorDiv,inputs)}var floorDiv=op({floorDiv_});function div_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");if([$a,$b]=makeTypesMatch($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv($a,$b);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(RealDiv,inputs,attrs)}var div=op({div_});function mul_(a,b){let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Multiply,inputs)}var mul=op({mul_});function abs_(x){let $x=convertToTensor(x,"x","abs");if($x.dtype==="complex64"){let inputs={x:$x};return ENGINE.runKernel(ComplexAbs,inputs)}else{let inputs={x:$x};return ENGINE.runKernel(Abs,inputs)}}var abs=op({abs_});function acos_(x){let inputs={x:convertToTensor(x,"x","acos")};return ENGINE.runKernel(Acos,inputs)}var acos=op({acos_});function acosh_(x){let inputs={x:convertToTensor(x,"x","acosh")};return ENGINE.runKernel(Acosh,inputs)}var acosh=op({acosh_});function addN_(tensors){assert(Array.isArray(tensors),()=>"The argument passed to tf.addN() must be a list of tensors"),assert(tensors.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);let $tensors=tensors.map((t,i)=>convertToTensor(t,`tensors${i}`,"addN")),firstTensor=$tensors[0];$tensors.forEach(t=>{if(t.dtype!==firstTensor.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),$tensors.forEach(t=>{if(!arraysEqual(t.shape,firstTensor.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let inputs=$tensors;return ENGINE.runKernel(AddN,inputs)}var addN=op({addN_});function all_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","all","bool")},attrs={axis,keepDims};return ENGINE.runKernel(All,inputs,attrs)}var all=op({all_});function any_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","any","bool")},attrs={axis,keepDims};return ENGINE.runKernel(Any,inputs,attrs)}var any=op({any_});function argMax_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMax")},attrs={axis};return ENGINE.runKernel(ArgMax,inputs,attrs)}var argMax=op({argMax_});function argMin_(x,axis=0){let inputs={x:convertToTensor(x,"x","argMin")},attrs={axis};return ENGINE.runKernel(ArgMin,inputs,attrs)}var argMin=op({argMin_});function asin_(x){let inputs={x:convertToTensor(x,"x","asin")};return ENGINE.runKernel(Asin,inputs)}var asin=op({asin_});function asinh_(x){let inputs={x:convertToTensor(x,"x","asinh")};return ENGINE.runKernel(Asinh,inputs)}var asinh=op({asinh_});function atan_(x){let inputs={x:convertToTensor(x,"x","atan")};return ENGINE.runKernel(Atan,inputs)}var atan=op({atan_});function atan2_(a,b){let $a=convertToTensor(a,"a","atan2"),$b=convertToTensor(b,"b","atan2");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Atan2,inputs)}var atan2=op({atan2_});function atanh_(x){let inputs={x:convertToTensor(x,"x","atanh")};return ENGINE.runKernel(Atanh,inputs)}var atanh=op({atanh_});function computeDilation2DInfo(inputShape,filterShape,strides,pad3,dataFormat="NHWC",dilations){let inputChannels=inputShape[3],$filterShape=[...filterShape,inputChannels],$dataFormat=convertConv2DDataFormat(dataFormat);return computeConv2DInfo(inputShape,$filterShape,strides,dilations,pad3,null,null,$dataFormat)}function computePool2DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,!1,dataFormat)}function computePool3DInfo(inShape,filterSize,strides,dilations,pad3,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,!1,$dataFormat,roundingMode)}function computeConv2DInfo(inShape,filterShape,strides,dilations,pad3,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam(strides),[dilationHeight,dilationWidth]=parseTupleParam(dilations),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo(inShape,filterShape,strides,dilations,pad3,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam(dilations),effectiveFilterDepth=getEffectiveFilterSize(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputRows,outputCols]}function computeOutputShape4D(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=round((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode),outputRows=round((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode),outputCols=round((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo(pad3,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape2D([inHeight,inWidth],filterHeight,strideHeight,pad3,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad3==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad3=="object"){let top=dataFormat==="channelsLast"?pad3[1][0]:pad3[2][0],bottom=dataFormat==="channelsLast"?pad3[1][1]:pad3[2][1],left=dataFormat==="channelsLast"?pad3[2][0]:pad3[3][0],right=dataFormat==="channelsLast"?pad3[2][1]:pad3[3][1];padInfo={top,bottom,left,right,type:top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT"},outHeight=round((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=round((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo(pad3,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad3=="number"){padInfo={top:pad3,bottom:pad3,left:pad3,right:pad3,front:pad3,back:pad3,type:pad3===0?"VALID":"NUMBER"};let outShape=computeOutputShape4D([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad3,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad3==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad3==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad3}`);return{padInfo,outDepth,outHeight,outWidth}}function round(value,roundingMode){if(!roundingMode)return Math.trunc(value);switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne(param){let[dimA,dimB,dimC]=parseTupleParam(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne(strides,dilations){return tupleValuesAreOne(strides)||tupleValuesAreOne(dilations)}function convertConv2DDataFormat(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function reshape_(x,shape){let inputs={x:convertToTensor(x,"x","reshape","string_or_numeric")},attrs={shape};return ENGINE.runKernel(Reshape,inputs,attrs)}var reshape=op({reshape_});function avgPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","avgPool","float32"),dilations=1;assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool,inputs,attrs);return res=cast(res,$x.dtype),reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool=op({avgPool_});function avgPool3d_(x,filterSize,strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","avgPool3d","float32"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(AvgPool3D,inputs,attrs);return res=cast(res,x5D.dtype),reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3d=op({avgPool3d_});function concat_(tensors,axis=0){assert(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray(tensors,"tensors","concat","string_or_numeric");if($tensors[0].dtype==="complex64"&&$tensors.forEach(tensor2=>{if(tensor2.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
+          with dtype ${tensor2.dtype}. `)}),$tensors.length===1)return clone($tensors[0]);let inputs=$tensors,attr={axis};return ENGINE.runKernel(Concat,inputs,attr)}var concat=op({concat_});function sigmoid_(x){let inputs={x:convertToTensor(x,"x","sigmoid")};return ENGINE.runKernel(Sigmoid,inputs)}var sigmoid=op({sigmoid_});function slice_(x,begin,size){let $x=convertToTensor(x,"x","slice","string_or_numeric");if($x.rank===0)throw new Error("Slicing scalar is not possible");let inputs={x:$x},attrs={begin,size};return ENGINE.runKernel(Slice,inputs,attrs)}var slice=op({slice_});function tanh_(x){let inputs={x:convertToTensor(x,"x","tanh")};return ENGINE.runKernel(Tanh,inputs)}var tanh2=op({tanh_});function basicLSTMCell_(forgetBias,lstmKernel,lstmBias,data,c,h){let $forgetBias=convertToTensor(forgetBias,"forgetBias","basicLSTMCell"),$lstmKernel=convertToTensor(lstmKernel,"lstmKernel","basicLSTMCell"),$lstmBias=convertToTensor(lstmBias,"lstmBias","basicLSTMCell"),$data=convertToTensor(data,"data","basicLSTMCell"),$c=convertToTensor(c,"c","basicLSTMCell"),$h=convertToTensor(h,"h","basicLSTMCell"),combined=concat([$data,$h],1),weighted=matMul(combined,$lstmKernel),res=add2(weighted,$lstmBias),batchSize=res.shape[0],sliceCols=res.shape[1]/4,sliceSize=[batchSize,sliceCols],i=slice(res,[0,0],sliceSize),j=slice(res,[0,sliceCols],sliceSize),f=slice(res,[0,sliceCols*2],sliceSize),o=slice(res,[0,sliceCols*3],sliceSize),newC=add2(mul(sigmoid(i),tanh2(j)),mul($c,sigmoid(add2($forgetBias,f)))),newH=mul(tanh2(newC),sigmoid(o));return[newC,newH]}var basicLSTMCell=op({basicLSTMCell_});function batchToSpaceND_(x,blockShape,crops){let $x=convertToTensor(x,"x","batchToSpaceND"),prod5=blockShape.reduce((a,b)=>a*b);assert($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert($x.shape[0]%prod5==0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`);let inputs={x:$x},attrs={blockShape,crops};return ENGINE.runKernel(BatchToSpaceND,inputs,attrs)}var batchToSpaceND=op({batchToSpaceND_});function xAs4D(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape(x,[1,1,1,x.size]):x.rank===2?x4D=reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_(x,mean4,variance,offset,scale2,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let inputs={x:xAs4D($x),scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE.runKernel(FusedBatchNorm,inputs,attrs);return reshape(res,$x.shape)}var batchNorm=op({batchNorm_});function batchNorm2d_(x,mean4,variance,offset,scale2,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`),assert($mean.rank===2||$mean.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===2||$variance.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===2||$scale.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===2||$offset.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm2d=op({batchNorm2d_});function batchNorm3d_(x,mean4,variance,offset,scale2,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`),assert($mean.rank===3||$mean.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===3||$variance.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===3||$scale.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===3||$offset.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm3d=op({batchNorm3d_});function batchNorm4d_(x,mean4,variance,offset,scale2,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean4,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`),assert($mean.rank===4||$mean.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===4||$variance.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===4||$scale.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===4||$offset.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm4d=op({batchNorm4d_});function bincount_(x,weights,size){let $x=convertToTensor(x,"x","bincount"),$weights=convertToTensor(weights,"weights","bincount");assert($x.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${$x.dtype}`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size};return ENGINE.runKernel(Bincount,inputs,attrs)}var bincount=op({bincount_});function broadcastTo_(x,shape){let input2=convertToTensor(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);if(reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0).length===0)return clone(input2);let inputs={x:input2},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var broadcastTo=op({broadcastTo_});function ceil_(x){let inputs={x:convertToTensor(x,"x","ceil")};return ENGINE.runKernel(Ceil,inputs)}var ceil=op({ceil_});function clipByValue_(x,clipValueMin,clipValueMax){let $x=convertToTensor(x,"x","clipByValue");assert(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE.runKernel(ClipByValue,inputs,attrs)}var clipByValue=op({clipByValue_});function concat1d_(tensors){return concat(tensors,0)}var concat1d=op({concat1d_});function concat2d_(tensors,axis){return concat(tensors,axis)}var concat2d=op({concat2d_});function concat3d_(tensors,axis){return concat(tensors,axis)}var concat3d=op({concat3d_});function concat4d_(tensors,axis){return concat(tensors,axis)}var concat4d=op({concat4d_});function conv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(Conv2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d=op({conv2d_});function conv1d_(x,filter,stride,pad3,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor(x,"x","conv1d"),$filter=convertToTensor(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape($x,[1,$x.shape[0],$x.shape[1]])),assert(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert(eitherStridesOrDilationsAreOne(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),res=conv2d(input4D,filter4D,[1,stride],pad3,"NHWC",[1,dilation],dimRoundingMode);return reshapedTo3D?reshape(res,[res.shape[2],res.shape[3]]):reshape(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d=op({conv1d_});function conv2DBackpropInput_(xShape,dy,filter,strides,pad3,dataFormat="NHWC",dimRoundingMode){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE.runKernel(Conv2DBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput=op({conv2DBackpropInput_});function conv2dTranspose_(x,filter,outputShape,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","conv2dTranspose"),$filter=convertToTensor(filter,"filter","conv2dTranspose");return conv2DBackpropInput(outputShape,$x,$filter,strides,pad3,"NHWC",dimRoundingMode)}var conv2dTranspose=op({conv2dTranspose_});function conv3d_(x,filter,strides,pad3,dataFormat="NDHWC",dilations=[1,1,1]){let $x=convertToTensor(x,"x","conv3d"),$filter=convertToTensor(filter,"filter","conv3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`),assert($filter.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`),assert(x5D.shape[4]===$filter.shape[3],()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NDHWC",()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);let inputs={x:x5D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations},res=ENGINE.runKernel(Conv3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3d=op({conv3d_});function conv3DBackpropInput_(xShape,dy,filter,strides,pad3){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let inputs={dy:dy5D,filter},attrs={pad:pad3,strides,inputShape:xShape5D},res=ENGINE.runKernel(Conv3DBackpropInputV2,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput=op({conv3DBackpropInput_});function conv3dTranspose_(x,filter,outputShape,strides,pad3){let $x=convertToTensor(x,"x","conv3dTranspose"),$filter=convertToTensor(filter,"filter","conv3dTranspose");return conv3DBackpropInput(outputShape,$x,$filter,strides,pad3)}var conv3dTranspose=op({conv3dTranspose_});function cos_(x){let inputs={x:convertToTensor(x,"x","cos")};return ENGINE.runKernel(Cos,inputs)}var cos=op({cos_});function cosh_(x){let inputs={x:convertToTensor(x,"x","cosh")};return ENGINE.runKernel(Cosh,inputs)}var cosh=op({cosh_});function cumsum_(x,axis=0,exclusive=!1,reverse5=!1){let inputs={x:convertToTensor(x,"x","cumsum")},attrs={axis,exclusive,reverse:reverse5};return ENGINE.runKernel(Cumsum,inputs,attrs)}var cumsum=op({cumsum_});function denseBincount_(x,weights,size,binaryOutput=!1){let $x=convertToTensor(x,"x","denseBincount"),$weights=convertToTensor(weights,"weights","denseBincount");assert($x.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`),assert($x.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`),assert(size>=0,()=>`size must be non-negative, but got ${size}.`),assert($weights.size===$x.size||$weights.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);let inputs={x:$x,weights:$weights},attrs={size,binaryOutput};return ENGINE.runKernel(DenseBincount,inputs,attrs)}var denseBincount=op({denseBincount_});function depthToSpace_(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
     ${inputHeight} and ${blockSize}  for depthToSpace with input shape
     ${$x.shape}`),assert(inputWidth*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
     ${inputWidth} and ${blockSize} for depthToSpace with input shape
-        ${$x.shape}`),assert(inputDepth%(blockSize*blockSize)===0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernel(DepthToSpace,inputs,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(DepthwiseConv2dNative,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let inputs={x:convertToTensor(x,"x","diag")};return ENGINE.runKernel(Diag,inputs)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad3,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i<inRank;i++){let dim=inRank-1-i,a=inShape[dim]||1;(outShape[outShape.length-1-i]||1)>1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_(a,b){let $a=convertToTensor(a,"a","equal"),$b=convertToTensor(b,"b","equal");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Equal,inputs)}var equal=op({equal_});function where_(condition,a,b){let $a=convertToTensor(a,"a","where"),$b=convertToTensor(b,"b","where"),$condition=convertToTensor(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape($a.shape,$b.shape),$broadcastedA=broadcastTo($a,broadcastShape),$broadcastedB=broadcastTo($b,broadcastShape);$condition.rank===1&&assert($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernel(Select,inputs)}var where=op({where_});function zerosLike_(x){let inputs={x:convertToTensor(x,"x","zerosLike")};return ENGINE.runKernel(ZerosLike,inputs)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros4=zerosLike(divResult),bEqualsZero=equal($b,zeros4);return where(bEqualsZero,zeros4,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]);return matMul($t1,t22D)}}var dot=op({dot_});function elu_(x){let inputs={x:convertToTensor(x,"x","elu")};return ENGINE.runKernel(Elu,inputs)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernel(Erf,inputs)}var erf=op({erf_});function exp_(x){let inputs={x:convertToTensor(x,"x","exp")};return ENGINE.runKernel(Exp,inputs)}var exp=op({exp_});function expandDims_(x,axis=0){let $x=convertToTensor(x,"x","expandDims","string_or_numeric");assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let inputs={input:$x},attrs={dim:axis};return ENGINE.runKernel(ExpandDims,inputs,attrs)}var expandDims=op({expandDims_});function expm1_(x){let inputs={x:convertToTensor(x,"x","expm1")};return ENGINE.runKernel(Expm1,inputs)}var expm1=op({expm1_});function tile_(x,reps){let $x=convertToTensor(x,"x","tile","string_or_numeric");assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let inputs={x:$x},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile(expandDims(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile(expandDims(expandDims(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile(expandDims(expandDims(expandDims(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye=op({eye_});function fill(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE.runKernel(Fill,{},attrs)}function floor_(x){let inputs={x:convertToTensor(x,"x","floor")};return ENGINE.runKernel(Floor,inputs)}var floor=op({floor_});function gather_(x,indices,axis=0,batchDims=0){let $x=convertToTensor(x,"x","gather"),$indices=convertToTensor(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis,batchDims};return ENGINE.runKernel(GatherV2,inputs,attrs)}var gather=op({gather_});function greater_(a,b){let $a=convertToTensor(a,"a","greater"),$b=convertToTensor(b,"b","greater");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Greater,inputs)}var greater=op({greater_});function greaterEqual_(a,b){let $a=convertToTensor(a,"a","greaterEqual"),$b=convertToTensor(b,"b","greaterEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(GreaterEqual,inputs)}var greaterEqual=op({greaterEqual_});function imag_(input2){let inputs={input:convertToTensor(input2,"input","imag")};return ENGINE.runKernel(Imag,inputs)}var imag=op({imag_});function isFinite_(x){let inputs={x:convertToTensor(x,"x","isFinite")};return ENGINE.runKernel(IsFinite,inputs)}var isFinite2=op({isFinite_});function isInf_(x){let inputs={x:convertToTensor(x,"x","isInf")};return ENGINE.runKernel(IsInf,inputs)}var isInf=op({isInf_});function isNaN_(x){let inputs={x:convertToTensor(x,"x","isNaN")};return ENGINE.runKernel(IsNan,inputs)}var isNaN2=op({isNaN_});function leakyRelu_(x,alpha=.2){let inputs={x:convertToTensor(x,"x","leakyRelu")},attrs={alpha};return ENGINE.runKernel(LeakyRelu,inputs,attrs)}var leakyRelu=op({leakyRelu_});function less_(a,b){let $a=convertToTensor(a,"a","less"),$b=convertToTensor(b,"b","less");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Less,inputs)}var less=op({less_});function lessEqual_(a,b){let $a=convertToTensor(a,"a","lessEqual"),$b=convertToTensor(b,"b","lessEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LessEqual,inputs)}var lessEqual=op({lessEqual_});function linspace(start,stop,num){if(num<=0)throw new Error("The number of values should be positive.");let attrs={start,stop,num};return ENGINE.runKernel(LinSpace,{},attrs)}function localResponseNormalization_(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor(x,"x","localResponseNormalization");assert($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
+        ${$x.shape}`),assert(inputDepth%(blockSize*blockSize)==0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernel(DepthToSpace,inputs,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(DepthwiseConv2dNative,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let inputs={x:convertToTensor(x,"x","diag")};return ENGINE.runKernel(Diag,inputs)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad3,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i<inRank;i++){let dim=inRank-1-i,a=inShape[dim]||1;(outShape[outShape.length-1-i]||1)>1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_(a,b){let $a=convertToTensor(a,"a","equal"),$b=convertToTensor(b,"b","equal");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Equal,inputs)}var equal=op({equal_});function where_(condition,a,b){let $a=convertToTensor(a,"a","where"),$b=convertToTensor(b,"b","where"),$condition=convertToTensor(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape($a.shape,$b.shape),$broadcastedA=broadcastTo($a,broadcastShape),$broadcastedB=broadcastTo($b,broadcastShape);$condition.rank===1&&assert($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernel(Select,inputs)}var where=op({where_});function zerosLike_(x){let inputs={x:convertToTensor(x,"x","zerosLike")};return ENGINE.runKernel(ZerosLike,inputs)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros4=zerosLike(divResult),bEqualsZero=equal($b,zeros4);return where(bEqualsZero,zeros4,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]);return matMul($t1,t22D)}}var dot=op({dot_});function elu_(x){let inputs={x:convertToTensor(x,"x","elu")};return ENGINE.runKernel(Elu,inputs)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernel(Erf,inputs)}var erf=op({erf_});function exp_(x){let inputs={x:convertToTensor(x,"x","exp")};return ENGINE.runKernel(Exp,inputs)}var exp=op({exp_});function expandDims_(x,axis=0){let $x=convertToTensor(x,"x","expandDims","string_or_numeric");assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let inputs={input:$x},attrs={dim:axis};return ENGINE.runKernel(ExpandDims,inputs,attrs)}var expandDims=op({expandDims_});function expm1_(x){let inputs={x:convertToTensor(x,"x","expm1")};return ENGINE.runKernel(Expm1,inputs)}var expm1=op({expm1_});function tile_(x,reps){let $x=convertToTensor(x,"x","tile","string_or_numeric");assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let inputs={x:$x},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile(expandDims(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile(expandDims(expandDims(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile(expandDims(expandDims(expandDims(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye=op({eye_});function fill(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE.runKernel(Fill,{},attrs)}function floor_(x){let inputs={x:convertToTensor(x,"x","floor")};return ENGINE.runKernel(Floor,inputs)}var floor=op({floor_});function gather_(x,indices,axis=0,batchDims=0){let $x=convertToTensor(x,"x","gather"),$indices=convertToTensor(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis,batchDims};return ENGINE.runKernel(GatherV2,inputs,attrs)}var gather=op({gather_});function greater_(a,b){let $a=convertToTensor(a,"a","greater"),$b=convertToTensor(b,"b","greater");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Greater,inputs)}var greater=op({greater_});function greaterEqual_(a,b){let $a=convertToTensor(a,"a","greaterEqual"),$b=convertToTensor(b,"b","greaterEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(GreaterEqual,inputs)}var greaterEqual=op({greaterEqual_});function imag_(input2){let inputs={input:convertToTensor(input2,"input","imag")};return ENGINE.runKernel(Imag,inputs)}var imag=op({imag_});function isFinite_(x){let inputs={x:convertToTensor(x,"x","isFinite")};return ENGINE.runKernel(IsFinite,inputs)}var isFinite2=op({isFinite_});function isInf_(x){let inputs={x:convertToTensor(x,"x","isInf")};return ENGINE.runKernel(IsInf,inputs)}var isInf=op({isInf_});function isNaN_(x){let inputs={x:convertToTensor(x,"x","isNaN")};return ENGINE.runKernel(IsNan,inputs)}var isNaN2=op({isNaN_});function leakyRelu_(x,alpha=.2){let inputs={x:convertToTensor(x,"x","leakyRelu")},attrs={alpha};return ENGINE.runKernel(LeakyRelu,inputs,attrs)}var leakyRelu=op({leakyRelu_});function less_(a,b){let $a=convertToTensor(a,"a","less"),$b=convertToTensor(b,"b","less");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Less,inputs)}var less=op({less_});function lessEqual_(a,b){let $a=convertToTensor(a,"a","lessEqual"),$b=convertToTensor(b,"b","lessEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LessEqual,inputs)}var lessEqual=op({lessEqual_});function linspace(start,stop,num){if(num<=0)throw new Error("The number of values should be positive.");let attrs={start,stop,num};return ENGINE.runKernel(LinSpace,{},attrs)}function localResponseNormalization_(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor(x,"x","localResponseNormalization");assert($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
                rank ${$x.rank}.`),assert(isInt(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE.runKernel(LRN,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization=op({localResponseNormalization_});function log_(x){let inputs={x:convertToTensor(x,"x","log")};return ENGINE.runKernel(Log,inputs)}var log=op({log_});function log1p_(x){let inputs={x:convertToTensor(x,"x","log1p")};return ENGINE.runKernel(Log1p,inputs)}var log1p=op({log1p_});function grad(f){return assert(isFunction(f),()=>"The f passed in grad(f) must be a function"),(x,dy)=>{let $x=convertToTensor(x,"x","tf.grad","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grad"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f($x),[$x],$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(grads2),grads2[0]})}}function grads(f){return assert(isFunction(f),()=>"The f passed in grads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let $args=convertToTensorArray(args,"args","tf.grads","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grads"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f(...$args),$args,$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(grads2),grads2})}}function valueAndGrad(f){return assert(isFunction(f),()=>"The f passed in valueAndGrad(f) must be a function"),(x,dy)=>{assert(x instanceof Tensor,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:grads2,value}=ENGINE.gradients(()=>f(x),[x],dy);return checkGrads(grads2),{grad:grads2[0],value}}}function valueAndGrads(f){return assert(isFunction(f),()=>"The f passed in valueAndGrads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args)&&args.every(arg=>arg instanceof Tensor),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let res=ENGINE.gradients(()=>f(...args),args,dy);return dy!=null&&assertShapesMatch(res.value.shape,dy.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(res.grads),res}}function variableGrads(f,varList){assert(isFunction(f),()=>"The f passed in variableGrads(f) must be a function"),assert(varList==null||Array.isArray(varList)&&varList.every(v=>v instanceof Variable),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let specifiedVarList=varList!=null;if(!specifiedVarList){varList=[];for(let varName in ENGINE.registeredVariables)varList.push(ENGINE.registeredVariables[varName])}let specifiedNonTrainable=specifiedVarList?varList.filter(variable2=>!variable2.trainable):null,originalVarCount=varList.length;varList=varList.filter(variable2=>variable2.trainable),assert(varList.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);let allowNoGradients=!0,{value,grads:grads2}=ENGINE.gradients(f,varList,null,allowNoGradients);assert(grads2.some(g=>g!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(value.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);let namedGrads={};return varList.forEach((v,i)=>{grads2[i]!=null&&(namedGrads[v.name]=grads2[i])}),specifiedNonTrainable!=null&&specifiedNonTrainable.forEach(v=>namedGrads[v.name]=null),{value,grads:namedGrads}}function customGrad(f){return ENGINE.customGrad(f)}function checkGrads(grads2){if(grads2.filter(g=>g==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
-    the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function logSumExp_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","logSumExp"),axes=parseAxisParam(axis,$x.shape),xMax=max($x,axes,!0),a=sub($x,xMax),b=exp(a),c=sum2(b,axes),d=log(c),res=add2(reshape(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,axes);return reshape(res,newShape)}return res}var logSumExp=op({logSumExp_});function logicalAnd_(a,b){let $a=convertToTensor(a,"a","logicalAnd","bool"),$b=convertToTensor(b,"b","logicalAnd","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalAnd,inputs)}var logicalAnd=op({logicalAnd_});function logicalNot_(x){let inputs={x:convertToTensor(x,"x","logicalNot","bool")};return ENGINE.runKernel(LogicalNot,inputs)}var logicalNot=op({logicalNot_});function logicalOr_(a,b){let $a=convertToTensor(a,"a","logicalOr","bool"),$b=convertToTensor(b,"b","logicalOr","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalOr,inputs)}var logicalOr=op({logicalOr_});function logicalXor_(a,b){let $a=convertToTensor(a,"a","logicalXor","bool"),$b=convertToTensor(b,"b","logicalXor","bool");return assertAndGetBroadcastShape($a.shape,$b.shape),logicalAnd(logicalOr(a,b),logicalNot(logicalAnd(a,b)))}var logicalXor=op({logicalXor_});function maxPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data,c,h){let $data=convertToTensor(data,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i<lstmCells.length;i++){let output=lstmCells[i](input2,$c[i],$h[i]);newStates.push(output[0]),newStates.push(output[1]),input2=output[1]}let newC=[],newH=[];for(let i=0;i<newStates.length;i+=2)newC.push(newStates[i]),newH.push(newStates[i+1]);return[newC,newH]}var multiRNNCell=op({multiRNNCell_});function multinomial_(logits,numSamples,seed,normalized=!1){let $logits=convertToTensor(logits,"logits","multinomial"),numOutcomes=$logits.size,origRank=$logits.rank;if(numOutcomes<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);if(origRank>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]===0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i<size;i++)values[i]=randFunction();return ENGINE.makeTensor(values,shape,dtype)}var rand=op({rand_}),seedrandom=__toModule(require_seedrandom2());var MPRandGauss=class{constructor(mean4,stdDeviation,dtype,truncated,seed){this.mean=mean4,this.stdDev=stdDeviation,this.dtype=dtype,this.nextVal=NaN,this.truncated=truncated,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let seedValue=seed||Math.random();this.random=seedrandom.alea(seedValue.toString())}nextValue(){if(!isNaN(this.nextVal)){let value=this.nextVal;return this.nextVal=NaN,value}let resultX,resultY,isValid=!1;for(;!isValid;){let v1,v2,s;do v1=2*this.random()-1,v2=2*this.random()-1,s=v1*v1+v2*v2;while(s>=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),u<v0||Math.log(u)<v1)break}return v=1/this.beta*this.d*v,this.alpha<1&&(v*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(v)}convertValue(value){return this.dtype==="float32"?value:Math.round(value)}},UniformRandom=class{constructor(min6=0,max6=1,dtype,seed){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=rgamma.nextValue();return res.toTensor()}var randomGamma=op({randomGamma_});function randomNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error(`Unsupported data type ${dtype}`);let randGauss=new MPRandGauss(mean4,stdDev,dtype,!1,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var randomNormal=op({randomNormal_});function randomUniform_(shape,minval=0,maxval=1,dtype="float32",seed){let res=buffer(shape,dtype),random=new UniformRandom(minval,maxval,null,seed);for(let i=0;i<res.values.length;i++)res.values[i]=random.nextValue();return res.toTensor()}var randomUniform=op({randomUniform_});function range(start,stop,step4=1,dtype="float32"){if(step4===0)throw new Error("Cannot have a step of zero");let attrs={start,stop,step:step4,dtype};return ENGINE.runKernel(Range,{},attrs)}function real_(input2){let inputs={input:convertToTensor(input2,"input","real")};return ENGINE.runKernel(Real,inputs)}var real=op({real_});function reciprocal_(x){let inputs={x:convertToTensor(x,"x","reciprocal")};return ENGINE.runKernel(Reciprocal,inputs)}var reciprocal=op({reciprocal_});function relu_(x){let inputs={x:convertToTensor(x,"x","relu")};return ENGINE.runKernel(Relu,inputs)}var relu=op({relu_});function relu6_(x){let inputs={x:convertToTensor(x,"x","relu6")};return ENGINE.runKernel(Relu6,inputs)}var relu6=op({relu6_});function reverse_(x,axis){let inputs={x:convertToTensor(x,"x","reverse")},attrs={dims:axis};return ENGINE.runKernel(Reverse,inputs,attrs)}var reverse=op({reverse_});function reverse1d_(x){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i<xVals.length;i++)ySet.has(xVals[i])||outputSize++;let buffer2=new TensorBuffer([outputSize],$x.dtype),indices=new TensorBuffer([outputSize],"int32");for(let i=0,p2=0;i<xVals.length;i++)ySet.has(xVals[i])||(buffer2.values[p2]=xVals[i],indices.values[p2]=i,p2++);return[buffer2.toTensor(),indices.toTensor()]}var setdiff1dAsync=setdiff1dAsync_;function sign_(x){let inputs={x:convertToTensor(x,"x","sign")};return ENGINE.runKernel(Sign,inputs)}var sign=op({sign_});function sin_(x){let inputs={x:convertToTensor(x,"x","sin")};return ENGINE.runKernel(Sin,inputs)}var sin=op({sin_});function sinh_(x){let inputs={x:convertToTensor(x,"x","sinh")};return ENGINE.runKernel(Sinh,inputs)}var sinh=op({sinh_});function slice1d_(x,begin,size){let $x=convertToTensor(x,"x","slice1d");return assert($x.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var truncatedNormal=op({truncatedNormal_});function unique_(x,axis=0){let $x=convertToTensor(x,"x","unique","string_or_numeric");assert($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i<condVals.length;i++)condVals[i]&&indices.push(i);let inBuffer=buffer(condShape,"int32"),out=buffer([indices.length,condShape.length],"int32");for(let i=0;i<indices.length;i++){let loc=inBuffer.indexToLoc(indices[i]),offset=i*condShape.length;out.values.set(loc,offset)}return out.toTensor()}async function whereAsync_(condition){let $condition=convertToTensor(condition,"condition","whereAsync","bool"),vals=await $condition.data(),res=whereImpl($condition.shape,vals);return condition!==$condition&&$condition.dispose(),res}var whereAsync=whereAsync_;async function booleanMaskAsync_(tensor2,mask,axis){let $tensor=convertToTensor(tensor2,"tensor","boolMask"),$mask=convertToTensor(mask,"mask","boolMask","bool"),axisFrom=axis==null?0:axis,maskDim=$mask.rank,tensorShape=$tensor.shape;assert(maskDim>0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i<axisFrom+maskDim;i++)leadingSize*=tensorShape[i];let targetTensorShape=tensorShape.slice(0,axisFrom).concat([leadingSize],tensorShape.slice(axisFrom+maskDim)),reshapedTensor=reshape($tensor,targetTensorShape),reshapedMask=reshape($mask,[-1]),positivePositions=await whereAsync(reshapedMask),indices=squeeze(positivePositions,[1]),res=gather(reshapedTensor,indices,axisFrom);return tensor2!==$tensor&&$tensor.dispose(),mask!==$mask&&$mask.dispose(),indices.dispose(),reshapedTensor.dispose(),reshapedMask.dispose(),positivePositions.dispose(),res}var booleanMaskAsync=booleanMaskAsync_;function notEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","notEqualStrict"),$b=convertToTensor(b,"b","notEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual($a,$b)}function lessStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessStrict"),$b=convertToTensor(b,"b","lessStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessStrict: "),less($a,$b)}function equalStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","equalStrict"),$b=convertToTensor(b,"b","equalStrict");return assertShapesMatch($a.shape,$b.shape,"Error in equalStrict: "),equal($a,$b)}function lessEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessEqualStrict"),$b=convertToTensor(b,"b","lessEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual($a,$b)}function greaterStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterStrict"),$b=convertToTensor(b,"b","greaterStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterStrict: "),greater($a,$b)}function greaterEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterEqualStrict"),$b=convertToTensor(b,"b","greaterEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual($a,$b)}var equalStrict=op({equalStrict_}),greaterEqualStrict=op({greaterEqualStrict_}),greaterStrict=op({greaterStrict_}),lessEqualStrict=op({lessEqualStrict_}),lessStrict=op({lessStrict_}),notEqualStrict=op({notEqualStrict_});function addStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","addStrict"),$b=convertToTensor(b,"b","addStrict");return assertShapesMatch($a.shape,$b.shape,"Error in addStrict: "),add2($a,$b)}function subStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","subStrict"),$b=convertToTensor(b,"b","subStrict");return assertShapesMatch($a.shape,$b.shape,"Error in subStrict: "),sub($a,$b)}function powStrict_(base2,exp4){return deprecationWarn("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch(base2.shape,exp4.shape,"Error in powStrict: "),pow(base2,exp4)}function mulStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");return assertShapesMatch($a.shape,$b.shape,"Error in multiplyStrict: "),mul($a,$b)}function divStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");return assertShapesMatch($a.shape,$b.shape,"Error in divideStrict: "),div($a,$b)}function modStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","modStrict"),$b=convertToTensor(b,"b","modStrict");return assertShapesMatch($a.shape,$b.shape,"Error in modStrict: "),mod($a,$b)}function minimumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","minimumStrict"),$b=convertToTensor(b,"b","minimumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in minimumStrict: "),minimum($a,$b)}function maximumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","maximumStrict"),$b=convertToTensor(b,"b","maximumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in maximumStrict: "),maximum($a,$b)}function squaredDifferenceStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","squaredDifferenceStrict"),$b=convertToTensor(b,"b","squaredDifferenceStrict");return assertShapesMatch($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference($a,$b)}var addStrict=op({addStrict_}),divStrict=op({divStrict_}),maximumStrict=op({maximumStrict_}),minimumStrict=op({minimumStrict_}),modStrict=op({modStrict_}),mulStrict=op({mulStrict_}),powStrict=op({powStrict_}),squaredDifferenceStrict=op({squaredDifferenceStrict_}),subStrict=op({subStrict_});function norm_(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor(x,"x","norm");let norm2=normImpl(x,ord,axis),keepDimsShape=norm2.shape;if(keepDims){let axes=parseAxisParam(axis,x.shape);keepDimsShape=expandShapeToKeepDim(norm2.shape,axes)}return reshape(norm2,keepDimsShape)}function normImpl(x,p2,axis=null){if(x.rank===0)return abs(x);if(x.rank!==1&&axis===null)return normImpl(reshape(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum2(abs(x),axis);if(p2===Infinity)return max(abs(x),axis);if(p2===-Infinity)return min(abs(x),axis);if(p2==="euclidean"||p2===2)return sqrt(sum2(pow(abs(x),scalar(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max(sum2(abs(x),axis[0]),axis[1]-1);if(p2===Infinity)return max(sum2(abs(x),axis[1]),axis[0]);if(p2===-Infinity)return min(sum2(abs(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt(sum2(square(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm=op({norm_});function movingAverage_(v,x,decay,step4,zeroDebias=!0){let $v=convertToTensor(v,"v","movingAverage"),$x=convertToTensor(x,"x","movingAverage"),$decay=convertToTensor(decay,"decay","movingAverage");assertTypesMatch($v,$x),assert(arraysEqual($v.shape,$x.shape),()=>"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i<x.shape.length;i++)noiseShape[i]==null&&x.shape[i]!=null?newDimension.push(x.shape[i]):newDimension.push(noiseShape[i]);return newDimension}return noiseShape}function dropout_(x,rate,noiseShape,seed){let $x=convertToTensor(x,"x","dropout");if(assert($x.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d(newValues,"float32")}async function inTopKAsync_(predictions,targets,k=1){let $predictions=convertToTensor(predictions,"predictions","inTopK"),$targets=convertToTensor(targets,"targets","inTopK");assert($predictions.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;b<batch;b++){let offset=b*size,vals=predictionsVals.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value),precision3[b]=0;for(let i=0;i<k;i++)if(valAndInd[i].index===targetsVals[b]){precision3[b]=1;break}}return predictions!==$predictions&&$predictions.dispose(),targets!==$targets&&$targets.dispose(),tensor(precision3,$targets.shape,"bool")}var inTopKAsync=inTopKAsync_;var fused_ops_exports={};__export(fused_ops_exports,{conv2d:()=>conv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal2.size;){let padLen=start+frameLength-signal2.size,pad3=concat([slice(signal2,start,frameLength-padLen),fill([padLen],padValue)]);output.push(pad3),start+=frameStep}return output.length===0?tensor2d([],[0,frameLength]):reshape(concat(output),[output.length,frameLength])}var frame=op({frame_});function stft_(signal2,frameLength,frameStep,fftLength,windowFn=hannWindow){fftLength==null&&(fftLength=enclosingPowerOfTwo(frameLength));let framedSignal=frame(signal2,frameLength,frameStep),windowedSignal=mul(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft(slice(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat(output)}var stft=op({stft_});function cropAndResize_(image3,boxes,boxInd,cropSize,method="bilinear",extrapolationValue=0){let $image=convertToTensor(image3,"image","cropAndResize"),$boxes=convertToTensor(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor(boxInd,"boxInd","cropAndResize","int32"),numBoxes=$boxes.shape[0];assert($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a<b?-1:0}function binarySearch_(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale2=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale2,iou),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale2,iou){let weight=Math.exp(scale2*iou*iou);return iou<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1===0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1===0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul(sum2(mul(ys[j],x)),ys[j]);x=sub(x,proj)}return div(x,norm(x,"euclidean"))}));return inputIsTensor2D?stack(ys,0):ys}var gramSchmidt=op({gramSchmidt_});function qr_(x,fullMatrices=!1){if(assert(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE.tidy(()=>{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_});var Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon3=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon3),l13=neg(mul($labels,log(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_});var spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy};var Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon3=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon3,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.rho,config.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config){return new cls(config.learningRate,config.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon,config.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config){return new cls(config.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config){return new cls(config.learningRate,config.momentum,config.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon3,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient!=null){let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config){return new cls(config.learningRate,config.decay,config.momentum,config.epsilon,config.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon3,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon3=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon3)}static adadelta(learningRate=.001,rho=.95,epsilon3=null){return new AdadeltaOptimizer(learningRate,rho,epsilon3)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon3=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon3,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}};var train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam};var delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log2,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(inSize){return inSize<=PARALLELIZE_THRESHOLD?inSize:nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)))}function getImageCenter(center,imageHeight,imageWidth){let centerX=imageWidth*(typeof center=="number"?center:center[0]),centerY=imageHeight*(typeof center=="number"?center:center[1]);return[centerX,centerY]}function getReshaped(inputShape,blockShape,prod5,batchToSpace=!0){let reshaped=[];if(batchToSpace)reshaped=reshaped.concat(blockShape.slice(0)),reshaped.push(inputShape[0]/prod5),reshaped=reshaped.concat(inputShape.slice(1));else{reshaped=reshaped.concat(inputShape[0]);let spatialLength=blockShape.length;for(let i=0;i<spatialLength;++i)reshaped=reshaped.concat([inputShape[i+1]/blockShape[i],blockShape[i]]);reshaped=reshaped.concat(inputShape.slice(spatialLength+1))}return reshaped}function getPermuted(reshapedRank,blockShapeRank,batchToSpace=!0){let permuted=[];if(batchToSpace){permuted.push(blockShapeRank);for(let i=blockShapeRank+1;i<reshapedRank;++i)i<=2*blockShapeRank?(permuted.push(i),permuted.push(i-(blockShapeRank+1))):permuted.push(i)}else{let permutedBeforeBatch=[],permutedAfterBatch=[];for(let i=1;i<reshapedRank;++i)i>=blockShapeRank*2+1||i%2===1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i<inputShape.length;++i)i<=blockShape.length?batchToSpace?reshapedPermuted.push(blockShape[i-1]*inputShape[i]):reshapedPermuted.push(inputShape[i]/blockShape[i-1]):reshapedPermuted.push(inputShape[i]);return reshapedPermuted}function getSliceBeginCoords(crops,blockShape){let sliceBeginCoords=[0];for(let i=0;i<blockShape;++i)sliceBeginCoords.push(crops[i][0]);return sliceBeginCoords}function getSliceSize(uncroppedShape,crops,blockShape){let sliceSize=uncroppedShape.slice(0,1);for(let i=0;i<blockShape;++i)sliceSize.push(uncroppedShape[i+1]-crops[i][0]-crops[i][1]);return sliceSize}var SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805;var ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429;function warn(...msg){env().getBool("IS_TEST")||console.warn(...msg)}function log2(...msg){env().getBool("IS_TEST")||console.log(...msg)}function mergeRealAndImagArrays(real4,imag4){if(real4.length!==imag4.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`);let result=new Float32Array(real4.length*2);for(let i=0;i<result.length;i+=2)result[i]=real4[i/2],result[i+1]=imag4[i/2];return result}function splitRealAndImagArrays(complex4){let real4=new Float32Array(complex4.length/2),imag4=new Float32Array(complex4.length/2);for(let i=0;i<complex4.length;i+=2)real4[i/2]=complex4[i],imag4[i/2]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithEvenIndex(complex4){let len=Math.ceil(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=0;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithOddIndex(complex4){let len=Math.floor(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=2;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function getComplexWithIndex(complex4,index){let real4=complex4[index*2],imag4=complex4[index*2+1];return{real:real4,imag:imag4}}function assignToTypedArray(data,real4,imag4,index){data[index*2]=real4,data[index*2+1]=imag4}function exponents(n,inverse){let real4=new Float32Array(n/2),imag4=new Float32Array(n/2);for(let i=0;i<Math.ceil(n/2);i++){let x=(inverse?2:-2)*Math.PI*(i/n);real4[i]=Math.cos(x),imag4[i]=Math.sin(x)}return{real:real4,imag:imag4}}function exponent(k,n,inverse){let x=(inverse?2:-2)*Math.PI*(k/n),real4=Math.cos(x),imag4=Math.sin(x);return{real:real4,imag:imag4}}function prepareSplitSize(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert(x.shape[axis]%numOrSizeSplits===0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)dim!==axis?outShape.push(aShape[dim]):outShape.push(numSegments);return outShape}function collectGatherOpShapeInfo(x,indices,axis,batchDims){let indicesRank=indices.shape.length,xRank=x.shape.length;if(batchDims!==0&&(batchDims<-indicesRank||batchDims>indicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
+    the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function logSumExp_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","logSumExp"),axes=parseAxisParam(axis,$x.shape),xMax=max($x,axes,!0),a=sub($x,xMax),b=exp(a),c=sum2(b,axes),d=log(c),res=add2(reshape(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,axes);return reshape(res,newShape)}return res}var logSumExp=op({logSumExp_});function logicalAnd_(a,b){let $a=convertToTensor(a,"a","logicalAnd","bool"),$b=convertToTensor(b,"b","logicalAnd","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalAnd,inputs)}var logicalAnd=op({logicalAnd_});function logicalNot_(x){let inputs={x:convertToTensor(x,"x","logicalNot","bool")};return ENGINE.runKernel(LogicalNot,inputs)}var logicalNot=op({logicalNot_});function logicalOr_(a,b){let $a=convertToTensor(a,"a","logicalOr","bool"),$b=convertToTensor(b,"b","logicalOr","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(LogicalOr,inputs)}var logicalOr=op({logicalOr_});function logicalXor_(a,b){let $a=convertToTensor(a,"a","logicalXor","bool"),$b=convertToTensor(b,"b","logicalXor","bool");return assertAndGetBroadcastShape($a.shape,$b.shape),logicalAnd(logicalOr(a,b),logicalNot(logicalAnd(a,b)))}var logicalXor=op({logicalXor_});function maxPool_(x,filterSize,strides,pad3,dimRoundingMode){let $x=convertToTensor(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data,c,h){let $data=convertToTensor(data,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i<lstmCells.length;i++){let output=lstmCells[i](input2,$c[i],$h[i]);newStates.push(output[0]),newStates.push(output[1]),input2=output[1]}let newC=[],newH=[];for(let i=0;i<newStates.length;i+=2)newC.push(newStates[i]),newH.push(newStates[i+1]);return[newC,newH]}var multiRNNCell=op({multiRNNCell_});function multinomial_(logits,numSamples,seed,normalized=!1){let $logits=convertToTensor(logits,"logits","multinomial"),numOutcomes=$logits.size,origRank=$logits.rank;if(numOutcomes<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);if(origRank>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]==0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i<size;i++)values[i]=randFunction();return ENGINE.makeTensor(values,shape,dtype)}var rand=op({rand_});var seedrandom=__toModule(require_seedrandom2());var MPRandGauss=class{constructor(mean4,stdDeviation,dtype,truncated,seed){this.mean=mean4,this.stdDev=stdDeviation,this.dtype=dtype,this.nextVal=NaN,this.truncated=truncated,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let seedValue=seed||Math.random();this.random=seedrandom.alea(seedValue.toString())}nextValue(){if(!isNaN(this.nextVal)){let value=this.nextVal;return this.nextVal=NaN,value}let resultX,resultY,isValid=!1;for(;!isValid;){let v1,v2,s;do v1=2*this.random()-1,v2=2*this.random()-1,s=v1*v1+v2*v2;while(s>=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),u<v0||Math.log(u)<v1)break}return v=1/this.beta*this.d*v,this.alpha<1&&(v*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(v)}convertValue(value){return this.dtype==="float32"?value:Math.round(value)}},UniformRandom=class{constructor(min6=0,max6=1,dtype,seed){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=rgamma.nextValue();return res.toTensor()}var randomGamma=op({randomGamma_});function randomNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error(`Unsupported data type ${dtype}`);let randGauss=new MPRandGauss(mean4,stdDev,dtype,!1,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var randomNormal=op({randomNormal_});function randomUniform_(shape,minval=0,maxval=1,dtype="float32",seed){let res=buffer(shape,dtype),random=new UniformRandom(minval,maxval,null,seed);for(let i=0;i<res.values.length;i++)res.values[i]=random.nextValue();return res.toTensor()}var randomUniform=op({randomUniform_});function range(start,stop,step4=1,dtype="float32"){if(step4===0)throw new Error("Cannot have a step of zero");let attrs={start,stop,step:step4,dtype};return ENGINE.runKernel(Range,{},attrs)}function real_(input2){let inputs={input:convertToTensor(input2,"input","real")};return ENGINE.runKernel(Real,inputs)}var real=op({real_});function reciprocal_(x){let inputs={x:convertToTensor(x,"x","reciprocal")};return ENGINE.runKernel(Reciprocal,inputs)}var reciprocal=op({reciprocal_});function relu_(x){let inputs={x:convertToTensor(x,"x","relu")};return ENGINE.runKernel(Relu,inputs)}var relu=op({relu_});function relu6_(x){let inputs={x:convertToTensor(x,"x","relu6")};return ENGINE.runKernel(Relu6,inputs)}var relu6=op({relu6_});function reverse_(x,axis){let inputs={x:convertToTensor(x,"x","reverse")},attrs={dims:axis};return ENGINE.runKernel(Reverse,inputs,attrs)}var reverse=op({reverse_});function reverse1d_(x){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i<xVals.length;i++)ySet.has(xVals[i])||outputSize++;let buffer2=new TensorBuffer([outputSize],$x.dtype),indices=new TensorBuffer([outputSize],"int32");for(let i=0,p2=0;i<xVals.length;i++)ySet.has(xVals[i])||(buffer2.values[p2]=xVals[i],indices.values[p2]=i,p2++);return[buffer2.toTensor(),indices.toTensor()]}var setdiff1dAsync=setdiff1dAsync_;function sign_(x){let inputs={x:convertToTensor(x,"x","sign")};return ENGINE.runKernel(Sign,inputs)}var sign=op({sign_});function sin_(x){let inputs={x:convertToTensor(x,"x","sin")};return ENGINE.runKernel(Sin,inputs)}var sin=op({sin_});function sinh_(x){let inputs={x:convertToTensor(x,"x","sinh")};return ENGINE.runKernel(Sinh,inputs)}var sinh=op({sinh_});function slice1d_(x,begin,size){let $x=convertToTensor(x,"x","slice1d");return assert($x.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var truncatedNormal=op({truncatedNormal_});function unique_(x,axis=0){let $x=convertToTensor(x,"x","unique","string_or_numeric");assert($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i<condVals.length;i++)condVals[i]&&indices.push(i);let inBuffer=buffer(condShape,"int32"),out=buffer([indices.length,condShape.length],"int32");for(let i=0;i<indices.length;i++){let loc=inBuffer.indexToLoc(indices[i]),offset=i*condShape.length;out.values.set(loc,offset)}return out.toTensor()}async function whereAsync_(condition){let $condition=convertToTensor(condition,"condition","whereAsync","bool"),vals=await $condition.data(),res=whereImpl($condition.shape,vals);return condition!==$condition&&$condition.dispose(),res}var whereAsync=whereAsync_;async function booleanMaskAsync_(tensor2,mask,axis){let $tensor=convertToTensor(tensor2,"tensor","boolMask"),$mask=convertToTensor(mask,"mask","boolMask","bool"),axisFrom=axis==null?0:axis,maskDim=$mask.rank,tensorShape=$tensor.shape;assert(maskDim>0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i<axisFrom+maskDim;i++)leadingSize*=tensorShape[i];let targetTensorShape=tensorShape.slice(0,axisFrom).concat([leadingSize],tensorShape.slice(axisFrom+maskDim)),reshapedTensor=reshape($tensor,targetTensorShape),reshapedMask=reshape($mask,[-1]),positivePositions=await whereAsync(reshapedMask),indices=squeeze(positivePositions,[1]),res=gather(reshapedTensor,indices,axisFrom);return tensor2!==$tensor&&$tensor.dispose(),mask!==$mask&&$mask.dispose(),indices.dispose(),reshapedTensor.dispose(),reshapedMask.dispose(),positivePositions.dispose(),res}var booleanMaskAsync=booleanMaskAsync_;function notEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","notEqualStrict"),$b=convertToTensor(b,"b","notEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual($a,$b)}function lessStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessStrict"),$b=convertToTensor(b,"b","lessStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessStrict: "),less($a,$b)}function equalStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","equalStrict"),$b=convertToTensor(b,"b","equalStrict");return assertShapesMatch($a.shape,$b.shape,"Error in equalStrict: "),equal($a,$b)}function lessEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessEqualStrict"),$b=convertToTensor(b,"b","lessEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual($a,$b)}function greaterStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterStrict"),$b=convertToTensor(b,"b","greaterStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterStrict: "),greater($a,$b)}function greaterEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterEqualStrict"),$b=convertToTensor(b,"b","greaterEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual($a,$b)}var equalStrict=op({equalStrict_}),greaterEqualStrict=op({greaterEqualStrict_}),greaterStrict=op({greaterStrict_}),lessEqualStrict=op({lessEqualStrict_}),lessStrict=op({lessStrict_}),notEqualStrict=op({notEqualStrict_});function addStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","addStrict"),$b=convertToTensor(b,"b","addStrict");return assertShapesMatch($a.shape,$b.shape,"Error in addStrict: "),add2($a,$b)}function subStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","subStrict"),$b=convertToTensor(b,"b","subStrict");return assertShapesMatch($a.shape,$b.shape,"Error in subStrict: "),sub($a,$b)}function powStrict_(base2,exp4){return deprecationWarn("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch(base2.shape,exp4.shape,"Error in powStrict: "),pow(base2,exp4)}function mulStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");return assertShapesMatch($a.shape,$b.shape,"Error in multiplyStrict: "),mul($a,$b)}function divStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");return assertShapesMatch($a.shape,$b.shape,"Error in divideStrict: "),div($a,$b)}function modStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","modStrict"),$b=convertToTensor(b,"b","modStrict");return assertShapesMatch($a.shape,$b.shape,"Error in modStrict: "),mod($a,$b)}function minimumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","minimumStrict"),$b=convertToTensor(b,"b","minimumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in minimumStrict: "),minimum($a,$b)}function maximumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","maximumStrict"),$b=convertToTensor(b,"b","maximumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in maximumStrict: "),maximum($a,$b)}function squaredDifferenceStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","squaredDifferenceStrict"),$b=convertToTensor(b,"b","squaredDifferenceStrict");return assertShapesMatch($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference($a,$b)}var addStrict=op({addStrict_}),divStrict=op({divStrict_}),maximumStrict=op({maximumStrict_}),minimumStrict=op({minimumStrict_}),modStrict=op({modStrict_}),mulStrict=op({mulStrict_}),powStrict=op({powStrict_}),squaredDifferenceStrict=op({squaredDifferenceStrict_}),subStrict=op({subStrict_});function norm_(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor(x,"x","norm");let norm2=normImpl(x,ord,axis),keepDimsShape=norm2.shape;if(keepDims){let axes=parseAxisParam(axis,x.shape);keepDimsShape=expandShapeToKeepDim(norm2.shape,axes)}return reshape(norm2,keepDimsShape)}function normImpl(x,p2,axis=null){if(x.rank===0)return abs(x);if(x.rank!==1&&axis===null)return normImpl(reshape(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum2(abs(x),axis);if(p2===Infinity)return max(abs(x),axis);if(p2===-Infinity)return min(abs(x),axis);if(p2==="euclidean"||p2===2)return sqrt(sum2(pow(abs(x),scalar(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max(sum2(abs(x),axis[0]),axis[1]-1);if(p2===Infinity)return max(sum2(abs(x),axis[1]),axis[0]);if(p2===-Infinity)return min(sum2(abs(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt(sum2(square(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm=op({norm_});function movingAverage_(v,x,decay,step4,zeroDebias=!0){let $v=convertToTensor(v,"v","movingAverage"),$x=convertToTensor(x,"x","movingAverage"),$decay=convertToTensor(decay,"decay","movingAverage");assertTypesMatch($v,$x),assert(arraysEqual($v.shape,$x.shape),()=>"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i<x.shape.length;i++)noiseShape[i]==null&&x.shape[i]!=null?newDimension.push(x.shape[i]):newDimension.push(noiseShape[i]);return newDimension}return noiseShape}function dropout_(x,rate,noiseShape,seed){let $x=convertToTensor(x,"x","dropout");if(assert($x.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d(newValues,"float32")}async function inTopKAsync_(predictions,targets,k=1){let $predictions=convertToTensor(predictions,"predictions","inTopK"),$targets=convertToTensor(targets,"targets","inTopK");assert($predictions.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;b<batch;b++){let offset=b*size,vals=predictionsVals.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value),precision3[b]=0;for(let i=0;i<k;i++)if(valAndInd[i].index===targetsVals[b]){precision3[b]=1;break}}return predictions!==$predictions&&$predictions.dispose(),targets!==$targets&&$targets.dispose(),tensor(precision3,$targets.shape,"bool")}var inTopKAsync=inTopKAsync_;var fused_ops_exports={};__export(fused_ops_exports,{conv2d:()=>conv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal2.size;){let padLen=start+frameLength-signal2.size,pad3=concat([slice(signal2,start,frameLength-padLen),fill([padLen],padValue)]);output.push(pad3),start+=frameStep}return output.length===0?tensor2d([],[0,frameLength]):reshape(concat(output),[output.length,frameLength])}var frame=op({frame_});function stft_(signal2,frameLength,frameStep,fftLength,windowFn=hannWindow){fftLength==null&&(fftLength=enclosingPowerOfTwo(frameLength));let framedSignal=frame(signal2,frameLength,frameStep),windowedSignal=mul(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft(slice(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat(output)}var stft=op({stft_});function cropAndResize_(image3,boxes,boxInd,cropSize,method="bilinear",extrapolationValue=0){let $image=convertToTensor(image3,"image","cropAndResize"),$boxes=convertToTensor(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor(boxInd,"boxInd","cropAndResize","int32"),numBoxes=$boxes.shape[0];assert($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a<b?-1:0}function binarySearch_(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale2=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale2,iou),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale2,iou){let weight=Math.exp(scale2*iou*iou);return iou<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1==0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1==0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul(sum2(mul(ys[j],x)),ys[j]);x=sub(x,proj)}return div(x,norm(x,"euclidean"))}));return inputIsTensor2D?stack(ys,0):ys}var gramSchmidt=op({gramSchmidt_});function qr_(x,fullMatrices=!1){if(assert(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE.tidy(()=>{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_});var Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon3=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon3),l13=neg(mul($labels,log(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_});var spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy};var Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon3=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon3,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.rho,config.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config){return new cls(config.learningRate,config.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon,config.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config){return new cls(config.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config){return new cls(config.learningRate,config.momentum,config.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon3,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config){return new cls(config.learningRate,config.decay,config.momentum,config.epsilon,config.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon3,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon3=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon3)}static adadelta(learningRate=.001,rho=.95,epsilon3=null){return new AdadeltaOptimizer(learningRate,rho,epsilon3)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon3=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon3,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}};var train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam};var delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log2,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(inSize){return inSize<=PARALLELIZE_THRESHOLD?inSize:nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)))}function getImageCenter(center,imageHeight,imageWidth){let centerX=imageWidth*(typeof center=="number"?center:center[0]),centerY=imageHeight*(typeof center=="number"?center:center[1]);return[centerX,centerY]}function getReshaped(inputShape,blockShape,prod5,batchToSpace=!0){let reshaped=[];if(batchToSpace)reshaped=reshaped.concat(blockShape.slice(0)),reshaped.push(inputShape[0]/prod5),reshaped=reshaped.concat(inputShape.slice(1));else{reshaped=reshaped.concat(inputShape[0]);let spatialLength=blockShape.length;for(let i=0;i<spatialLength;++i)reshaped=reshaped.concat([inputShape[i+1]/blockShape[i],blockShape[i]]);reshaped=reshaped.concat(inputShape.slice(spatialLength+1))}return reshaped}function getPermuted(reshapedRank,blockShapeRank,batchToSpace=!0){let permuted=[];if(batchToSpace){permuted.push(blockShapeRank);for(let i=blockShapeRank+1;i<reshapedRank;++i)i<=2*blockShapeRank?(permuted.push(i),permuted.push(i-(blockShapeRank+1))):permuted.push(i)}else{let permutedBeforeBatch=[],permutedAfterBatch=[];for(let i=1;i<reshapedRank;++i)i>=blockShapeRank*2+1||i%2==1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i<inputShape.length;++i)i<=blockShape.length?batchToSpace?reshapedPermuted.push(blockShape[i-1]*inputShape[i]):reshapedPermuted.push(inputShape[i]/blockShape[i-1]):reshapedPermuted.push(inputShape[i]);return reshapedPermuted}function getSliceBeginCoords(crops,blockShape){let sliceBeginCoords=[0];for(let i=0;i<blockShape;++i)sliceBeginCoords.push(crops[i][0]);return sliceBeginCoords}function getSliceSize(uncroppedShape,crops,blockShape){let sliceSize=uncroppedShape.slice(0,1);for(let i=0;i<blockShape;++i)sliceSize.push(uncroppedShape[i+1]-crops[i][0]-crops[i][1]);return sliceSize}var SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805;var ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429;function warn(...msg){env().getBool("IS_TEST")||console.warn(...msg)}function log2(...msg){env().getBool("IS_TEST")||console.log(...msg)}function mergeRealAndImagArrays(real4,imag4){if(real4.length!==imag4.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`);let result=new Float32Array(real4.length*2);for(let i=0;i<result.length;i+=2)result[i]=real4[i/2],result[i+1]=imag4[i/2];return result}function splitRealAndImagArrays(complex4){let real4=new Float32Array(complex4.length/2),imag4=new Float32Array(complex4.length/2);for(let i=0;i<complex4.length;i+=2)real4[i/2]=complex4[i],imag4[i/2]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithEvenIndex(complex4){let len=Math.ceil(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=0;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function complexWithOddIndex(complex4){let len=Math.floor(complex4.length/4),real4=new Float32Array(len),imag4=new Float32Array(len);for(let i=2;i<complex4.length;i+=4)real4[Math.floor(i/4)]=complex4[i],imag4[Math.floor(i/4)]=complex4[i+1];return{real:real4,imag:imag4}}function getComplexWithIndex(complex4,index){let real4=complex4[index*2],imag4=complex4[index*2+1];return{real:real4,imag:imag4}}function assignToTypedArray(data,real4,imag4,index){data[index*2]=real4,data[index*2+1]=imag4}function exponents(n,inverse){let real4=new Float32Array(n/2),imag4=new Float32Array(n/2);for(let i=0;i<Math.ceil(n/2);i++){let x=(inverse?2:-2)*Math.PI*(i/n);real4[i]=Math.cos(x),imag4[i]=Math.sin(x)}return{real:real4,imag:imag4}}function exponent(k,n,inverse){let x=(inverse?2:-2)*Math.PI*(k/n),real4=Math.cos(x),imag4=Math.sin(x);return{real:real4,imag:imag4}}function prepareSplitSize(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert(x.shape[axis]%numOrSizeSplits==0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)dim!==axis?outShape.push(aShape[dim]):outShape.push(numSegments);return outShape}function collectGatherOpShapeInfo(x,indices,axis,batchDims){let indicesRank=indices.shape.length,xRank=x.shape.length;if(batchDims!==0&&(batchDims<-indicesRank||batchDims>indicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
     ${xRank}).`);if(axis<batchDims)throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);for(let i=0;i<batchDims;++i)if(x.shape[i]!==indices.shape[i])throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`);let dimSize=x.shape[axis],outputShape=[],batchSize=1,outerSize=1,sliceSize=1;for(let i=0;i<batchDims;++i)outputShape.push(x.shape[i]),batchSize*=x.shape[i];for(let i=batchDims;i<axis;i++)outputShape.push(x.shape[i]),outerSize*=x.shape[i];for(let i=batchDims;i<indicesRank;i++)outputShape.push(indices.shape[i]);for(let i=axis+1;i<xRank;i++)outputShape.push(x.shape[i]),sliceSize*=x.shape[i];return{batchSize,sliceSize,outerSize,dimSize,outputShape}}function castTensor(x,dtype,backend2){if(dtype==="complex64"){if(x.dtype==="complex64")return x.clone();let zerosTensor=zeros(x.shape),floatX=cast(x,"float32"),result=backend2.complex(floatX,zerosTensor);return zerosTensor.dispose(),floatX.dispose(),result}if(!hasEncodingLoss(x.dtype,dtype))return ENGINE.makeTensorFromDataId(x.dataId,x.shape,dtype);if(x.dtype==="complex64"){let real4=backend2.real(x),result=cast(real4,dtype);return real4.dispose(),result}if(dtype==="int32")return backend2.int(x);if(dtype==="bool"){let zero=scalar(0,x.dtype),result=backend2.notEqual(x,zero);return zero.dispose(),result}else throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}function reshapeTensor(x,shape){return ENGINE.makeTensorFromDataId(x.dataId,shape,x.dtype)}function fromUint8ToStringArray(vals){try{return vals.map(val=>decodeString(val))}catch(err){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`)}}function fromStringArrayToUint8(strings){return strings.map(s=>encodeString(s))}var kernel_impls_exports={};__export(kernel_impls_exports,{nonMaxSuppressionV3Impl:()=>nonMaxSuppressionV3Impl,nonMaxSuppressionV4Impl:()=>nonMaxSuppressionV4Impl,nonMaxSuppressionV5Impl:()=>nonMaxSuppressionV5Impl,whereImpl:()=>whereImpl});var absGradConfig={kernelName:Abs,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,step(cast(x,"float32"),-1))}}};var acosGradConfig={kernelName:Acos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square(cast(x,"float32")),b=sqrt(sub(scalar(1),a));return neg(div(dy,b))}}}};var acoshGradConfig={kernelName:Acosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(sub(square(cast(x,"float32")),1));return div(dy,a)}}}};var addGradConfig={kernelName:Add,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}};var addNGradConfig={kernelName:AddN,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}};var argMaxGradConfig={kernelName:ArgMax,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}};var argMinGradConfig={kernelName:ArgMin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}};var asinGradConfig={kernelName:Asin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sqrt(sub(scalar(1),square(cast(x,"float32")))))}}};var asinhGradConfig={kernelName:Asinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(add2(scalar(1),square(cast(x,"float32"))));return div(dy,a)}}}};var atan2GradConfig={kernelName:Atan2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let d=add2(square(a),square(b)),res=mul(dy,div(b,d)),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let d=add2(square(a),square(b)),res=neg(mul(dy,div(a,d))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}};var atanGradConfig={kernelName:Atan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(square(cast(x,"float32")),1))}}};var atanhGradConfig={kernelName:Atanh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sub(scalar(1),square(cast(x,"float32"))))}}};function avgPool3dGrad_(dy,input2,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","avgPool3dGrad"),$input=convertToTensor(input2,"input","avgPool3dGrad"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert(dy5D.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dGrad=op({avgPool3dGrad_});var avgPool3DGradConfig={kernelName:AvgPool3D,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dGrad(dy,x,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function avgPoolGrad_(dy,input2,filterSize,strides,pad3){let $dy=convertToTensor(dy,"dy","avgPoolGrad"),$input=convertToTensor(input2,"input","avgPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert(dy4D.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`),assert(input4D.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);let inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad3},res=ENGINE.runKernel(AvgPoolGrad,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolGrad=op({avgPoolGrad_});var avgPoolGradConfig={kernelName:AvgPool,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>avgPoolGrad(dy,x,filterSize,strides,pad3)}}};var batchMatMulGradConfig={kernelName:BatchMatMul,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul(dy,b,!1,!0),b:()=>matMul(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul(dy,b,!1,!1),b:()=>matMul(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul(b,dy,!1,!0),b:()=>matMul(a,dy,!1,!1)}:{a:()=>matMul(b,dy,!0,!0),b:()=>matMul(dy,a,!0,!0)}}};var batchToSpaceNDGradConfig={kernelName:BatchToSpaceND,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND(dy,blockShape,crops)}}};var broadcastToGradConfig={kernelName:BroadcastTo,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i<reps.length;i++)reps[i]>1&&axes.push(i);return{x:()=>sum2(dy,axes,!0)}}};var castGradConfig={kernelName:Cast,gradFunc:dy=>({x:()=>dy.clone()})};var ceilGradConfig={kernelName:Ceil,gradFunc:dy=>({x:()=>zerosLike(dy)})};var clipByValueGradConfig={kernelName:ClipByValue,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where(logicalAnd(greaterEqual(x,clipValueMin),lessEqual(x,clipValueMax)),dy,zerosLike(dy))}}};var complexAbsGradConfig={kernelName:ComplexAbs,inputsToSave:["x"],gradFunc:absGradConfig.gradFunc};var concatGradConfig={kernelName:Concat,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]);return split(dy,sizeSplits,$axis).map(t=>()=>t)}};var conv2DGradConfig={kernelName:Conv2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad3,dataFormat}=attrs;return assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput(x4D.shape,dy,$filter,strides,pad3,dataFormat),filter:()=>conv2DBackpropFilter(x4D,dy,$filter.shape,strides,pad3,dataFormat)}}};var conv2DBackpropInputGradConfig={kernelName:Conv2DBackpropInput,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d(ddx,filter,strides,pad3,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter(ddx,dy,filter.shape,strides,pad3,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_(x,dy,filterShape,strides,pad3){let x5D=x;x.rank===4&&(x5D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad3,filterShape};return ENGINE.runKernel(Conv3DBackpropFilterV2,inputs,attrs)}var conv3DBackpropFilter=op({conv3DBackpropFilter_});var conv3DGradConfig={kernelName:Conv3D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3}=attrs;assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput(x5D.shape,dy,$filter,strides,pad3),filter:()=>conv3DBackpropFilter(x5D,dy,$filter.shape,strides,pad3)}}};var cosGradConfig={kernelName:Cos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(neg(sin(cast(x,"float32"))),dy)}}};var coshGradConfig={kernelName:Cosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(sinh(cast(x,"float32")),dy)}}};var cumsumGradConfig={kernelName:Cumsum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse5}=attrs;return{x:()=>{let permutation=getAxesPermutation([axis],x.rank),out=cumsum(dy,axis,exclusive,!reverse5);return permutation!=null&&(out=transpose(out,permutation)),out}}}};var depthwiseConv2dNativeGradConfig={kernelName:DepthwiseConv2dNative,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert(tupleValuesAreOne($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),{x:()=>depthwiseConv2dNativeBackpropInput(x.shape,dy,filter,strides,pad3,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter(x,dy,filter.shape,strides,pad3,dilations,dimRoundingMode)}}};var dilation2dGradConfig={kernelName:Dilation2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE.runKernel(Dilation2DBackpropInput,inputInputs,attrs),filter:()=>ENGINE.runKernel(Dilation2DBackpropFilter,filterInputs,attrs)}}};var eluGradConfig={kernelName:Elu,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,inputs={dy,y};return{x:()=>ENGINE.runKernel(EluGrad,inputs)}}};var erfGradConfig={kernelName:Erf,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul(exp(neg(square(x))),2/Math.sqrt(Math.PI));return{x:()=>mul(dy,a)}}};var expGradConfig={kernelName:Exp,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,y)}}};var expandDimsGradConfig={kernelName:ExpandDims,inputsToSave:["input"],gradFunc:(dy,saved)=>{let[input2]=saved;return{input:()=>reshape(dy,input2.shape)}}};var expm1GradConfig={kernelName:Expm1,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,exp(x))}}};var floorGradConfig={kernelName:Floor,gradFunc:dy=>({x:()=>zerosLike(dy)})};var floorDivGradConfig={kernelName:FloorDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))}}}};var fusedBatchNormGradConfig={kernelName:FusedBatchNorm,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean4,variance,scale2]=saved,scaleValue=scale2==null?scalar(1):scale2,reductionAxes=getReductionAxes(mean4.shape,x.shape),tileShape=[];if(mean4.rank===1){for(let i=0;i<x.shape.length-1;++i)tileShape.push(x.shape[i]);tileShape.push(1)}let xMinusMean=sub(x,mean4),dyTimesScaleValue=mul(dy,scaleValue),oneOverSqrtVariance=rsqrt(add2(variance,scalar(varianceEpsilon))),minusHalfRCube=mul(mul(mul(oneOverSqrtVariance,oneOverSqrtVariance),oneOverSqrtVariance),scalar(-.5));return{x:()=>mean4.rank===1?reshape(mul(mul(dy,tile(reshape(oneOverSqrtVariance,[1,1,1,mean4.shape[0]]),tileShape)),scaleValue),x.shape):reshape(mul(mul(dy,oneOverSqrtVariance),scaleValue),x.shape),mean:()=>{let meanDer=mul(mul(oneOverSqrtVariance,scalar(-1)),dyTimesScaleValue);return mean4.rank===1&&(meanDer=sum2(meanDer,reductionAxes)),reshape(meanDer,mean4.shape)},variance:()=>{let varianceDer=mul(mul(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean4.rank===1&&(varianceDer=sum2(varianceDer,reductionAxes)),reshape(varianceDer,mean4.shape)},scale:()=>{let xMinusMean2TimesRsqrt=mul(xMinusMean,oneOverSqrtVariance),scaleDer=mul(dy,xMinusMean2TimesRsqrt);return mean4.rank===1&&(scaleDer=sum2(scaleDer,reductionAxes)),reshape(scaleDer,mean4.shape)},offset:()=>{let offsetDer=dy;return mean4.rank===1&&(offsetDer=sum2(offsetDer,reductionAxes)),reshape(offsetDer,mean4.shape)}}}};var gatherGradConfig={kernelName:GatherV2,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam(axis,x.shape)[0];return{x:()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange(0,outerDims),innerAxesIndices=arrayRange(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat([outerShape,[indicesSize],innerShape]),values=reshape(dy,valuesShape),reshapedIndices=reshape(indices,[indicesSize]),transposeDims=arrayConcat([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose(values,transposeDims),paramsGrad=unsortedSegmentSum(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation(transposeDims);return paramsGrad=transpose(paramsGrad,invertTransposeDims),paramsGrad},indices:()=>indices}}};function arrayRange(start,stop){let result=[];for(let i=start;i<stop;++i)result.push(i);return result}function arrayConcat(arrays){let result=[];for(let i=0;i<arrays.length;++i)for(let j=0;j<arrays[i].length;++j)result.push(arrays[i][j]);return result}var greaterEqualGradConfig={kernelName:GreaterEqual,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>zerosLike(a),b:()=>zerosLike(b)}}};var identityGradConfig={kernelName:Identity,gradFunc:dy=>({x:()=>cast(dy,"float32")})};var isFiniteGradConfig={kernelName:IsFinite,gradFunc:dy=>({x:()=>zerosLike(dy)})};var isInfGradConfig={kernelName:IsInf,gradFunc:dy=>({x:()=>zerosLike(dy)})};var isNanGradConfig={kernelName:IsNan,gradFunc:dy=>({x:()=>zerosLike(dy)})};var leakyReluGradConfig={kernelName:LeakyRelu,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{alpha}=attrs,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha))}}};var log1pGradConfig={kernelName:Log1p,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(x,1))}}};var logGradConfig={kernelName:Log,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,cast(x,"float32"))}}};var logSoftmaxGradConfig={kernelName:LogSoftmax,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax6=exp(value);return sub(dy,mul(sum2(dy,axis,keepDims),softmax6))}}}};function localResponseNormalizationBackprop_(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE.runKernel(LRNGrad,inputs,attrs)}var localResponseNormalizationBackprop=op({localResponseNormalizationBackprop_});var lrnGradConfig={kernelName:LRN,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax(dy,y,xOrig,origAxes){return y.rank<xOrig.rank&&(y=reshape(y,expandShapeToKeepDim(y.shape,origAxes))),dy.rank<xOrig.rank&&(dy=reshape(dy,expandShapeToKeepDim(dy.shape,origAxes))),{x:()=>mul(dy,cast(equal(xOrig,y),dy.dtype))}}var maxGradConfig={kernelName:Max,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam(reductionIndices,x.shape),maxGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}};var maximumGradConfig={kernelName:Maximum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(greaterEqual(a,b),"float32")),b:()=>mul(dy,cast(less(a,b),"float32"))}}};function maxPool3dGrad_(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPool3dGrad"),$input=convertToTensor(input2,"input","maxPool3dGrad"),$output=convertToTensor(output,"output","maxPool3dGrad"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert(dy5D.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(output5D.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dGrad=op({maxPool3dGrad_});var maxPool3DGradConfig={kernelName:MaxPool3D,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dGrad(dy,x,y,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function maxPoolGrad_(dy,input2,output,filterSize,strides,pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPoolGrad"),$input=convertToTensor(input2,"input","maxPoolGrad"),$output=convertToTensor(output,"output","maxPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert($dy.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`),assert($input.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad3,dimRoundingMode};return ENGINE.runKernel(MaxPoolGrad,inputs,attrs)}var maxPoolGrad=op({maxPoolGrad_});var maxPoolGradConfig={kernelName:MaxPool,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>maxPoolGrad(dy,x,y,filterSize,strides,pad3)}}};var meanGradConfig={kernelName:Mean,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis}=attrs,axes=parseAxisParam(axis,x.shape),reduceShape=computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=sizeFromShape(reduceShape);return{x:()=>{let expandedDyShape=x.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape);return div(mul(expandedDy,ones2(x.shape,"float32")),reduceSize)}}}};var minGradConfig={kernelName:Min,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam(axis,x.shape),minGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>minGrad.x()}}};var minimumGradConfig={kernelName:Minimum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(lessEqual(a,b),"float32")),b:()=>mul(dy,cast(greater(a,b),"float32"))}}};var mirrorPadGradConfig={kernelName:MirrorPad,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}};var modGradConfig={kernelName:Mod,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(dy,reduceAxes),a.shape):dy},b:()=>{let res=mul(dy,neg(floor(div(a,b)))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}};var multiplyGradConfig={kernelName:Multiply,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=mul(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}};var negGradConfig={kernelName:Neg,gradFunc:dy=>({x:()=>neg(dy)})};var oneHotGradConfig={kernelName:OneHot,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros(indices.shape,"float32")}}};var onesLikeGradConfig={kernelName:OnesLike,gradFunc:dy=>({x:()=>zerosLike(dy)})};var packGradConfig={kernelName:Pack,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return unstack(dy,axis).map(t=>()=>t)}};var padV2GradConfig={kernelName:PadV2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}};var powGradConfig={kernelName:Pow,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(dy,saved)=>{let[a,b,y]=saved,base2=a,exp4=b,outShape=assertAndGetBroadcastShape(base2.shape,exp4.shape);return{a:()=>{let expFloat=cast(exp4,"float32"),res=mul(dy,mul(expFloat,pow(base2,sub(expFloat,scalar(1))))),reduceAxes=getReductionAxes(base2.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,base2.shape)},b:()=>{let condition=greater(base2,0),logBase=where(condition,log(base2),zerosLike(base2)),res=mul(dy,mul(y,logBase)),reduceAxes=getReductionAxes(exp4.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,exp4.shape)}}}};var preluGradConfig={kernelName:Prelu,inputsToSave:["x","alpha"],gradFunc:(dy,saved)=>{let[x,alpha]=saved,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha)),alpha:()=>{let res=where(mask,zerosLike(dy),mul(dy,x)),reduceAxes=getReductionAxes(alpha.shape,dy.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,alpha.shape)}}}};var divGradConfig={kernelName:RealDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))}}}};var reciprocalGradConfig={kernelName:Reciprocal,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,neg(square(x)))}}};var relu6GradConfig={kernelName:Relu6,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,mask=mul(lessEqual(x,6),step(x));return{x:()=>mul(dy,cast(mask,"float32"))}}};var reluGradConfig={kernelName:Relu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,cast(step(x),"float32"))}}};var reshapeGradConfig={kernelName:Reshape,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>reshape(dy,x.shape)}}};var resizeBilinearGradConfig={kernelName:ResizeBilinear,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,inputs={dy,images};return{images:()=>ENGINE.runKernel(ResizeBilinearGrad,inputs,attrs)}}};var resizeNearestNeighborGradConfig={kernelName:ResizeNearestNeighbor,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,inputs={dy,images};return{images:()=>ENGINE.runKernel(ResizeNearestNeighborGrad,inputs,attrs)}}};var reverseGradConfig={kernelName:Reverse,gradFunc:(dy,saved,attrs)=>{let{dims}=attrs,axes=parseAxisParam(dims,dy.shape);return{x:()=>reverse(dy,axes)}}};var roundGradConfig={kernelName:Round,gradFunc:dy=>({x:()=>zerosLike(dy)})};var rsqrtGradConfig={kernelName:Rsqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>neg(div(dy,mul(pow(x,1.5),2)))}}};var selectGradConfig={kernelName:Select,inputsToSave:["condition"],gradFunc:(dy,saved)=>{let[condition]=saved;return{condition:()=>cast(zerosLike(condition),"float32"),t:()=>mul(dy,cast(condition,dy.dtype)),e:()=>mul(dy,cast(logicalNot(condition),dy.dtype))}}};var seluGradConfig={kernelName:Selu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let mask=greater(x,scalar(0)),scaleAlpha2=scalar(SELU_SCALEALPHA),scale2=scalar(SELU_SCALE),greaterThanZeroDer=mul(dy,scale2),lessEqualZeroDer=mul(mul(dy,scaleAlpha2),exp(cast(x,"float32")));return where(mask,greaterThanZeroDer,lessEqualZeroDer)}}}};var sigmoidGradConfig={kernelName:Sigmoid,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,mul(y,sub(scalar(1),y)))}}};var signGradConfig={kernelName:Sign,gradFunc:dy=>({x:()=>zerosLike(dy)})};var sinGradConfig={kernelName:Sin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cos(cast(x,"float32")),dy)}}};var sinhGradConfig={kernelName:Sinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cosh(cast(x,"float32")),dy)}}};var sliceGradConfig={kernelName:Slice,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams(x,begin,size),paddings=[];for(let i=0;i<dy.rank;i++)paddings.push([begin_[i],inputShape[i]-begin_[i]-size_[i]]);return{x:()=>pad(dy,paddings)}}};var softmaxGradConfig={kernelName:Softmax,outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[y]=saved,{dim}=attrs,keepDims=!0,dyTimesY=mul(dy,y);return{logits:()=>sub(dyTimesY,mul(sum2(dyTimesY,[dim],keepDims),y))}}};var softplusGradConfig={kernelName:Softplus,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,sigmoid(x))}}};var spaceToBatchNDGradConfig={kernelName:SpaceToBatchND,gradFunc:(dy,saved,attrs)=>{let{blockShape,paddings}=attrs;return{x:()=>batchToSpaceND(dy,blockShape,paddings)}}};var splitVGradConfig={kernelName:SplitV,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return{x:()=>concat(dy,axis)}}};var sqrtGradConfig={kernelName:Sqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,mul(sqrt(cast(x,"float32")),2))}}};var squareGradConfig={kernelName:Square,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,mul(cast(x,"float32"),2))}}};var squaredDifferenceGradConfig={kernelName:SquaredDifference,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,two=scalar(2);return{a:()=>mul(dy,mul(two,sub(a,b))),b:()=>mul(dy,mul(two,sub(b,a)))}}};var stepGradConfig={kernelName:Step,gradFunc:dy=>({x:()=>zerosLike(dy)})};var subGradConfig={kernelName:Sub,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(neg(res),b.shape)}}}};var sumGradConfig={kernelName:Sum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs;parseAxisParam(axis,x.shape).forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=mul(expandedDy,ones2(x.shape,"float32"));return{x:()=>derX}}};var tanGradConfig={kernelName:Tan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,square(cos(x)))}}};var tanhGradConfig={kernelName:Tanh,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(sub(scalar(1),square(y)),dy)}}};var tileGradConfig={kernelName:Tile,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs;return{x:()=>{let xGrad=zerosLike(x);if(x.rank===1)for(let i=0;i<reps[0];++i)xGrad=add2(xGrad,slice(dy,[i*x.shape[0]],[x.shape[0]]));else if(x.rank===2)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1]],[x.shape[0],x.shape[1]]));else if(x.rank===3)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2]],[x.shape[0],x.shape[1],x.shape[2]]));else if(x.rank===4)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)for(let l=0;l<reps[3];++l)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2],l*x.shape[3]],[x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);return xGrad}}}};var transposeGradConfig={kernelName:Transpose,gradFunc:(dy,saved,attrs)=>{let transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation(perm);return{x:()=>transpose(dy,undoPerm)}}};var unpackGradConfig={kernelName:Unpack,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack(dy,axis)}}};var unsortedSegmentSumGradConfig={kernelName:UnsortedSegmentSum,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved;return{x:()=>gatherDropNegatives(dy,segmentIds)}}};function gatherDropNegatives(x,indices){let zeroClippedIndices=maximum(indices,zerosLike(indices)),gathered=gather(x,zeroClippedIndices),isPositive=greaterEqual(indices,scalar(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i<numIters;++i)isPositive=expandDims(isPositive,i+1);isPositive=logicalAnd(isPositive,ones2(gathered.shape,"bool"));let zeroSlice=zerosLike(gathered);return where(isPositive,gathered,zeroSlice)}var zerosLikeGradConfig={kernelName:ZerosLike,gradFunc:dy=>({x:()=>zerosLike(dy)})};var gradConfigs=[absGradConfig,acosGradConfig,acoshGradConfig,addGradConfig,addNGradConfig,argMaxGradConfig,argMinGradConfig,asinGradConfig,asinhGradConfig,atan2GradConfig,atanGradConfig,atanhGradConfig,avgPool3DGradConfig,avgPoolGradConfig,batchMatMulGradConfig,batchToSpaceNDGradConfig,broadcastToGradConfig,castGradConfig,ceilGradConfig,clipByValueGradConfig,complexAbsGradConfig,concatGradConfig,conv2DBackpropInputGradConfig,conv2DGradConfig,conv3DGradConfig,cosGradConfig,coshGradConfig,cumsumGradConfig,depthwiseConv2dNativeGradConfig,dilation2dGradConfig,divGradConfig,eluGradConfig,erfGradConfig,expGradConfig,expandDimsGradConfig,expm1GradConfig,floorDivGradConfig,floorGradConfig,fusedBatchNormGradConfig,gatherGradConfig,greaterEqualGradConfig,identityGradConfig,isFiniteGradConfig,isInfGradConfig,isNanGradConfig,leakyReluGradConfig,log1pGradConfig,logGradConfig,logSoftmaxGradConfig,lrnGradConfig,maxGradConfig,maxGradConfig,maximumGradConfig,maxPool3DGradConfig,maxPoolGradConfig,meanGradConfig,minGradConfig,minimumGradConfig,mirrorPadGradConfig,modGradConfig,multiplyGradConfig,negGradConfig,oneHotGradConfig,onesLikeGradConfig,packGradConfig,padV2GradConfig,padV2GradConfig,powGradConfig,preluGradConfig,reciprocalGradConfig,relu6GradConfig,reluGradConfig,reshapeGradConfig,resizeBilinearGradConfig,resizeNearestNeighborGradConfig,reverseGradConfig,roundGradConfig,rsqrtGradConfig,selectGradConfig,seluGradConfig,sigmoidGradConfig,signGradConfig,sinGradConfig,sinhGradConfig,sliceGradConfig,softmaxGradConfig,softplusGradConfig,spaceToBatchNDGradConfig,spaceToBatchNDGradConfig,splitVGradConfig,splitVGradConfig,sqrtGradConfig,squaredDifferenceGradConfig,squareGradConfig,stepGradConfig,subGradConfig,sumGradConfig,tanGradConfig,tanhGradConfig,tileGradConfig,transposeGradConfig,unpackGradConfig,unsortedSegmentSumGradConfig,zerosLikeGradConfig];for(let gradientConfig of gradConfigs)registerGradient(gradientConfig);Tensor.prototype.abs=function(){return this.throwIfDisposed(),abs(this)};Tensor.prototype.acos=function(){return this.throwIfDisposed(),acos(this)};Tensor.prototype.acosh=function(){return this.throwIfDisposed(),acosh(this)};Tensor.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict(this,x)};Tensor.prototype.add=function(b){return this.throwIfDisposed(),add2(this,b)};Tensor.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all(this,axis,keepDims)};Tensor.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any(this,axis,keepDims)};Tensor.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax(this,axis)};Tensor.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin(this,axis)};Tensor.prototype.asScalar=function(){return this.throwIfDisposed(),assert(this.size===1,()=>"The array must have only 1 element."),reshape(this,[])};Tensor.prototype.asType=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.as1D=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape(this,[rows,columns])};Tensor.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape(this,[rows,columns,depth])};Tensor.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2])};Tensor.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2,depth3])};Tensor.prototype.asin=function(){return this.throwIfDisposed(),asin(this)};Tensor.prototype.asinh=function(){return this.throwIfDisposed(),asinh(this)};Tensor.prototype.atan=function(){return this.throwIfDisposed(),atan(this)};Tensor.prototype.atan2=function(b){return this.throwIfDisposed(),atan2(this,b)};Tensor.prototype.atanh=function(){return this.throwIfDisposed(),atanh(this)};Tensor.prototype.avgPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),avgPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND(this,blockShape,crops)};Tensor.prototype.batchNorm=function(mean4,variance,offset,scale2,varianceEpsilon){return this.throwIfDisposed(),batchNorm(this,mean4,variance,offset,scale2,varianceEpsilon)};Tensor.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo(this,shape)};Tensor.prototype.cast=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.ceil=function(){return this.throwIfDisposed(),ceil(this)};Tensor.prototype.clipByValue=function(min6,max6){return this.throwIfDisposed(),clipByValue(this,min6,max6)};Tensor.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor&&(x=[x]),concat([this,...x],axis)};Tensor.prototype.conv1d=function(filter,stride,pad3,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d(this,filter,stride,pad3,dataFormat,dilation,dimRoundingMode)};Tensor.prototype.conv2dTranspose=function(filter,outputShape,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose(this,filter,outputShape,strides,pad3,dimRoundingMode)};Tensor.prototype.conv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.cos=function(){return this.throwIfDisposed(),cos(this)};Tensor.prototype.cosh=function(){return this.throwIfDisposed(),cosh(this)};Tensor.prototype.cumsum=function(axis,exclusive,reverse5){return this.throwIfDisposed(),cumsum(this,axis,exclusive,reverse5)};Tensor.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace(this,blockSize,dataFormat)};Tensor.prototype.depthwiseConv2D=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return deprecationWarn("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.depthwiseConv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.dilation2d=function(filter,strides,pad3,dilations,dataFormat){return this.throwIfDisposed(),dilation2d(this,filter,strides,pad3,dilations,dataFormat)};Tensor.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan(this,b)};Tensor.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict(this,x)};Tensor.prototype.div=function(b){return this.throwIfDisposed(),div(this,b)};Tensor.prototype.dot=function(b){return this.throwIfDisposed(),dot(this,b)};Tensor.prototype.elu=function(){return this.throwIfDisposed(),elu(this)};Tensor.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict(this,x)};Tensor.prototype.equal=function(b){return this.throwIfDisposed(),equal(this,b)};Tensor.prototype.erf=function(){return this.throwIfDisposed(),erf(this)};Tensor.prototype.exp=function(){return this.throwIfDisposed(),exp(this)};Tensor.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims(this,axis)};Tensor.prototype.expm1=function(){return this.throwIfDisposed(),expm1(this)};Tensor.prototype.fft=function(){return this.throwIfDisposed(),fft(this)};Tensor.prototype.flatten=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.floor=function(){return this.throwIfDisposed(),floor(this)};Tensor.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv(this,b)};Tensor.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather(this,indices,axis)};Tensor.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict(this,x)};Tensor.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual(this,b)};Tensor.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict(this,x)};Tensor.prototype.greater=function(b){return this.throwIfDisposed(),greater(this,b)};Tensor.prototype.ifft=function(){return this.throwIfDisposed(),ifft(this)};Tensor.prototype.irfft=function(){return this.throwIfDisposed(),irfft(this)};Tensor.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite2(this)};Tensor.prototype.isInf=function(){return this.throwIfDisposed(),isInf(this)};Tensor.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN2(this)};Tensor.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu(this,alpha)};Tensor.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict(this,x)};Tensor.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual(this,b)};Tensor.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict(this,x)};Tensor.prototype.less=function(b){return this.throwIfDisposed(),less(this,b)};Tensor.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization(this,depthRadius,bias,alpha,beta)};Tensor.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid(this)};Tensor.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax(this,axis)};Tensor.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp(this,axis,keepDims)};Tensor.prototype.log=function(){return this.throwIfDisposed(),log(this)};Tensor.prototype.log1p=function(){return this.throwIfDisposed(),log1p(this)};Tensor.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd(this,b)};Tensor.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot(this)};Tensor.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr(this,b)};Tensor.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor(this,b)};Tensor.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul(this,b,transposeA,transposeB)};Tensor.prototype.maxPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),maxPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max(this,axis,keepDims)};Tensor.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict(this,x)};Tensor.prototype.maximum=function(b){return this.throwIfDisposed(),maximum(this,b)};Tensor.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean(this,axis,keepDims)};Tensor.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min(this,axis,keepDims)};Tensor.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict(this,x)};Tensor.prototype.minimum=function(b){return this.throwIfDisposed(),minimum(this,b)};Tensor.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad(this,paddings,mode)};Tensor.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict(this,x)};Tensor.prototype.mod=function(b){return this.throwIfDisposed(),mod(this,b)};Tensor.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict(this,x)};Tensor.prototype.mul=function(b){return this.throwIfDisposed(),mul(this,b)};Tensor.prototype.neg=function(){return this.throwIfDisposed(),neg(this)};Tensor.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm(this,ord,axis,keepDims)};Tensor.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict(this,x)};Tensor.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual(this,b)};Tensor.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot(this,depth,onValue,offValue)};Tensor.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike(this)};Tensor.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad(this,paddings,constantValue)};Tensor.prototype.pool=function(windowShape,poolingType,padding,dilationRate,strides){return this.throwIfDisposed(),pool(this,windowShape,poolingType,padding,dilationRate,strides)};Tensor.prototype.powStrict=function(exp4){return this.throwIfDisposed(),powStrict(this,exp4)};Tensor.prototype.pow=function(exp4){return this.throwIfDisposed(),pow(this,exp4)};Tensor.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu(this,alpha)};Tensor.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod(this,axis,keepDims)};Tensor.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal(this)};Tensor.prototype.relu=function(){return this.throwIfDisposed(),relu(this)};Tensor.prototype.relu6=function(){return this.throwIfDisposed(),relu6(this)};Tensor.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape(this,x.shape)};Tensor.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape(this,shape)};Tensor.prototype.resizeBilinear=function(newShape2D,alignCorners,halfPixelCenters){return this.throwIfDisposed(),resizeBilinear(this,newShape2D,alignCorners,halfPixelCenters)};Tensor.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners,halfFloatCenters){return this.throwIfDisposed(),resizeNearestNeighbor(this,newShape2D,alignCorners,halfFloatCenters)};Tensor.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse(this,axis)};Tensor.prototype.rfft=function(){return this.throwIfDisposed(),rfft(this)};Tensor.prototype.round=function(){return this.throwIfDisposed(),round2(this)};Tensor.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt(this)};Tensor.prototype.selu=function(){return this.throwIfDisposed(),selu(this)};Tensor.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d(this,depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat)};Tensor.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid(this)};Tensor.prototype.sign=function(){return this.throwIfDisposed(),sign(this)};Tensor.prototype.sin=function(){return this.throwIfDisposed(),sin(this)};Tensor.prototype.sinh=function(){return this.throwIfDisposed(),sinh(this)};Tensor.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice(this,begin,size)};Tensor.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax(this,dim)};Tensor.prototype.softplus=function(){return this.throwIfDisposed(),softplus(this)};Tensor.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND(this,blockShape,paddings)};Tensor.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split(this,numOrSizeSplits,axis)};Tensor.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt(this)};Tensor.prototype.square=function(){return this.throwIfDisposed(),square(this)};Tensor.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference(this,b)};Tensor.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict(this,x)};Tensor.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze(this,axis)};Tensor.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor?[this,x]:[this,...x];return stack(tensorsToBeStacked,axis)};Tensor.prototype.step=function(alpha){return this.throwIfDisposed(),step(this,alpha)};Tensor.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict(this,x)};Tensor.prototype.sub=function(b){return this.throwIfDisposed(),sub(this,b)};Tensor.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum2(this,axis,keepDims)};Tensor.prototype.tan=function(){return this.throwIfDisposed(),tan(this)};Tensor.prototype.tanh=function(){return this.throwIfDisposed(),tanh2(this)};Tensor.prototype.tile=function(reps){return this.throwIfDisposed(),tile(this,reps)};Tensor.prototype.toBool=function(){return this.throwIfDisposed(),cast(this,"bool")};Tensor.prototype.toFloat=function(){return this.throwIfDisposed(),cast(this,"float32")};Tensor.prototype.toInt=function(){return this.throwIfDisposed(),cast(this,"int32")};Tensor.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk(this,k,sorted)};Tensor.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose(this,perm)};Tensor.prototype.unique=function(axis){return this.throwIfDisposed(),unique(this,axis)};Tensor.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum(this,segmentIds,numSegments)};Tensor.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack(this,axis)};Tensor.prototype.where=function(condition,x){return this.throwIfDisposed(),where(condition,this,x)};Tensor.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike(this)};var exports_constraints_exports={};__export(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon(){return _epsilon==null&&(_epsilon=backend().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;i<numValues;i++)newArray=newArray.concat(value);return newArray}else{let newArray=new Array(numValues);return newArray.fill(value),newArray}}function assert2(val,message){if(!val)throw new AssertionError(message)}function count(array2,refernce){let counter=0;for(let item of array2)item===refernce&&counter++;return counter}function singletonOrArray(xs){return xs.length===1?xs[0]:xs}function toList(x){return Array.isArray(x)?x:[x]}function toSnakeCase(name){let insecure=name.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return insecure[0]!=="_"?insecure:"private"+insecure}function toCamelCase(identifier){return identifier.length<=1||identifier.indexOf("_")===-1?identifier:identifier.replace(/[_]+(\w|$)/g,(m,p1)=>p1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;let dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config){if(!(config==null||typeof config!="object"))if(Array.isArray(config))config.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config);for(let field of fields){let value=config[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
 2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config=identifier;if(config.className==null||config.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.
 'className' and 'config' must set.`);let className=config.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
-2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config.config);let returnObj=fromConfig(cls,config.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return a<b?-1:a>b?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique2(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now2=util_exports.now();return now2-lastTime<waitMs||(lastTime=now2,lastResult=f(...args)),lastResult}}function mapActivationToFusedKernel(activationName){return activationName==="relu"?"relu":activationName==="linear"?"linear":activationName==="elu"?"elu":null}function calcL2Norms(w,axis){return tidy(()=>sqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config){return new MinMaxNorm(config)}var exports_initializers_exports={};__export(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros2});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"];var nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i<end;++i)prod5*=array2[i];return prod5}function toArray1D(array2){return array2=Array.isArray(array2)?new Float32Array(array2):array2,tensor1d(array2)}function min2(array2){return min(toArray1D(array2)).dataSync()[0]}function max2(array2){return max(toArray1D(array2)).dataSync()[0]}function range2(begin,end){if(end<begin)throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);let out=[];for(let i=begin;i<end;++i)out.push(i);return out}function cast2(x,dtype){return x.asType(dtype)}function expandDims2(x,axis=-1){let outShape=x.shape.slice();return axis<0&&(axis=outShape.length+axis+1),outShape.splice(axis,0,1),x.reshape(outShape)}function repeat(x,n){return tidy(()=>{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims2(x,1);return tile2(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile2(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square2(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu2(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale2=this.scale;if(this.mode==="fanIn"?scale2/=Math.max(1,fanIn):this.mode==="fanOut"?scale2/=Math.max(1,fanOut):scale2/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale2);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale2);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config={};return config.className=className,config.config={},deserializeInitializer(config)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros2(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity(args){return new Identity2(args)}function varianceScaling(config){return new VarianceScaling(config)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add3,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d3,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d4,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu3,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum2,minimum:()=>minimum2,multiply:()=>multiply,permute:()=>permute,prelu:()=>prelu2,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape2,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax2,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),!(this.inputSpec==null||this.inputSpec.length===0)){let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndex<inputs.length;inputIndex++){let x=inputs[inputIndex],spec=inputSpec[inputIndex];if(spec!=null){let ndim=x.rank;if(spec.ndim!=null&&ndim!==spec.ndim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);if(spec.maxNDim!=null&&ndim>spec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim<spec.minNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);if(spec.dtype!=null&&x.dtype!==spec.dtype)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);if(spec.axes){let xShape=x.shape;for(let key in spec.axes){let axis=Number(key),value=spec.axes[key],xShapeAtAxis=axis>=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i<spec.shape.length;++i){let specDim=spec.shape[i],dim=x.shape[i];if(specDim!=null&&dim!=null&&specDim!==dim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`)}}}}}call(inputs,kwargs){return inputs}invokeCallHook(inputs,kwargs){this._callHook!=null&&this._callHook(inputs,kwargs)}setCallHook(callHook){this._callHook=callHook}clearCallHook(){this._callHook=null}apply(inputs,kwargs){kwargs=kwargs||{},this.assertNotDisposed();let inputsList=toList(inputs),allAreSymbolic=!0;for(let input2 of inputsList)if(!(input2 instanceof SymbolicTensor)){allAreSymbolic=!1;break}let noneAreSymbolic=!0;for(let input2 of inputsList)if(input2 instanceof SymbolicTensor){noneAreSymbolic=!1;break}if(allAreSymbolic===noneAreSymbolic)throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return nameScope(this.name,()=>{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node of this.inboundNodes){let shapeString=JSON.stringify(node.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length!==0){let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;i<paramValues.length;++i){let pv=paramValues[i],p2=params[i],w=weights[i];if(!util_exports.arraysEqual(pv.shape,w.shape))throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);weightValueTuples.push([p2,w])}batchSetValue(weightValueTuples)}})}addWeight(name,shape,dtype,initializer,regularizer,trainable,constraint){if(this._addedWeightNames.indexOf(name)!==-1)throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);this._addedWeightNames.push(name),dtype==null&&(dtype="float32"),this.fastWeightInitDuringBuild&&(initializer=getInitializer("zeros"));let initValue=initializer.apply(shape,dtype),weight=new LayerVariable(initValue,dtype,name,trainable,constraint);return initValue.dispose(),regularizer!=null&&this.addLoss(()=>regularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;i<outputTensors.length;i++)outputTensors[i].sourceLayer=this,outputTensors[i].nodeIndex=this.inboundNodes.length-1,outputTensors[i].tensorIndex=i}getConfig(){let config={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(config.batchInputShape=this.batchInputShape),this.dtype!=null&&(config.dtype=this.dtype),config}disposeWeights(){return this.weights.forEach(weight=>weight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount===0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node=layer.inboundNodes[nodeIndex];if(node.inboundLayers.length===0)return node.inputTensors;{let sourceTensors=[];for(let i=0;i<node.inboundLayers.length;i++){let x=node.inputTensors[i],layer2=node.inboundLayers[i],nodeIndex2=node.nodeIndices[i],previousSources=getSourceInputs(x,layer2,nodeIndex2);for(let x2 of previousSources)sourceTensors.indexOf(x2)===-1&&sourceTensors.push(x2)}return sourceTensors}}}var InputLayer=class extends Layer{constructor(args){super({dtype:args.dtype,name:args.name!=null?args.name:getUid("input").toString()});if(args.batchSize==null&&(args.batchSize=null),args.sparse==null&&(args.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=args.sparse,args.inputShape!=null&&args.batchInputShape!=null)throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let batchInputShape=args.batchInputShape;if(batchInputShape==null){if(args.inputShape==null)throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");batchInputShape=[args.batchSize].concat(args.inputShape)}else if(args.batchSize!=null)throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let dtype=args.dtype||"float32";this.batchInputShape=batchInputShape,this.dtype=dtype,this.inputSpec=[{shape:batchInputShape}];let inputTensor=new SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);inputTensor.nodeIndex=0,inputTensor.tensorIndex=0,new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[inputTensor],outputTensors:[inputTensor],inputMasks:[null],outputMasks:[null],inputShapes:[batchInputShape],outputShapes:[batchInputShape]})}apply(inputs,kwargs){throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};InputLayer.className="InputLayer";serialization_exports.registerClass(InputLayer);function Input(config){if(config.batchShape==null&&config.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(config.batchShape!=null&&config.shape!=null)throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let batchShape=config.batchShape;config.shape!=null&&batchShape==null&&(batchShape=[null].concat(config.shape));let dtype=config.dtype;return dtype==null&&(dtype="float32"),new InputLayer({batchInputShape:batchShape,name:config.name,dtype,sparse:config.sparse}).inboundNodes[0].outputTensors[0]}async function resolveScalarsInLogs(logs){if(logs!=null){let promises=[],keys=[],scalarsToDispose=[];for(let key in logs){let value=logs[key];if(typeof value!="number"){let valueScalar=value;promises.push(valueScalar.data()),keys.push(key),scalarsToDispose.push(valueScalar)}}if(promises.length>0){let values=await Promise.all(promises);for(let i=0;i<values.length;++i)logs[keys[i]]=values[i][0];dispose(scalarsToDispose)}}}function disposeTensorsInLogs(logs){if(logs!=null)for(let key in logs){let value=logs[key];typeof value!="number"&&value.dispose()}}var ModelLoggingVerbosity;(function(ModelLoggingVerbosity2){ModelLoggingVerbosity2[ModelLoggingVerbosity2.SILENT=0]="SILENT",ModelLoggingVerbosity2[ModelLoggingVerbosity2.VERBOSE=1]="VERBOSE"})(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));var DEFAULT_YIELD_EVERY_MS=125,BaseCallback=class{constructor(){this.validationData=null}setParams(params){this.params=params}async onEpochBegin(epoch,logs){}async onEpochEnd(epoch,logs){}async onBatchBegin(batch,logs){}async onBatchEnd(batch,logs){}async onTrainBegin(logs){}async onTrainEnd(logs){}setModel(model2){}},CallbackList=class{constructor(callbacks2,queueLength=10){callbacks2==null&&(callbacks2=[]),this.callbacks=callbacks2,this.queueLength=queueLength}append(callback){this.callbacks.push(callback)}setParams(params){for(let callback of this.callbacks)callback.setParams(params)}setModel(model2){for(let callback of this.callbacks)callback.setModel(model2)}async onEpochBegin(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochBegin(epoch,logs)}async onEpochEnd(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochEnd(epoch,logs)}async onBatchBegin(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchBegin(batch,logs)}async onBatchEnd(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchEnd(batch,logs)}async onTrainBegin(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainBegin(logs)}async onTrainEnd(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainEnd(logs)}},BaseLogger=class extends BaseCallback{constructor(){super()}async onEpochBegin(epoch){this.seen=0,this.totals={}}async onBatchEnd(batch,logs){logs==null&&(logs={});let batchSize=logs.size==null?0:logs.size;this.seen+=batchSize;for(let key in logs){let value=logs[key];if(typeof value=="number")this.totals.hasOwnProperty(key)||(this.totals[key]=0),this.totals[key]=this.totals[key]+value*batchSize;else{let oldTotalsToDispose;key in this.totals?oldTotalsToDispose=this.totals[key]:this.totals[key]=0;let total=tidy(()=>add2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log5=mul(div(1,this.seen),this.totals[key]);logs[key]=log5,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;i<valueArray.length;++i)if(typeof valueArray[i]!="number"){let valueScalar=valueArray[i];promises.push(valueScalar.data()),keys.push(key),indices.push(i)}}let values=await Promise.all(promises);for(let n=0;n<values.length;++n)this.history[keys[n]][indices[n]].dispose(),this.history[keys[n]][indices[n]]=values[n][0]}},CustomCallback=class extends BaseCallback{constructor(args,yieldEvery){super();if(this.currentEpoch=0,this.yieldEvery=yieldEvery||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=DEFAULT_YIELD_EVERY_MS),this.yieldEvery==="never"&&args.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");util_exports.isNumber(this.yieldEvery)&&(this.maybeWait=debounce(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=args.onTrainBegin,this.trainEnd=args.onTrainEnd,this.epochBegin=args.onEpochBegin,this.epochEnd=args.onEpochEnd,this.batchBegin=args.onBatchBegin,this.batchEnd=args.onBatchEnd,this.yield=args.onYield}async maybeWait(epoch,batch,logs){let ps=[];this.yield!=null&&(await resolveScalarsInLogs(logs),ps.push(this.yield(epoch,batch,logs))),ps.push(nextFrame()),await Promise.all(ps)}async onEpochBegin(epoch,logs){this.currentEpoch=epoch,this.epochBegin!=null&&(await resolveScalarsInLogs(logs),await this.epochBegin(epoch,logs))}async onEpochEnd(epoch,logs){let ps=[];this.epochEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.epochEnd(epoch,logs))),this.yieldEvery==="epoch"&&ps.push(nextFrame()),await Promise.all(ps)}async onBatchBegin(batch,logs){this.batchBegin!=null&&(await resolveScalarsInLogs(logs),await this.batchBegin(batch,logs))}async onBatchEnd(batch,logs){let ps=[];this.batchEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.batchEnd(batch,logs))),this.yieldEvery==="batch"?ps.push(nextFrame()):util_exports.isNumber(this.yieldEvery)&&ps.push(this.maybeWait(this.currentEpoch,batch,logs)),await Promise.all(ps)}async onTrainBegin(logs){this.trainBegin!=null&&(await resolveScalarsInLogs(logs),await this.trainBegin(logs))}async onTrainEnd(logs){this.trainEnd!=null&&(await resolveScalarsInLogs(logs),await this.trainEnd(logs))}};function standardizeCallbacks(callbacks2,yieldEvery){return callbacks2==null&&(callbacks2={}),callbacks2 instanceof BaseCallback?[callbacks2]:Array.isArray(callbacks2)&&callbacks2[0]instanceof BaseCallback?callbacks2:toList(callbacks2).map(callbackConfig=>new CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square2(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square2(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon(),Number.MAX_VALUE),firstLog=log(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon(),Number.MAX_VALUE),secondLog=log(add2(1,clippedTrue));return mean(square2(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square2(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log22=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log22);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon(),1-epsilon()),neg(sum2(mul(target.toFloat(),log(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon(),1-epsilon());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon(),1-epsilon()),y=log(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon(),1),clippedPred=clipByValue(yPred,epsilon(),1);return sum2(mul(yTrue,log(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log(add2(epsilon(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast2(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast2(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon()),Adam:()=>train.adam(.001,.9,.999,epsilon()),Adamax:()=>train.adamax(.002,.9,.999,epsilon(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys){if(typeof key!="string")return!1;if(!plainObjectCheck(x[key]))return!1}return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i<layers.length;++i)sequentialLike?printLayerSummary(layers[i],positions,printFn):printLayerSummaryWithConnections(layers[i],positions,relevantNodes,printFn),printFn((i===layers.length-1?"=":"_").repeat(lineLength));model2.checkTrainableWeightsConsistency();let trainableCount=countTrainableParams(model2),nonTrainableCount=countParamsInWeights(model2.nonTrainableWeights);printFn(`Total params: ${trainableCount+nonTrainableCount}`),printFn(`Trainable params: ${trainableCount}`),printFn(`Non-trainable params: ${nonTrainableCount}`),printFn("_".repeat(lineLength))}function countTrainableParams(model2){let trainableCount;return model2.collectedTrainableWeights!=null?trainableCount=countParamsInWeights(model2.collectedTrainableWeights):trainableCount=countParamsInWeights(model2.trainableWeights),trainableCount}function isModelSequentialLike(model2){let sequentialLike=!0,nodesByDepth=[],nodes=[];for(let depth in model2.nodesByDepth)nodesByDepth.push(model2.nodesByDepth[depth]);for(let depthNodes of nodesByDepth){if(depthNodes.length>1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node of layer.inboundNodes)if(nodes.indexOf(node)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i<fields.length;++i)i>0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node)===-1))for(let i=0;i<node.inboundLayers.length;++i){let inboundLayer=node.inboundLayers[i].name,inboundLayerIndex=node.nodeIndices[i],inboundTensorIndex=node.tensorIndices[i];connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`)}let name=layer.name,className=layer.getClassName(),firstConnection=connections.length===0?"":connections[0],fields=[`${name} (${className})`,outputShape,layer.countParams().toString(),firstConnection];printRow(fields,positions,printFn);for(let i=1;i<connections.length;++i)printRow(["","","",connections[i]],positions,printFn)}function isArrayItemInputOrOutputName(key,index,value){return(key==="inboundNodes"||key==="outputLayers"||key==="inputLayers")&&index===0&&typeof value=="string"}function convertPythonicToTs(pythonicConfig,key){if(pythonicConfig===null)return null;if(typeof pythonicConfig=="string")return toCamelCase(pythonicConfig);if(typeof pythonicConfig=="number"||typeof pythonicConfig=="boolean")return pythonicConfig;if(pythonicConfig instanceof Array){let tsArray=[],arrayLength=pythonicConfig.length;for(let i=0;i<arrayLength;++i){let item=pythonicConfig[i];isArrayItemInputOrOutputName(key,i,item)?tsArray.push(item):tsArray.push(convertPythonicToTs(item,key))}return tsArray}else{let tsDict={};for(let pythonicKey of Object.keys(pythonicConfig)){let pythonicValue=pythonicConfig[pythonicKey];if(pythonicKey==="name"&&typeof pythonicValue=="string")tsDict[pythonicKey]=pythonicValue;else{let tsKey=toCamelCase(pythonicKey);tsDict[tsKey]=convertPythonicToTs(pythonicValue,tsKey)}}return tsDict}}function convertTsToPythonic(tsConfig,key){if(tsConfig==null)return null;if(typeof tsConfig=="string")return toSnakeCase(tsConfig);if(typeof tsConfig=="number"||typeof tsConfig=="boolean")return tsConfig;if(tsConfig instanceof Array){let pyArray=[],arrayLength=tsConfig.length;for(let i=0;i<arrayLength;++i){let item=tsConfig[i];isArrayItemInputOrOutputName(key,i,item)?pyArray.push(item):pyArray.push(convertTsToPythonic(item,key))}return pyArray}else{let pyDict={};for(let tsKey of Object.keys(tsConfig)){let tsValue=tsConfig[tsKey],pyKey=toSnakeCase(tsKey);(tsKey==="name"||tsKey==="className")&&typeof tsValue=="string"?pyDict[pyKey]=tsValue:pyDict[pyKey]=convertTsToPythonic(tsValue,tsKey)}return pyDict}}var version2="2.8.2";function assertFeedCompatibility(key,val){if(key.dtype==null||key.dtype===val.dtype)return val;try{return cast(val,key.dtype)}catch(err){throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`)}}var FeedDict=class{constructor(feeds){if(this.id2Value={},this.id2Mask={},this.name2Id={},feeds instanceof FeedDict)for(let id in feeds.id2Value)this.id2Value[id]=feeds.id2Value[id],id in feeds.id2Mask&&(this.id2Mask[id]=feeds.id2Mask[id]);else{if(feeds==null)return;for(let feed of feeds)this.add(feed.key,feed.value)}}add(key,value,mask){if(this.id2Value[key.id]==null)this.id2Value[key.id]=assertFeedCompatibility(key,value),this.name2Id[key.name]=key.id,mask!=null&&(this.id2Mask[key.id]=mask);else throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);return this}addFeed(feed){this.add(feed.key,feed.value)}hasKey(key){return this.id2Value[key.id]!=null}names(){return Object.keys(this.name2Id)}getValue(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Value[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Value[id]}}getMask(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Mask[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Mask[id]}}disposeMasks(){this.id2Mask!=null&&dispose(this.id2Mask)}},cachedSorted={},cachedRecipientCounts={};function execute(fetches,feedDict,kwargs,probe){let training=kwargs==null?!1:kwargs.training,arrayFetches=Array.isArray(fetches),fetchArray=arrayFetches?fetches:[fetches],outputNames=fetchArray.map(t=>t.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;i<sorted.length;++i){if(probe!=null){let numTensors=memory().numTensors;numTensors>probe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors<probe.minNumTensors&&(probe.minNumTensors=numTensors)}let symbolic=sorted[i],srcLayer=symbolic.sourceLayer;if(!(srcLayer instanceof InputLayer)){let inputValues=[],inputMasks=[],tensorsToDispose=[],maskExists=!1;for(let input2 of symbolic.inputs){let value=internalFeedDict.getValue(input2),mask=internalFeedDict.getMask(input2);inputValues.push(value),inputMasks.push(mask),mask!=null&&(maskExists=!0),training||(recipientCounts[input2.name]--,recipientCounts[input2.name]===0&&!feedDict.hasKey(input2)&&outputNames.indexOf(input2.name)===-1&&!value.isDisposed&&input2.sourceLayer.stateful!==!0&&tensorsToDispose.push(value))}maskExists&&(kwargs=kwargs||{},kwargs.mask=inputMasks[0]);let outputTensors=toList(srcLayer.apply(inputValues,kwargs)),outputMask=null;srcLayer.supportsMasking&&(outputMask=srcLayer.computeMask(inputValues,inputMasks));let layerOutputs=getNodeOutputs(symbolic),outputSymbolicTensors=Array.isArray(layerOutputs)?layerOutputs:[layerOutputs];for(let i2=0;i2<outputSymbolicTensors.length;++i2){internalFeedDict.hasKey(outputSymbolicTensors[i2])||internalFeedDict.add(outputSymbolicTensors[i2],outputTensors[i2],Array.isArray(outputMask)?outputMask[0]:outputMask);let index=outputNames.indexOf(outputSymbolicTensors[i2].name);index!==-1&&(finalOutputs[index]=outputTensors[i2])}training||dispose(tensorsToDispose)}}return internalFeedDict.disposeMasks(),arrayFetches?finalOutputs:finalOutputs[0]}function getTopologicalSortAndRecipientCounts(fetches,feedDict){util_exports.assert(fetches!=null&&fetches.length>0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)(recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name))&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;i<fetch3.sourceLayer.inboundNodes.length;++i)for(let outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors)if(outputTensor.id===fetch3.id){nodeIndex=i;break}layerOutputs=fetch3.sourceLayer.getOutputAt(nodeIndex)}return layerOutputs}var Container=class extends Layer{constructor(args){super({});if(this.containerNodes=new Set,this.name=args.name,this.name==null){let prefix=this.getClassName().toLowerCase();this.name=getUid(prefix)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(args.inputs)?this.inputs=args.inputs.slice():this.inputs=[args.inputs],Array.isArray(args.outputs)?this.outputs=args.outputs.slice():this.outputs=[args.outputs],unique2(this.inputs).length!==this.inputs.length)throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(x=>x.name)}`);unique2(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i];if(!(layer instanceof InputLayer))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);this.inputNames.push(layer.name),this.feedInputShapes.push(layer.batchInputShape),this.feedInputNames.push(layer.name)}for(let layer of this.outputLayers)this.outputNames.push(layer.name);this.internalInputShapes=this.inputs.map(x=>x.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node)===-1){this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node)===-1&&nodesInProgress2.push(node);let numInboundLayers=node.inboundLayers.length;for(let i=0;i<numInboundLayers;i++){let x=node.inputTensors[i],layer2=node.inboundLayers[i],nodeIndex2=node.nodeIndices[i],tensorIndex2=node.tensorIndices[i];buildMapOfGraph(x,finishedNodes2,nodesInProgress2,layer2,nodeIndex2,tensorIndex2)}for(finishedNodes2.push(node);nodesInProgress2.indexOf(node)>=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node),1);nodesInDecreasingDepth.push(node)}},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node of reversedNodesInDecreasingDepth){nodeIDToNode[node.id]=node,node.id in nodesDepths||(nodesDepths[node.id]=0);let depth=nodesDepths[node.id],previousDepth=layersDepths[node.outboundLayer.id]==null?0:layersDepths[node.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node.outboundLayer.id]=depth,layerIDToLayer[node.outboundLayer.id]=node.outboundLayer,nodesDepths[node.id]=depth;for(let i=0;i<node.inboundLayers.length;i++){let inboundLayer=node.inboundLayers[i],nodeIndex=node.nodeIndices[i],inboundNode=inboundLayer.inboundNodes[nodeIndex],previousDepth2=nodesDepths[inboundNode.id]==null?0:nodesDepths[inboundNode.id];nodesDepths[inboundNode.id]=Math.max(depth+1,previousDepth2),nodeIDToNode[inboundNode.id]=inboundNode}}let nodesByDepth={};for(let nodeID in nodesDepths){let depth=nodesDepths[nodeID];depth in nodesByDepth||(nodesByDepth[depth]=[]),nodesByDepth[depth].push(nodeIDToNode[nodeID])}let layersByDepth={};for(let layerID in layersDepths){let depth=layersDepths[layerID];depth in layersByDepth||(layersByDepth[depth]=[]),layersByDepth[depth].push(layerIDToLayer[layerID])}let depthKeys=Object.keys(layersByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndex<bIndex?-1:aIndex>bIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node of nodesByDepth[depth]){let layer=node.outboundLayer;if(layer!=null){for(let x of node.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version2}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i]);return execute(this.outputs,feedDict,kwargs)})}computeMask(inputs,mask){return tidy(()=>{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;i<inputShapes.length;i++){let layer=this.inputLayers[i],inputShape2=inputShapes[i],shapeKey=layer.name+"_0_0";layersToOutputShapes[shapeKey]=inputShape2}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)===-1){let inputShapes2=[];for(let j=0;j<node.inboundLayers.length;j++){let inboundLayer=node.inboundLayers[j],nodeIndex2=node.nodeIndices[j],tensorIndex=node.tensorIndices[j],shapeKey=`${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`,inputShape2=layersToOutputShapes[shapeKey];inputShapes2.push(inputShape2)}let outputShape=layer.computeOutputShape(singletonOrArray(inputShapes2)),outputShapes2=normalizeShapeList(outputShape),nodeIndex=layer.inboundNodes.indexOf(node);for(let j=0;j<outputShapes2.length;j++){let shapeKey=`${layer.name}_${nodeIndex}_${j}`;layersToOutputShapes[shapeKey]=outputShapes2[j]}}}}let outputShapes=[],outputShapeKeys=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],tensorIndex=this.outputLayersTensorIndices[i],shapeKey=`${layer.name}_${nodeIndex}_${tensorIndex}`;outputShapeKeys.push(shapeKey)}for(let i=0;i<outputShapeKeys.length;i++){let key=outputShapeKeys[i];assert2(key in layersToOutputShapes),outputShapes.push(layersToOutputShapes[key])}return singletonOrArray(outputShapes)}runInternalGraph(inputs,masks){masks==null&&(masks=pyListRepeat(null,inputs.length));let tensorMap={};for(let i=0;i<this.inputs.length;++i){let x=this.inputs[i],y=inputs[i],mask=masks[i];tensorMap[x.id]=[y,mask]}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer,referenceInputTensors=node.inputTensors,referenceOutputTensors=node.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node.callArgs!=null&&(kwargs=node.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i<referenceOutputTensors.length;++i){let x=referenceOutputTensors[i],y=outputTensors2[i],mask=outputMasks2[i];tensorMap[x.id]=[y,mask]}}}}let outputTensors=[],outputMasks=[],outputShapes=[];for(let x of this.outputs){assert2(x.id in tensorMap,`Could not compute output ${x.name} : ${x.id}`);let[tensor2,mask]=tensorMap[x.id];outputShapes.push(tensor2.shape),outputTensors.push(tensor2),outputMasks.push(mask)}return[outputTensors,outputMasks,outputShapes]}buildNodeConversionMap(layers){let nodeConversionMap={},keptNodes;for(let layer of this.layers){keptNodes=layer instanceof Container?1:0;for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let nodeKey=Container.nodeKey(layer,originalNodeIndex);this.containerNodes.has(nodeKey)&&(nodeConversionMap[nodeKey]=keptNodes,keptNodes+=1)}}return nodeConversionMap}getLayer(name,index){if(index!=null){if(this.layers.length<=index)throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);return this.layers[index]}else if(name==null)throw new ValueError("Provide either a layer name or layer index");for(let layer of this.layers)if(layer.name===name)return layer;throw new ValueError(`No such layer: ${name}`)}calculateLosses(){return tidy(()=>{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex<layer.inboundNodes.length;++nodeIndex){let nodeKey=Container.nodeKey(layer,nodeIndex);this.containerNodes.has(nodeKey)&&losses4.push(...layer.calculateLosses())}return losses4})}getConfig(){let config={name:this.name},nodeConversionMap=this.buildNodeConversionMap(this.layers),layerConfigs=[];for(let layer of this.layers){let layerClassName=layer.getClassName(),layerConfig=layer.getConfig(),filteredInboundNodes=[];for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let node=layer.inboundNodes[originalNodeIndex],nodeKey=Container.nodeKey(layer,originalNodeIndex),kwargs={};if(this.containerNodes.has(nodeKey)){if(node.callArgs)try{JSON.stringify(node.callArgs),kwargs=node.callArgs}catch(err){console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),kwargs={}}if(node.inboundLayers.length>0){let nodeData=[];for(let i=0;i<node.inboundLayers.length;i++){let inboundLayer=node.inboundLayers[i],nodeIndex=node.nodeIndices[i],tensorIndex=node.tensorIndices[i],nodeKey2=Container.nodeKey(inboundLayer,nodeIndex),newNodeIndex=nodeConversionMap[nodeKey2];newNodeIndex==null&&(newNodeIndex=0),nodeData.push([inboundLayer.name,newNodeIndex,tensorIndex,kwargs])}filteredInboundNodes.push(nodeData)}}}let dict={};dict.name=layer.name,dict.className=layerClassName,dict.config=layerConfig,dict.inboundNodes=filteredInboundNodes,layerConfigs.push(dict)}config.layers=layerConfigs;let modelInputs=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i],nodeIndex=this.inputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!!this.containerNodes.has(nodeKey)){let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.inputLayersTensorIndices[i];modelInputs.push([layer.name,newNodeIndex,tensorIndex])}}config.inputLayers=modelInputs;let modelOutputs=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!!this.containerNodes.has(nodeKey)){let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.outputLayersTensorIndices[i];modelOutputs.push([layer.name,newNodeIndex,tensorIndex])}}return config.outputLayers=modelOutputs,config}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let createdLayers={},unprocessedNodes={};function addUnprocessedNode(layer,nodeData){layer.name in unprocessedNodes?unprocessedNodes[layer.name].push(nodeData):unprocessedNodes[layer.name]=[nodeData]}function processNode(layer,nodeData){let inputTensors2=[],kwargs;for(let inputData of nodeData){let inboundLayerName=inputData[0],inboundNodeIndex=inputData[1],inboundTensorIndex=inputData[2];if(kwargs=inputData[3]==null?{}:inputData[3],!(inboundLayerName in createdLayers)){addUnprocessedNode(layer,nodeData);return}let inboundLayer=createdLayers[inboundLayerName];if(inboundLayer.inboundNodes.length<=inboundNodeIndex){addUnprocessedNode(layer,nodeData);return}let inboundNode=inboundLayer.inboundNodes[inboundNodeIndex];inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex])}inputTensors2.length>0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config.customObjects!=null?config.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config.name,layersFromConfig=config.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex<flattenedXs.length;xIndex++)util_exports.assert(flattenedXs[xIndex].shape[0]===batchSize,()=>`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex<flattenedYs.length;yIndex++)util_exports.assert(flattenedYs[yIndex].shape[0]===batchSize,()=>`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data){if(data.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data[0],ys:data[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch<args.epochs;){let epochLogs={};await callbackList.onEpochBegin(epoch);let stepsDone=0,batchIndex=0;for(hasBatchesPerEpoch||(dataIterator=await dataset.iterator());hasBatchesPerEpoch?stepsDone<args.batchesPerEpoch:!0;){let iteratorOut=await dataIterator.next();if(hasBatchesPerEpoch&&iteratorOut.done){console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch*args.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(iteratorOut.value!=null){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),batchLogs={};batchLogs.batch=batchIndex,batchLogs.size=xs[0].shape[0],await callbackList.onBatchBegin(batchIndex,batchLogs);let sampleWeights=[];if(args.classWeight!=null){let standardClassWeights=standardizeClassWeights(args.classWeight,model2.outputNames);for(let i=0;i<standardClassWeights.length;++i)sampleWeights.push(await standardizeWeights(ys[i],null,standardClassWeights[i]))}let ins=xs.concat(ys).concat(sampleWeights),outs=trainFunction(ins);dispose(ins);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),batchIndex++,stepsDone++}if(hasBatchesPerEpoch?stepsDone>=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i<model2.metricsNames.length;++i)epochLogs[`val_${model2.metricsNames[i]}`]=valOuts[i]}break}if(model2.stopTraining_)break}if(await callbackList.onEpochEnd(epoch,epochLogs),epoch++,model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}finally{model2.isTraining=!1}}function getStepsPerEpoch(dataset,args){let stepsPerEpoch=null;return args.batchesPerEpoch!=null?stepsPerEpoch=args.batchesPerEpoch:Number.isFinite(dataset.size)&&(stepsPerEpoch=dataset.size),stepsPerEpoch}function isDatasetObject(dataset){return typeof dataset.iterator=="function"}function isLazyIteratorObject(iterator){return typeof iterator.next=="function"}async function evaluateDataset(model2,dataset,args){args=args||{};let hasBatches=args.batches!=null,f=model2.testFunction,outs=[];if(args.verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch<args.batches:!0;){let iteratorOut=await dataIterator.next();if(outs=tidy(()=>{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));let batchSize=xsAndYs[0].shape[0];for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i],oldScalar=outs[i];outs[i]=tidy(()=>add2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i<outs.length;++i){let oldScalar=outs[i];outs[i]=div(outs[i],numExamples),dispose(oldScalar)}return singletonOrArray(outs)}function checkBatchSize(batchSize){util_exports.assert(batchSize>0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart<size;)batchEnd=batchStart+batchSize,batchEnd>=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range2(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch<epochs;++epoch){await callbackList.onEpochBegin(epoch);let epochLogs={};if(stepsPerEpoch!=null)throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");{if(shuffle2==="batch")throw new NotImplementedError("batch shuffling is not implemneted yet");shuffle2&&util_exports.shuffle(indexArray);let epochIndexArray1D=tensor1d(indexArray),batches=makeBatches(numTrainSamples,batchSize);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchLogs={};if(await callbackList.onBatchBegin(batchIndex,batchLogs),tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}if(batchIndex===batches.length-1&&doValidation){let valOuts=model2.testLoop(valF,valIns,batchSize);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=valOuts[i];keep(out),epochLogs["val_"+label]=out}}}),await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),model2.stopTraining_)break}epochIndexArray1D.dispose()}if(await callbackList.onEpochEnd(epoch,epochLogs),model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}async function fitTensors(model2,x,y,args={}){if(model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;let inputs,targets,inputValX,inputValY,valX,valY,sampleWeights;try{let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!1,standardizedOuts=await model2.standardizeUserData(x,y,args.sampleWeight,args.classWeight,checkBatchAxis,batchSize);inputs=standardizedOuts[0],targets=standardizedOuts[1],sampleWeights=standardizedOuts[2];let doValidation=!1,valIns;if(args.validationData!=null&&args.validationData.length>0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;i<tensors.length;++i){let tensor2=tensors[i];if(tensor2.rank===1)outs.push(expandDims2(tensor2,1));else{if(tensor2.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");outs.push(tensor2)}}return outs}function disposeNewTensors(tensors,refTensors){if(tensors!=null){let oldTensorIds=[];if(refTensors instanceof Tensor)oldTensorIds.push(refTensors.id);else if(Array.isArray(refTensors))refTensors.forEach(t=>oldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data!=null){let gotUnexpectedData=!1;if(isDataArray(data)&&data.length>0)gotUnexpectedData=!0;else if(isDataDict(data)){for(let key in data)if(data.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`)}return[]}if(data==null)return names.map(name=>null);let arrays;if(isDataDict(data)){data=data,arrays=[];for(let name of names){if(data[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data[name])}}else if(isDataArray(data)){if(data=data,data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);arrays=data}else{if(data=data,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);arrays=[data]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null){for(let i=0;i<names.length;++i)if(shapes[i]!=null){let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);for(let j=0;j<shapes[i].length;++j)if(!(j===0&&!checkBatchAxis)){let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim>=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique2(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique2(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i<targets.length;++i){let y=targets[i],loss=lossFns[i],shape=outputShapes[i];if(loss!=null){if(loss===categoricalCrossentropy&&y.shape[y.shape.length-1]===1)throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(keyLosses.indexOf(loss)!==-1){let slicedYShape=y.shape.slice(1),slicedShape=shape.slice(1);for(let j=0;j<slicedYShape.length;++j){let targetDim=slicedYShape[j],outDim=slicedShape[j];if(outDim!=null&&targetDim!==outDim)throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function checkInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){let arrays;if(Array.isArray(data)){if(data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);arrays=data}else{if(names.length>1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);arrays=[data]}if(shapes!=null){for(let i=0;i<names.length;++i)if(shapes[i]!=null){let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);for(let j=0;j<shapes[i].length;++j)if(!(j===0&&!checkBatchAxis)){let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim!==dim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`)}}}}function collectMetrics(metrics2,outputNames){if(metrics2==null||Array.isArray(metrics2)&&metrics2.length===0)return outputNames.map(name=>[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let shape=this.internalOutputShapes[i],name=this.outputNames[i];this.feedOutputNames.push(name),this.feedOutputShapes.push(shape),this.feedLossFns.push(this.lossFunctions[i])}let skipTargetIndices=[];this.metrics=args.metrics,this.metricsNames=["loss"],this.metricsTensors=[],nameScope("loss",()=>{for(let i=0;i<this.outputs.length;++i)if(skipTargetIndices.indexOf(i)===-1){let weightedLoss=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i<this.outputs.length;++i)if(skipTargetIndices.indexOf(i)===-1){let outputMetrics=nestedMetrics[i];(metrics2=>{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&(this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?"))}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i])}else for(let input2 of this.inputs){let tensorValue=inputs[input2.name];if(tensorValue==null)throw new ValueError(`No value is provided for the model's input ${input2.name}`);feedDict.add(input2,tensorValue)}let executeOutputs=execute(outputSymbolicTensors,feedDict);return outputsIsArray?executeOutputs:executeOutputs[0]}retrieveSymbolicTensors(symbolicTensorNames){let outputSymbolicTensors=pyListRepeat(null,symbolicTensorNames.length),outputsRemaining=symbolicTensorNames.length;for(let layer of this.layers){let layerOutputs=Array.isArray(layer.output)?layer.output:[layer.output],layerOutputNames=layerOutputs.map(output=>output.name);for(let i=0;i<symbolicTensorNames.length;++i){let index=layerOutputNames.indexOf(symbolicTensorNames[i]);if(index!==-1&&(outputSymbolicTensors[i]=layerOutputs[index],outputsRemaining--),outputsRemaining===0)break}if(outputsRemaining===0)break}if(outputsRemaining>0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex<batches.length;++batchIndex)tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;i<insBatch.length;++i)feeds.push({key:this.inputs[i],value:insBatch[i]});else feeds.push({key:this.inputs[0],value:insBatch});let feedDict=new FeedDict(feeds);return execute(this.outputs,feedDict)}).forEach((batchOut,i)=>outsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i<this.feedOutputShapes.length;++i){let outputShape=this.feedOutputShapes[i];this.feedLossFns[i]===sparseCategoricalCrossentropy?outputShapes.push(outputShape.slice(0,outputShape.length-1).concat([1])):outputShapes.push(outputShape)}if(x=standardizeInputData(x,this.feedInputNames,this.feedInputShapes,!1,"input"),y=standardizeInputData(y,this.feedOutputNames,outputShapes,!1,"target"),checkArrayLengths(x,y,null),checkLossAndTargetCompatibility(y,this.feedLossFns,this.feedOutputShapes),this.stateful&&batchSize!=null&&batchSize>0&&x[0].shape[0]%batchSize!==0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i<classWeights.length;++i)standardSampleWeights.push(await standardizeWeights(standardYs[i],null,classWeights[i]))}return[standardXs,standardYs,standardSampleWeights]}testLoop(f,ins,batchSize,verbose=0,steps){return tidy(()=>{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range2(0,numSamples));for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(indexArray,batchStart,batchEnd-batchStart),insBatch=sliceArraysByIndices(ins,batchIds),batchOuts=f(insBatch);if(batchIndex===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i];outs[i]=add2(outs[i],mul(batchEnd-batchStart,batchOut))}}for(let i=0;i<outs.length;++i)outs[i]=div(outs[i],numSamples)}return outs})}getDedupedMetricsNames(){let outLabels=this.metricsNames,dedupedOutLabels=[];for(let i=0;i<outLabels.length;++i){let label=outLabels[i],newLabel=label;count(outLabels,label)>1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data=>{let lossValues=[],inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict,{training:!0}),totalLoss;for(let i=0;i<this.lossFunctions.length;++i){let loss=this.lossFunctions[i](targets[i],outputs[i]);sampleWeights[i]!=null&&(loss=computeWeightedLoss2(loss,sampleWeights[i]));let meanLoss=mean(loss);lossValues.push(meanLoss),i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss)}for(let i=0;i<this.metricsTensors.length;++i){let weightedMetric;if(this.outputs.length>1&&i<this.outputs.length)weightedMetric=lossValues[i];else{let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1];weightedMetric=mean(metric(targets[outputIndex],outputs[outputIndex]))}keep(weightedMetric),metricsValues.push(weightedMetric)}return totalLoss=mean(totalLoss),this.calculateLosses().forEach(regularizerLoss=>{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict);for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=mean(lossFunction(targets[i],outputs[i]));i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss),valOutputs.push(totalLoss)}for(let i=0;i<this.metricsTensors.length;++i){let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1],meanMetric=mean(metric(targets[outputIndex],outputs[outputIndex]));valOutputs.push(meanMetric)}return valOutputs})}async fit(x,y,args={}){return fitTensors(this,x,y,args)}async fitDataset(dataset,args){return fitDataset(this,dataset,args)}async trainOnBatch(x,y){let standardizeOut=await this.standardizeUserData(x,y),inputs=standardizeOut[0],targets=standardizeOut[1],losses4=this.makeTrainFunction()(inputs.concat(targets)),lossValues=[];for(let loss of losses4){let v=await loss.data();lossValues.push(v[0])}return dispose(losses4),singletonOrArray(lossValues)}getNamedWeights(config){let namedWeights=[],trainableOnly=config!=null&&config.trainableOnly,weights=trainableOnly?this.trainableWeights:this.weights,weightValues=this.getWeights(trainableOnly);for(let i=0;i<weights.length;++i)trainableOnly&&!weights[i].trainable||namedWeights.push({name:weights[i].originalName,tensor:weightValues[i]});return namedWeights}set stopTraining(stop){this.stopTraining_=stop}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(optimizer){this.optimizer_!==optimizer&&(this.optimizer_=optimizer,this.isOptimizerOwned=!1)}dispose(){let result=super.dispose();if(result.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let numTensorsBeforeOptmizerDisposal=memory().numTensors;this.optimizer_.dispose(),result.numDisposedVariables+=numTensorsBeforeOptmizerDisposal-memory().numTensors}return result}getLossIdentifiers(){let lossNames;if(typeof this.loss=="string")lossNames=toSnakeCase(this.loss);else if(Array.isArray(this.loss)){for(let loss of this.loss)if(typeof loss!="string")throw new Error("Serialization of non-string loss is not supported.");lossNames=this.loss.map(name=>toSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version2}`,convertedBy:null};if((config==null?!1:config.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config instanceof Array){if(config[0].className==null||config[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config}else util_exports.assert(config.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config.layers,delete config.layers,extraModelConfig=config;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config){return new Sequential(config)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config){return Input(config)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu2(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config={};return config.className="linear",config.config={},deserializeActivation(config)}if(typeof identifier=="string"){let config={};return config.className=identifier,config.config={},deserializeActivation(config)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square2(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config){return new cls({l1:config.l1,l2:config.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i<inputShape.length;++i)axes[i]=inputShape[i];this.inputSpec=[new InputSpec({ndim:inputShape.length,axes})],this.built=!0}call(inputs,kwargs){return inputs=getExactlyOneTensor(inputs),prelu(inputs,this.alpha.read())}getConfig(){let config={alphaInitializer:serializeInitializer(this.alphaInitializer),alphaRegularizer:serializeRegularizer(this.alphaRegularizer),alphaConstraint:serializeConstraint(this.alphaConstraint),sharedAxes:this.sharedAxes},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};PReLU.className="PReLU";serialization_exports.registerClass(PReLU);var ELU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA=1,args==null&&(args={}),args.alpha!=null&&args.alpha!==this.DEFAULT_ALPHA)throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return elu(x)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ELU.className="ELU";serialization_exports.registerClass(ELU);var ThresholdedReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_THETA=1,args==null&&(args={}),this.theta=args.theta==null?this.DEFAULT_THETA:args.theta}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return x.mul(cast2(x.greater(this.theta),"float32"))}computeOutputShape(inputShape){return inputShape}getConfig(){let config={theta:this.theta},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ThresholdedReLU.className="ThresholdedReLU";serialization_exports.registerClass(ThresholdedReLU);var Softmax3=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_AXIS=1,args==null&&(args={}),this.softmax=new Softmax2().apply,this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return this.softmax(x,this.axis)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Softmax3.className="Softmax";serialization_exports.registerClass(Softmax3);function normalizeArray(value,n,name){if(typeof value=="number")return pyListRepeat(value,n);if(value.length!==n)throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);for(let i=0;i<n;++i){let singleValue=value[i];if(!isInteger(singleValue))throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`)}return value}function convOutputLength(inputLength,filterSize,padding,stride,dilation=1){if(inputLength==null)return inputLength;let dilatedFilterSize=filterSize+(filterSize-1)*(dilation-1),outputLength;return padding==="same"?outputLength=inputLength:outputLength=inputLength-dilatedFilterSize+1,Math.floor((outputLength+stride-1)/stride)}function deconvLength(dimSize,strideSize,kernelSize,padding){if(dimSize==null)return null;if(padding==="valid")dimSize=dimSize*strideSize+max2([kernelSize-strideSize,0]);else if(padding==="same")dimSize=dimSize*strideSize;else throw new ValueError(`Unsupport padding mode: ${padding}.`);return dimSize}function preprocessConv2DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i<space.length;++i){let newDim=convOutputLength(space[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);newSpace.push(newDim)}let outputShape=[inputShape[0]];return this.dataFormat==="channelsLast"?(outputShape=outputShape.concat(newSpace),outputShape.push(this.filters)):(outputShape.push(this.filters),outputShape=outputShape.concat(newSpace)),outputShape}getConfig(){let config={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static verifyArgs(args){if(!("filters"in args)||typeof args.filters!="number"||args.filters<1)throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config=super.getConfig();return delete config.dilationRate,config}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config){super(rank,config);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config.kernelInitializer!=null||config.kernelRegularizer!=null||config.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config.padding!=null&&config.padding!=="same"&&config.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);this.depthMultiplier=config.depthMultiplier==null?1:config.depthMultiplier,this.depthwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<this.rank+2)throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(inputShape)}`);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=this.kernelSize.concat([inputDim,this.depthMultiplier]),pointwiseKernelShape=[];for(let i=0;i<this.rank;++i)pointwiseKernelShape.push(1);pointwiseKernelShape.push(inputDim*this.depthMultiplier,this.filters);let trainable=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,trainable,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",pointwiseKernelShape,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,trainable,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,trainable,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config=super.getConfig();return delete config.rank,delete config.kernelInitializer,delete config.kernelRegularizer,delete config.kernelConstraint,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config=super.getConfig();return delete config.rank,delete config.dataFormat,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d3(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d3(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config=super.getConfig();return config.depthMultiplier=this.depthMultiplier,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range2(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;t<timeSteps;++t){let currentInput=perStepInputs[t],stepOutputs=tidy(()=>stepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range2(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;i<numStates;++i)output.push(null);return output}else return this.states_}set states(s){this.states_=s}build(inputShape){let constantShape=null;if(this.numConstants!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let batchSize=this.stateful?inputShape[0]:null,inputDim=inputShape.slice(2);this.inputSpec[0]=new InputSpec({shape:[batchSize,null,...inputDim]});let stepInputShape=[inputShape[0]].concat(inputShape.slice(2));if(constantShape!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");this.cell.build(stepInputShape);let stateSize;if(Array.isArray(this.cell.stateSize)?stateSize=this.cell.stateSize:stateSize=[this.cell.stateSize],this.stateSpec!=null){if(!util_exports.arraysEqual(this.stateSpec.map(spec=>spec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],dim=Array.isArray(this.cell.stateSize)?this.cell.stateSize[index]:this.cell.stateSize,expectedShape=[batchSize,dim];if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims2(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile2(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile2(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cellConfig=config.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return new cls(config)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let matrixX=dot2(inputs,this.kernel.read());this.useBias&&(matrixX=biasAdd(matrixX,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0]));let recurrentKernelValue=this.recurrentKernel.read(),[rk1,rk2]=split(recurrentKernelValue,[2*this.units,this.units],recurrentKernelValue.rank-1),matrixInner=dot2(hTMinus1,rk1),[xZ,xR,xH]=split(matrixX,3,matrixX.rank-1),[recurrentZ,recurrentR]=split(matrixInner,2,matrixInner.rank-1);z=this.recurrentActivation.apply(add2(xZ,recurrentZ)),r=this.recurrentActivation.apply(add2(xR,recurrentR));let recurrentH=dot2(mul(r,hTMinus1),rk2);hh=this.activation.apply(add2(xH,recurrentH));let h=add2(mul(z,hTMinus1),mul(add2(1,neg(z)),hh));return[h,h]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},baseConfig,config)}};GRUCell.className="GRUCell";serialization_exports.registerClass(GRUCell);var GRU=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new GRUCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let z=dot2(inputs,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0])),z=add2(z,dot2(hTMinus1,this.recurrentKernel.read())),this.useBias&&(z=biasAdd(z,this.bias.read()));let[z0,z1,z2,z3]=split(z,4,z.rank-1);i=this.recurrentActivation.apply(z0),f=this.recurrentActivation.apply(z1),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(z2))),o=this.recurrentActivation.apply(z3);let h=mul(o,this.activation.apply(c));return[h,h,c]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},baseConfig,config)}};LSTMCell.className="LSTMCell";serialization_exports.registerClass(LSTMCell);var LSTM=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new LSTMCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i<this.cells.length;++i){let cell=this.cells[i];states=nestedStates[i],i===0?callInputs=[inputs[0]].concat(states):callInputs=[callInputs[0]].concat(states),callInputs=cell.call(callInputs,kwargs),newNestedStates.push(callInputs.slice(1))}states=[];for(let cellStates of newNestedStates.slice().reverse())states.push(...cellStates);return[callInputs[0]].concat(states)})}build(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let outputDim;this.cells.forEach((cell,i)=>{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cells=[];for(let cellConfig of config.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;i<cell.weights.length;++i)tuples.push([cell.weights[i],inputWeights[i]])}batchSetValue(tuples)}};StackedRNNCells.className="StackedRNNCells";serialization_exports.registerClass(StackedRNNCells);function generateDropoutMask(args){let{ones:ones4,rate,training=!1,count:count2=1}=args,droppedInputs=()=>dropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i<p2.length;i++)e.indexOf(p2[i])<0&&Object.prototype.propertyIsEnumerable.call(s,p2[i])&&(t[p2[i]]=s[p2[i]]);return t},ConvRNN2D=class extends RNN{constructor(args){if(args.unroll)throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(args.cell))throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(args);this.inputSpec=[new InputSpec({ndim:5})]}call(inputs,kwargs){return tidy(()=>{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],expectedShape=stateShape;if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config){return new cls(config)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i<this.noiseShape.length;++i)noiseShape.push(this.noiseShape[i]==null?inputShape[i]:this.noiseShape[i]);return noiseShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0<this.rate&&this.rate<1){let training=kwargs.training==null?!1:kwargs.training,noiseShape=this.getNoiseShape(input2);return inTrainPhase(()=>dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i<input2.rank;++i)permutation.push(i);permutation.push(1),input2=input2.transpose(permutation)}return batchFlatten(input2)})}getConfig(){let config={};this.dataFormat!=null&&(config.dataFormat=this.dataFormat);let baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Flatten.className="Flatten";serialization_exports.registerClass(Flatten);var Activation2=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.activation=getActivation(args.activation)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config={n:this.n},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i<this.targetShape.length;++i)this.isUnknown(this.targetShape[i])&&(this.targetShape[i]=null)}isUnknown(dim){return dim<0||dim==null}fixUnknownDimension(inputShape,outputShape){let errorMsg="Total size of new array must be unchanged.",finalShape=outputShape.slice(),known=1,unknown=null;for(let i=0;i<finalShape.length;++i){let dim=finalShape[i];if(this.isUnknown(dim))if(unknown===null)unknown=i;else throw new ValueError("Can only specifiy one unknown dimension.");else known*=dim}let originalSize=arrayProd(inputShape);if(unknown!==null){if(known===0||originalSize%known!==0)throw new ValueError(errorMsg);finalShape[unknown]=originalSize/known}else if(originalSize!==known)throw new ValueError(errorMsg);return finalShape}computeOutputShape(inputShape){let anyUnknownDims=!1;for(let i=0;i<inputShape.length;++i)if(this.isUnknown(inputShape[i])){anyUnknownDims=!0;break}return anyUnknownDims?inputShape.slice(0,1).concat(this.targetShape):inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape))}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range2(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={maskValue:this.maskValue};return Object.assign(config,baseConfig),config}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k<inLens.length;++k){let s1=inLens[k],s2=inputShape[k+1];if(s1!=null&&s2!=null&&s1!==s2)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);s1==null&&(inLens[i]=s2),i++}}return[inputShape[0],...inLens,this.outputDim]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast2(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length<shape2.length)return this.computeElementwiseOpOutputShape(shape2,shape1);if(shape2.length===0)return shape1;let outputShape=shape1.slice(0,shape1.length-shape2.length);for(let k=0;k<shape2.length;++k){let i=shape1[shape1.length-shape2.length+k],j=shape2[k];if(i==null||j==null||i<0||j<0)outputShape.push(null);else if(i===1)outputShape.push(j);else if(j===1)outputShape.push(i);else{if(i!==j)throw new ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(shape1)+" "+JSON.stringify(shape2));outputShape.push(i)}}return outputShape}build(inputShape){if(Array.isArray(inputShape)&&!Array.isArray(inputShape[0])&&(inputShape=[getExactlyOneShape(inputShape)]),inputShape=inputShape,inputShape.length<2)throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);if(batchSizes=unique2(batchSizes),batchSizes.length>1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let allRanks=inputShape.map(shape=>shape.length);inputShape.indexOf(null)===-1&&unique2(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max2(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k<maxNDim-xNDim;++k)x=expandDims2(x,1);reshapedInputs.push(x)}return this.mergeFunction(reshapedInputs)}else{let transposed=!1;for(let x of inputs){let xNDim=x.rank;if(xNDim==null){let xShape=x.shape,batchSize=xShape[0],newShape=xShape.slice(1).concat([batchSize]),xTransposed=x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));xTransposed=transpose(xTransposed,[1,0]),xTransposed=xTransposed.reshape(newShape),reshapedInputs.push(xTransposed),transposed=!0}else if(xNDim>1){let dims=range2(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range2(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);return batchSizes=unique2(batchSizes),batchSizes.length===1?outputShape=batchSizes.concat(outputShape):outputShape=[null].concat(outputShape),outputShape}computeMask(inputs,mask){return tidy(()=>{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i<mask.length-1;++i)output=logicalAnd(output,mask[i]);return output})}},Add2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return output})}};Add2.className="Add";serialization_exports.registerClass(Add2);var Multiply2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=mul(output,inputs[i]);return output})}};Multiply2.className="Multiply";serialization_exports.registerClass(Multiply2);var Average=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return mul(1/inputs.length,output)})}};Average.className="Average";serialization_exports.registerClass(Average);var Maximum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=maximum(output,inputs[i]);return output})}};Maximum2.className="Maximum";serialization_exports.registerClass(Maximum2);var Minimum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=minimum(output,inputs[i]);return output})}};Minimum2.className="Minimum";serialization_exports.registerClass(Minimum2);var Concatenate=class extends Merge{constructor(args){super(args);this.DEFAULT_AXIS=-1,args==null&&(args={}),this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0]))||inputShape.length===1)throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");inputShape=inputShape;let allNoneShape=!0;for(let shape of inputShape)if(shape!=null){allNoneShape=!1;break}if(!allNoneShape){let shapeSet=[];for(let i=0;i<inputShape.length;++i){let shapeWithoutConcatAxis=inputShape[i].slice();shapeWithoutConcatAxis.splice(this.axis,1);let exists=!1;for(let shape of shapeSet)if(util_exports.arraysEqual(shape,shapeWithoutConcatAxis)){exists=!0;break}exists||shapeSet.push(shapeWithoutConcatAxis)}if(shapeSet.length>1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i<inputs.length;++i)mask[i]==null?outputMasks.push(onesLike(inputs[i]).asType("bool")):mask[i].rank<inputs[i].rank?outputMasks.push(expandDims(mask[i],-1)):outputMasks.push(mask[i]);let concatenatedMasks=concat(outputMasks,this.axis);return all(concatenatedMasks,-1,!1)})}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Concatenate.className="Concatenate";serialization_exports.registerClass(Concatenate);function interpretAxis(axis,dim){for(;axis<0;)axis+=dim;return axis}function batchDot(x,y,axes){if(x.shape.length>3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);y=y.reshape(y.shape.concat(diffShape))}else if(yNDim>xNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);x=x.reshape(x.shape.concat(diffShape))}else diff=0;let out;if(x.shape.length===2&&y.shape.length===2)axesArray[0]===axesArray[1]?out=x.mul(y).sum(axesArray[0]):out=x.transpose([1,0]).mul(y).sum(axesArray[1]);else{let adjX=axesArray[0]!==x.shape.length-1,adjY=axesArray[1]===y.shape.length-1;out=x.matMul(y,adjX,adjY)}if(diff>0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i<idx+diff;++i)squeezeAxes.push(i);out=out.squeeze(squeezeAxes)}return out.shape.length===1&&(out=out.expandDims(1)),out})}var Dot=class extends Merge{constructor(args){super(args);this.axes=args.axes,this.normalize=args.normalize==null?!1:args.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={stddev:this.stddev};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale2=1.0507009873554805,alphaP=-alpha*scale2,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast2(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon3=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon3);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon3),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range2(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon3),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range2(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range2(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range2(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=nDims);for(let axis of this.axis)if(axis<0||axis>=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique2(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale2=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i<nDims;++i)this.axis.indexOf(i)!==-1?(momentsTiling.push(inputShape[i]),scaleOffsetTiling.push(1)):(momentsTiling.push(1),scaleOffsetTiling.push(inputShape[i]));return mean4=mean4.tile(momentsTiling),variance=variance.tile(momentsTiling),scale2=scale2.tile(scaleOffsetTiling),offset=offset.tile(scaleOffsetTiling),batchNormalization(input2,mean4,variance,offset,scale2,this.epsilon)})}getConfig(){let config={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LayerNormalization.className="LayerNormalization";serialization_exports.registerClass(LayerNormalization);function spatial2dPadding(x,padding,dataFormat){return tidy(()=>{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims2(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config,customObjects={}){let layerConfig=config.layer,layer=deserialize(layerConfig,customObjects);delete config.layer;let newConfig={layer};return Object.assign(newConfig,config),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static fromConfig(cls,config){let rnnLayer=deserialize(config.layer);if(delete config.layer,config.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu3(args){return new ELU(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu2(args){return new PReLU(args)}function softmax2(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d3(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d4(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape2(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add3(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum2(args){return new Maximum2(args)}function minimum2(args){return new Minimum2(args)}function multiply(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config){return new L1L2(config)}function l12(config){return l1(config)}function l22(config){return l2(config)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less2(currVal,prevVal){return currVal<prevVal}function greater2(currVal,prevVal){return currVal>prevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less2:this.mode==="max"?this.monitorFunc=greater2:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater2:this.monitorFunc=less2,this.monitorFunc===less2&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less2?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping};var DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node,tensorMap,context,resourceManager){let inputParam=node.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node.inputNames.slice(start)[0],tensorMap,context,resourceManager),data=tensor2.dataSync();return inputParam.type==="number"?data[0]:util_exports.toNestedArray(tensor2.shape,data)}let attrParam=node.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node,tensorMap,context){let pad3=getParamValue("pad",node,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export(basic_math_exports,{json:()=>json2});var json2=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],control_exports={};__export(control_exports,{json:()=>json3});var json3=[{tfOpName:"EmptyTensorList",category:"control",inputs:[{start:0,name:"elementShape",type:"shape"},{start:1,name:"maxNumElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"LoopCond",category:"control",inputs:[{start:0,name:"pred",type:"tensor"}]},{tfOpName:"Switch",category:"control",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"pred",type:"tensor"}]},{tfOpName:"Merge",category:"control",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"Enter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"frame_name",name:"frameName",type:"string"},{tfName:"is_constant",name:"isConstant",type:"bool"}]},{tfOpName:"Exit",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NextIteration",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayV3",category:"control",inputs:[{start:0,name:"size",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"},{tfName:"dynamic_size",name:"dynamicSize",type:"bool"},{tfName:"clear_after_read",name:"clearAfterRead",type:"bool"},{tfName:"identical_element_shapes",name:"identicalElementShapes",type:"bool"},{tfName:"tensor_array_name",name:"name",type:"string"}]},{tfOpName:"TensorArrayWriteV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayReadV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayGatherV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"}]},{tfOpName:"TensorArrayScatterV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArrayConcatV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape_except0",name:"elementShapeExcept0",type:"shape",notSupported:!0}]},{tfOpName:"TensorArraySplitV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"tensor",type:"tensor"},{start:2,name:"lengths",type:"number[]"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArraySizeV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}]},{tfOpName:"TensorArrayCloseV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"}]},{tfOpName:"StatelessIf",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"If",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"StatelessWhile",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"While",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"TensorListScatter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListScatterV2",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"},{start:3,name:"numElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListGather",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfN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json4=[{tfOpName:"AvgPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[],notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPoolWithArgmax",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"include_batch_in_index",name:"includeBatchInIndex",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AvgPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Conv1D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"stride",name:"stride",type:"number"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NWC"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"dilation",name:"dilation",type:"number",defaultValue:1}]},{tfOpName:"Conv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"useCudnnOnGpu",name:"useCudnnOnGpu",type:"bool"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"_FusedConv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"use_cudnn_on_gpu",name:"useCudnnOnGpu",type:"bool",defaultValue:!0},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"leakyrelu_alpha",name:"leakyreluAlpha",type:"number"}]},{tfOpName:"Conv2DBackpropInput",category:"convolution",inputs:[{start:2,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:0,name:"outputShape",type:"number[]"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]",notSupported:!0}]},{tfOpName:"DepthwiseConv2d",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"DepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"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json10=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"half_pixel_centers",name:"halfPixelCenters",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"half_pixel_centers",name:"halfPixelCenters",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}],logical_exports={};__export(logical_exports,{json:()=>json11});var 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json12=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],normalization_exports={};__export(normalization_exports,{json:()=>json13});var json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],reduction_exports={};__export(reduction_exports,{json:()=>json14});var json14=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],slice_join_exports={};__export(slice_join_exports,{json:()=>json15});var json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],spectral_exports={};__export(spectral_exports,{json:()=>json16});var json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],transformation_exports={};__export(transformation_exports,{json:()=>json17});var json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];var OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op.startsWith("Placeholder")?placeholders.push(map[node.name]):node.op==="Const"?weights.push(map[node.name]):(node.input==null||node.input.length===0)&&initNodes.push(map[node.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node=nodes[key];node.children.length===0&&outputs.push(node)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node!=null&&(node.signatureKey=outputNodeNameToKey[name],outputs.push(node))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node&&(node.signatureKey=inputNodeNameToKey[name],inputs.push(node))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node){let mapper=getRegisteredOp(node.op)||this.opMappers[node.op]||{};node.attr==null&&(node.attr={});let newNode={name:node.name,op:node.op,category:mapper.category,inputNames:(node.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op==="Const"&&weights.push(map[node.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node.signatureKey=arg.name,inputs.push(node),nodes[nodeName]=node}),Object.keys(nodes).forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node=nodes[nodeName];node!=null&&(node.defaultOutput=index,outputs.push(node))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){typeof value=="string"&&(value=DataType[value]);switch(value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&&param.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&&param.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&&param.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&&param.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&&param.list&&param.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node,tensorMap,context){this.node=node,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node.inputNames.map(name=>this.getInput(name)),node.rawAttrs!=null&&(this.attrs=Object.keys(node.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}};var executeOp=(node,tensorMap,context)=>{switch(node.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp2=(node,tensorMap,context)=>{switch(node.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node,tensorMap,context),getParamValue("imag",node,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node,tensorMap,context))];case"Log":return[log(getParamValue("x",node,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node,tensorMap,context))];case"Real":return[real(getParamValue("x",node,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node,tensorMap,context))];case"Round":return[round2(getParamValue("x",node,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node,tensorMap,context))];case"Square":return[square(getParamValue("x",node,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node,tensorMap,context),getParamValue("clipValueMin",node,tensorMap,context),getParamValue("clipValueMax",node,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node,tensorMap,context),getParamValue("axes",node,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==-1&&n2[i]!==-1&&n1[i]!==n2[i])return!1;return!0}var TensorArray=class{constructor(name,dtype,maxSize,elementShape,identicalElementShapes,dynamicSize,clearAfterRead){this.name=name,this.dtype=dtype,this.maxSize=maxSize,this.elementShape=elementShape,this.identicalElementShapes=identicalElementShapes,this.dynamicSize=dynamicSize,this.clearAfterRead=clearAfterRead,this.tensors=[],this.closed_=!1,this.idTensor=scalar(0),keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config.config);let returnObj=fromConfig(cls,config.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return a<b?-1:a>b?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique2(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now2=util_exports.now();return now2-lastTime<waitMs||(lastTime=now2,lastResult=f(...args)),lastResult}}function mapActivationToFusedKernel(activationName){return activationName==="relu"?"relu":activationName==="linear"?"linear":activationName==="elu"?"elu":null}function calcL2Norms(w,axis){return tidy(()=>sqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config){return new MinMaxNorm(config)}var exports_initializers_exports={};__export(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros2});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"];var nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i<end;++i)prod5*=array2[i];return prod5}function toArray1D(array2){return array2=Array.isArray(array2)?new Float32Array(array2):array2,tensor1d(array2)}function min2(array2){return min(toArray1D(array2)).dataSync()[0]}function max2(array2){return max(toArray1D(array2)).dataSync()[0]}function range2(begin,end){if(end<begin)throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);let out=[];for(let i=begin;i<end;++i)out.push(i);return out}function cast2(x,dtype){return x.asType(dtype)}function expandDims2(x,axis=-1){let outShape=x.shape.slice();return axis<0&&(axis=outShape.length+axis+1),outShape.splice(axis,0,1),x.reshape(outShape)}function repeat(x,n){return tidy(()=>{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims2(x,1);return tile2(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile2(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square2(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu2(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale2=this.scale;if(this.mode==="fanIn"?scale2/=Math.max(1,fanIn):this.mode==="fanOut"?scale2/=Math.max(1,fanOut):scale2/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale2);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale2);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config={};return config.className=className,config.config={},deserializeInitializer(config)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros2(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity(args){return new Identity2(args)}function varianceScaling(config){return new VarianceScaling(config)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add3,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d3,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d4,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu3,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum2,minimum:()=>minimum2,multiply:()=>multiply,permute:()=>permute,prelu:()=>prelu2,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape2,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax2,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndex<inputs.length;inputIndex++){let x=inputs[inputIndex],spec=inputSpec[inputIndex];if(spec==null)continue;let ndim=x.rank;if(spec.ndim!=null&&ndim!==spec.ndim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);if(spec.maxNDim!=null&&ndim>spec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim<spec.minNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);if(spec.dtype!=null&&x.dtype!==spec.dtype)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);if(spec.axes){let xShape=x.shape;for(let key in spec.axes){let axis=Number(key),value=spec.axes[key],xShapeAtAxis=axis>=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i<spec.shape.length;++i){let specDim=spec.shape[i],dim=x.shape[i];if(specDim!=null&&dim!=null&&specDim!==dim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`)}}}call(inputs,kwargs){return inputs}invokeCallHook(inputs,kwargs){this._callHook!=null&&this._callHook(inputs,kwargs)}setCallHook(callHook){this._callHook=callHook}clearCallHook(){this._callHook=null}apply(inputs,kwargs){kwargs=kwargs||{},this.assertNotDisposed();let inputsList=toList(inputs),allAreSymbolic=!0;for(let input2 of inputsList)if(!(input2 instanceof SymbolicTensor)){allAreSymbolic=!1;break}let noneAreSymbolic=!0;for(let input2 of inputsList)if(input2 instanceof SymbolicTensor){noneAreSymbolic=!1;break}if(allAreSymbolic===noneAreSymbolic)throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return nameScope(this.name,()=>{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node of this.inboundNodes){let shapeString=JSON.stringify(node.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;i<paramValues.length;++i){let pv=paramValues[i],p2=params[i],w=weights[i];if(!util_exports.arraysEqual(pv.shape,w.shape))throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);weightValueTuples.push([p2,w])}batchSetValue(weightValueTuples)})}addWeight(name,shape,dtype,initializer,regularizer,trainable,constraint){if(this._addedWeightNames.indexOf(name)!==-1)throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);this._addedWeightNames.push(name),dtype==null&&(dtype="float32"),this.fastWeightInitDuringBuild&&(initializer=getInitializer("zeros"));let initValue=initializer.apply(shape,dtype),weight=new LayerVariable(initValue,dtype,name,trainable,constraint);return initValue.dispose(),regularizer!=null&&this.addLoss(()=>regularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;i<outputTensors.length;i++)outputTensors[i].sourceLayer=this,outputTensors[i].nodeIndex=this.inboundNodes.length-1,outputTensors[i].tensorIndex=i}getConfig(){let config={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(config.batchInputShape=this.batchInputShape),this.dtype!=null&&(config.dtype=this.dtype),config}disposeWeights(){return this.weights.forEach(weight=>weight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount==0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node=layer.inboundNodes[nodeIndex];if(node.inboundLayers.length===0)return node.inputTensors;{let sourceTensors=[];for(let i=0;i<node.inboundLayers.length;i++){let x=node.inputTensors[i],layer2=node.inboundLayers[i],nodeIndex2=node.nodeIndices[i],previousSources=getSourceInputs(x,layer2,nodeIndex2);for(let x2 of previousSources)sourceTensors.indexOf(x2)===-1&&sourceTensors.push(x2)}return sourceTensors}}}var InputLayer=class extends Layer{constructor(args){super({dtype:args.dtype,name:args.name!=null?args.name:getUid("input").toString()});if(args.batchSize==null&&(args.batchSize=null),args.sparse==null&&(args.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=args.sparse,args.inputShape!=null&&args.batchInputShape!=null)throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let batchInputShape=args.batchInputShape;if(batchInputShape==null){if(args.inputShape==null)throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");batchInputShape=[args.batchSize].concat(args.inputShape)}else if(args.batchSize!=null)throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let dtype=args.dtype||"float32";this.batchInputShape=batchInputShape,this.dtype=dtype,this.inputSpec=[{shape:batchInputShape}];let inputTensor=new SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);inputTensor.nodeIndex=0,inputTensor.tensorIndex=0,new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[inputTensor],outputTensors:[inputTensor],inputMasks:[null],outputMasks:[null],inputShapes:[batchInputShape],outputShapes:[batchInputShape]})}apply(inputs,kwargs){throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};InputLayer.className="InputLayer";serialization_exports.registerClass(InputLayer);function Input(config){if(config.batchShape==null&&config.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(config.batchShape!=null&&config.shape!=null)throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let batchShape=config.batchShape;config.shape!=null&&batchShape==null&&(batchShape=[null].concat(config.shape));let dtype=config.dtype;return dtype==null&&(dtype="float32"),new InputLayer({batchInputShape:batchShape,name:config.name,dtype,sparse:config.sparse}).inboundNodes[0].outputTensors[0]}async function resolveScalarsInLogs(logs){if(logs==null)return;let promises=[],keys=[],scalarsToDispose=[];for(let key in logs){let value=logs[key];if(typeof value!="number"){let valueScalar=value;promises.push(valueScalar.data()),keys.push(key),scalarsToDispose.push(valueScalar)}}if(promises.length>0){let values=await Promise.all(promises);for(let i=0;i<values.length;++i)logs[keys[i]]=values[i][0];dispose(scalarsToDispose)}}function disposeTensorsInLogs(logs){if(logs!=null)for(let key in logs){let value=logs[key];typeof value!="number"&&value.dispose()}}var ModelLoggingVerbosity;(function(ModelLoggingVerbosity2){ModelLoggingVerbosity2[ModelLoggingVerbosity2.SILENT=0]="SILENT",ModelLoggingVerbosity2[ModelLoggingVerbosity2.VERBOSE=1]="VERBOSE"})(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));var DEFAULT_YIELD_EVERY_MS=125,BaseCallback=class{constructor(){this.validationData=null}setParams(params){this.params=params}async onEpochBegin(epoch,logs){}async onEpochEnd(epoch,logs){}async onBatchBegin(batch,logs){}async onBatchEnd(batch,logs){}async onTrainBegin(logs){}async onTrainEnd(logs){}setModel(model2){}},CallbackList=class{constructor(callbacks2,queueLength=10){callbacks2==null&&(callbacks2=[]),this.callbacks=callbacks2,this.queueLength=queueLength}append(callback){this.callbacks.push(callback)}setParams(params){for(let callback of this.callbacks)callback.setParams(params)}setModel(model2){for(let callback of this.callbacks)callback.setModel(model2)}async onEpochBegin(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochBegin(epoch,logs)}async onEpochEnd(epoch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onEpochEnd(epoch,logs)}async onBatchBegin(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchBegin(batch,logs)}async onBatchEnd(batch,logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onBatchEnd(batch,logs)}async onTrainBegin(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainBegin(logs)}async onTrainEnd(logs){logs==null&&(logs={});for(let callback of this.callbacks)await callback.onTrainEnd(logs)}},BaseLogger=class extends BaseCallback{constructor(){super()}async onEpochBegin(epoch){this.seen=0,this.totals={}}async onBatchEnd(batch,logs){logs==null&&(logs={});let batchSize=logs.size==null?0:logs.size;this.seen+=batchSize;for(let key in logs){let value=logs[key];if(typeof value=="number")this.totals.hasOwnProperty(key)||(this.totals[key]=0),this.totals[key]=this.totals[key]+value*batchSize;else{let oldTotalsToDispose;key in this.totals?oldTotalsToDispose=this.totals[key]:this.totals[key]=0;let total=tidy(()=>add2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log5=mul(div(1,this.seen),this.totals[key]);logs[key]=log5,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;i<valueArray.length;++i)if(typeof valueArray[i]!="number"){let valueScalar=valueArray[i];promises.push(valueScalar.data()),keys.push(key),indices.push(i)}}let values=await Promise.all(promises);for(let n=0;n<values.length;++n)this.history[keys[n]][indices[n]].dispose(),this.history[keys[n]][indices[n]]=values[n][0]}},CustomCallback=class extends BaseCallback{constructor(args,yieldEvery){super();if(this.currentEpoch=0,this.yieldEvery=yieldEvery||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=DEFAULT_YIELD_EVERY_MS),this.yieldEvery==="never"&&args.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");util_exports.isNumber(this.yieldEvery)&&(this.maybeWait=debounce(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=args.onTrainBegin,this.trainEnd=args.onTrainEnd,this.epochBegin=args.onEpochBegin,this.epochEnd=args.onEpochEnd,this.batchBegin=args.onBatchBegin,this.batchEnd=args.onBatchEnd,this.yield=args.onYield}async maybeWait(epoch,batch,logs){let ps=[];this.yield!=null&&(await resolveScalarsInLogs(logs),ps.push(this.yield(epoch,batch,logs))),ps.push(nextFrame()),await Promise.all(ps)}async onEpochBegin(epoch,logs){this.currentEpoch=epoch,this.epochBegin!=null&&(await resolveScalarsInLogs(logs),await this.epochBegin(epoch,logs))}async onEpochEnd(epoch,logs){let ps=[];this.epochEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.epochEnd(epoch,logs))),this.yieldEvery==="epoch"&&ps.push(nextFrame()),await Promise.all(ps)}async onBatchBegin(batch,logs){this.batchBegin!=null&&(await resolveScalarsInLogs(logs),await this.batchBegin(batch,logs))}async onBatchEnd(batch,logs){let ps=[];this.batchEnd!=null&&(await resolveScalarsInLogs(logs),ps.push(this.batchEnd(batch,logs))),this.yieldEvery==="batch"?ps.push(nextFrame()):util_exports.isNumber(this.yieldEvery)&&ps.push(this.maybeWait(this.currentEpoch,batch,logs)),await Promise.all(ps)}async onTrainBegin(logs){this.trainBegin!=null&&(await resolveScalarsInLogs(logs),await this.trainBegin(logs))}async onTrainEnd(logs){this.trainEnd!=null&&(await resolveScalarsInLogs(logs),await this.trainEnd(logs))}};function standardizeCallbacks(callbacks2,yieldEvery){return callbacks2==null&&(callbacks2={}),callbacks2 instanceof BaseCallback?[callbacks2]:Array.isArray(callbacks2)&&callbacks2[0]instanceof BaseCallback?callbacks2:toList(callbacks2).map(callbackConfig=>new CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square2(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square2(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon(),Number.MAX_VALUE),firstLog=log(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon(),Number.MAX_VALUE),secondLog=log(add2(1,clippedTrue));return mean(square2(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square2(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log22=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log22);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon(),1-epsilon()),neg(sum2(mul(target.toFloat(),log(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon(),1-epsilon());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon(),1-epsilon()),y=log(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon(),1),clippedPred=clipByValue(yPred,epsilon(),1);return sum2(mul(yTrue,log(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log(add2(epsilon(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast2(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast2(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon()),Adam:()=>train.adam(.001,.9,.999,epsilon()),Adamax:()=>train.adamax(.002,.9,.999,epsilon(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys)if(typeof key!="string"||!plainObjectCheck(x[key]))return!1;return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i<layers.length;++i)sequentialLike?printLayerSummary(layers[i],positions,printFn):printLayerSummaryWithConnections(layers[i],positions,relevantNodes,printFn),printFn((i===layers.length-1?"=":"_").repeat(lineLength));model2.checkTrainableWeightsConsistency();let trainableCount=countTrainableParams(model2),nonTrainableCount=countParamsInWeights(model2.nonTrainableWeights);printFn(`Total params: ${trainableCount+nonTrainableCount}`),printFn(`Trainable params: ${trainableCount}`),printFn(`Non-trainable params: ${nonTrainableCount}`),printFn("_".repeat(lineLength))}function countTrainableParams(model2){let trainableCount;return model2.collectedTrainableWeights!=null?trainableCount=countParamsInWeights(model2.collectedTrainableWeights):trainableCount=countParamsInWeights(model2.trainableWeights),trainableCount}function isModelSequentialLike(model2){let sequentialLike=!0,nodesByDepth=[],nodes=[];for(let depth in model2.nodesByDepth)nodesByDepth.push(model2.nodesByDepth[depth]);for(let depthNodes of nodesByDepth){if(depthNodes.length>1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node of layer.inboundNodes)if(nodes.indexOf(node)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i<fields.length;++i)i>0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node)===-1))for(let i=0;i<node.inboundLayers.length;++i){let inboundLayer=node.inboundLayers[i].name,inboundLayerIndex=node.nodeIndices[i],inboundTensorIndex=node.tensorIndices[i];connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`)}let name=layer.name,className=layer.getClassName(),firstConnection=connections.length===0?"":connections[0],fields=[`${name} (${className})`,outputShape,layer.countParams().toString(),firstConnection];printRow(fields,positions,printFn);for(let i=1;i<connections.length;++i)printRow(["","","",connections[i]],positions,printFn)}function isArrayItemInputOrOutputName(key,index,value){return(key==="inboundNodes"||key==="outputLayers"||key==="inputLayers")&&index===0&&typeof value=="string"}function convertPythonicToTs(pythonicConfig,key){if(pythonicConfig===null)return null;if(typeof pythonicConfig=="string")return toCamelCase(pythonicConfig);if(typeof pythonicConfig=="number"||typeof pythonicConfig=="boolean")return pythonicConfig;if(pythonicConfig instanceof Array){let tsArray=[],arrayLength=pythonicConfig.length;for(let i=0;i<arrayLength;++i){let item=pythonicConfig[i];isArrayItemInputOrOutputName(key,i,item)?tsArray.push(item):tsArray.push(convertPythonicToTs(item,key))}return tsArray}else{let tsDict={};for(let pythonicKey of Object.keys(pythonicConfig)){let pythonicValue=pythonicConfig[pythonicKey];if(pythonicKey==="name"&&typeof pythonicValue=="string")tsDict[pythonicKey]=pythonicValue;else{let tsKey=toCamelCase(pythonicKey);tsDict[tsKey]=convertPythonicToTs(pythonicValue,tsKey)}}return tsDict}}function convertTsToPythonic(tsConfig,key){if(tsConfig==null)return null;if(typeof tsConfig=="string")return toSnakeCase(tsConfig);if(typeof tsConfig=="number"||typeof tsConfig=="boolean")return tsConfig;if(tsConfig instanceof Array){let pyArray=[],arrayLength=tsConfig.length;for(let i=0;i<arrayLength;++i){let item=tsConfig[i];isArrayItemInputOrOutputName(key,i,item)?pyArray.push(item):pyArray.push(convertTsToPythonic(item,key))}return pyArray}else{let pyDict={};for(let tsKey of Object.keys(tsConfig)){let tsValue=tsConfig[tsKey],pyKey=toSnakeCase(tsKey);(tsKey==="name"||tsKey==="className")&&typeof tsValue=="string"?pyDict[pyKey]=tsValue:pyDict[pyKey]=convertTsToPythonic(tsValue,tsKey)}return pyDict}}var version2="2.8.3";function assertFeedCompatibility(key,val){if(key.dtype==null||key.dtype===val.dtype)return val;try{return cast(val,key.dtype)}catch(err){throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`)}}var FeedDict=class{constructor(feeds){if(this.id2Value={},this.id2Mask={},this.name2Id={},feeds instanceof FeedDict)for(let id in feeds.id2Value)this.id2Value[id]=feeds.id2Value[id],id in feeds.id2Mask&&(this.id2Mask[id]=feeds.id2Mask[id]);else{if(feeds==null)return;for(let feed of feeds)this.add(feed.key,feed.value)}}add(key,value,mask){if(this.id2Value[key.id]==null)this.id2Value[key.id]=assertFeedCompatibility(key,value),this.name2Id[key.name]=key.id,mask!=null&&(this.id2Mask[key.id]=mask);else throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);return this}addFeed(feed){this.add(feed.key,feed.value)}hasKey(key){return this.id2Value[key.id]!=null}names(){return Object.keys(this.name2Id)}getValue(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Value[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Value[id]}}getMask(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Mask[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Mask[id]}}disposeMasks(){this.id2Mask!=null&&dispose(this.id2Mask)}},cachedSorted={},cachedRecipientCounts={};function execute(fetches,feedDict,kwargs,probe){let training=kwargs==null?!1:kwargs.training,arrayFetches=Array.isArray(fetches),fetchArray=arrayFetches?fetches:[fetches],outputNames=fetchArray.map(t=>t.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;i<sorted.length;++i){if(probe!=null){let numTensors=memory().numTensors;numTensors>probe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors<probe.minNumTensors&&(probe.minNumTensors=numTensors)}let symbolic=sorted[i],srcLayer=symbolic.sourceLayer;if(srcLayer instanceof InputLayer)continue;let inputValues=[],inputMasks=[],tensorsToDispose=[],maskExists=!1;for(let input2 of symbolic.inputs){let value=internalFeedDict.getValue(input2),mask=internalFeedDict.getMask(input2);inputValues.push(value),inputMasks.push(mask),mask!=null&&(maskExists=!0),training||(recipientCounts[input2.name]--,recipientCounts[input2.name]===0&&!feedDict.hasKey(input2)&&outputNames.indexOf(input2.name)===-1&&!value.isDisposed&&input2.sourceLayer.stateful!==!0&&tensorsToDispose.push(value))}maskExists&&(kwargs=kwargs||{},kwargs.mask=inputMasks[0]);let outputTensors=toList(srcLayer.apply(inputValues,kwargs)),outputMask=null;srcLayer.supportsMasking&&(outputMask=srcLayer.computeMask(inputValues,inputMasks));let layerOutputs=getNodeOutputs(symbolic),outputSymbolicTensors=Array.isArray(layerOutputs)?layerOutputs:[layerOutputs];for(let i2=0;i2<outputSymbolicTensors.length;++i2){internalFeedDict.hasKey(outputSymbolicTensors[i2])||internalFeedDict.add(outputSymbolicTensors[i2],outputTensors[i2],Array.isArray(outputMask)?outputMask[0]:outputMask);let index=outputNames.indexOf(outputSymbolicTensors[i2].name);index!==-1&&(finalOutputs[index]=outputTensors[i2])}training||dispose(tensorsToDispose)}return internalFeedDict.disposeMasks(),arrayFetches?finalOutputs:finalOutputs[0]}function getTopologicalSortAndRecipientCounts(fetches,feedDict){util_exports.assert(fetches!=null&&fetches.length>0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name)&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;i<fetch3.sourceLayer.inboundNodes.length;++i)for(let outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors)if(outputTensor.id===fetch3.id){nodeIndex=i;break}layerOutputs=fetch3.sourceLayer.getOutputAt(nodeIndex)}return layerOutputs}var Container=class extends Layer{constructor(args){super({});if(this.containerNodes=new Set,this.name=args.name,this.name==null){let prefix=this.getClassName().toLowerCase();this.name=getUid(prefix)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(args.inputs)?this.inputs=args.inputs.slice():this.inputs=[args.inputs],Array.isArray(args.outputs)?this.outputs=args.outputs.slice():this.outputs=[args.outputs],unique2(this.inputs).length!==this.inputs.length)throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(x=>x.name)}`);unique2(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i];if(!(layer instanceof InputLayer))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);this.inputNames.push(layer.name),this.feedInputShapes.push(layer.batchInputShape),this.feedInputNames.push(layer.name)}for(let layer of this.outputLayers)this.outputNames.push(layer.name);this.internalInputShapes=this.inputs.map(x=>x.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node)===-1&&nodesInProgress2.push(node);let numInboundLayers=node.inboundLayers.length;for(let i=0;i<numInboundLayers;i++){let x=node.inputTensors[i],layer2=node.inboundLayers[i],nodeIndex2=node.nodeIndices[i],tensorIndex2=node.tensorIndices[i];buildMapOfGraph(x,finishedNodes2,nodesInProgress2,layer2,nodeIndex2,tensorIndex2)}for(finishedNodes2.push(node);nodesInProgress2.indexOf(node)>=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node),1);nodesInDecreasingDepth.push(node)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node of reversedNodesInDecreasingDepth){nodeIDToNode[node.id]=node,node.id in nodesDepths||(nodesDepths[node.id]=0);let depth=nodesDepths[node.id],previousDepth=layersDepths[node.outboundLayer.id]==null?0:layersDepths[node.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node.outboundLayer.id]=depth,layerIDToLayer[node.outboundLayer.id]=node.outboundLayer,nodesDepths[node.id]=depth;for(let i=0;i<node.inboundLayers.length;i++){let inboundLayer=node.inboundLayers[i],nodeIndex=node.nodeIndices[i],inboundNode=inboundLayer.inboundNodes[nodeIndex],previousDepth2=nodesDepths[inboundNode.id]==null?0:nodesDepths[inboundNode.id];nodesDepths[inboundNode.id]=Math.max(depth+1,previousDepth2),nodeIDToNode[inboundNode.id]=inboundNode}}let nodesByDepth={};for(let nodeID in nodesDepths){let depth=nodesDepths[nodeID];depth in nodesByDepth||(nodesByDepth[depth]=[]),nodesByDepth[depth].push(nodeIDToNode[nodeID])}let layersByDepth={};for(let layerID in layersDepths){let depth=layersDepths[layerID];depth in layersByDepth||(layersByDepth[depth]=[]),layersByDepth[depth].push(layerIDToLayer[layerID])}let depthKeys=Object.keys(layersByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndex<bIndex?-1:aIndex>bIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node of nodesByDepth[depth]){let layer=node.outboundLayer;if(layer!=null){for(let x of node.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version2}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i]);return execute(this.outputs,feedDict,kwargs)})}computeMask(inputs,mask){return tidy(()=>{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;i<inputShapes.length;i++){let layer=this.inputLayers[i],inputShape2=inputShapes[i],shapeKey=layer.name+"_0_0";layersToOutputShapes[shapeKey]=inputShape2}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;j<node.inboundLayers.length;j++){let inboundLayer=node.inboundLayers[j],nodeIndex2=node.nodeIndices[j],tensorIndex=node.tensorIndices[j],shapeKey=`${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`,inputShape2=layersToOutputShapes[shapeKey];inputShapes2.push(inputShape2)}let outputShape=layer.computeOutputShape(singletonOrArray(inputShapes2)),outputShapes2=normalizeShapeList(outputShape),nodeIndex=layer.inboundNodes.indexOf(node);for(let j=0;j<outputShapes2.length;j++){let shapeKey=`${layer.name}_${nodeIndex}_${j}`;layersToOutputShapes[shapeKey]=outputShapes2[j]}}}let outputShapes=[],outputShapeKeys=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],tensorIndex=this.outputLayersTensorIndices[i],shapeKey=`${layer.name}_${nodeIndex}_${tensorIndex}`;outputShapeKeys.push(shapeKey)}for(let i=0;i<outputShapeKeys.length;i++){let key=outputShapeKeys[i];assert2(key in layersToOutputShapes),outputShapes.push(layersToOutputShapes[key])}return singletonOrArray(outputShapes)}runInternalGraph(inputs,masks){masks==null&&(masks=pyListRepeat(null,inputs.length));let tensorMap={};for(let i=0;i<this.inputs.length;++i){let x=this.inputs[i],y=inputs[i],mask=masks[i];tensorMap[x.id]=[y,mask]}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer,referenceInputTensors=node.inputTensors,referenceOutputTensors=node.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node.callArgs!=null&&(kwargs=node.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i<referenceOutputTensors.length;++i){let x=referenceOutputTensors[i],y=outputTensors2[i],mask=outputMasks2[i];tensorMap[x.id]=[y,mask]}}}}let outputTensors=[],outputMasks=[],outputShapes=[];for(let x of this.outputs){assert2(x.id in tensorMap,`Could not compute output ${x.name} : ${x.id}`);let[tensor2,mask]=tensorMap[x.id];outputShapes.push(tensor2.shape),outputTensors.push(tensor2),outputMasks.push(mask)}return[outputTensors,outputMasks,outputShapes]}buildNodeConversionMap(layers){let nodeConversionMap={},keptNodes;for(let layer of this.layers){keptNodes=layer instanceof Container?1:0;for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let nodeKey=Container.nodeKey(layer,originalNodeIndex);this.containerNodes.has(nodeKey)&&(nodeConversionMap[nodeKey]=keptNodes,keptNodes+=1)}}return nodeConversionMap}getLayer(name,index){if(index!=null){if(this.layers.length<=index)throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);return this.layers[index]}else if(name==null)throw new ValueError("Provide either a layer name or layer index");for(let layer of this.layers)if(layer.name===name)return layer;throw new ValueError(`No such layer: ${name}`)}calculateLosses(){return tidy(()=>{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex<layer.inboundNodes.length;++nodeIndex){let nodeKey=Container.nodeKey(layer,nodeIndex);this.containerNodes.has(nodeKey)&&losses4.push(...layer.calculateLosses())}return losses4})}getConfig(){let config={name:this.name},nodeConversionMap=this.buildNodeConversionMap(this.layers),layerConfigs=[];for(let layer of this.layers){let layerClassName=layer.getClassName(),layerConfig=layer.getConfig(),filteredInboundNodes=[];for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let node=layer.inboundNodes[originalNodeIndex],nodeKey=Container.nodeKey(layer,originalNodeIndex),kwargs={};if(this.containerNodes.has(nodeKey)){if(node.callArgs)try{JSON.stringify(node.callArgs),kwargs=node.callArgs}catch(err){console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),kwargs={}}if(node.inboundLayers.length>0){let nodeData=[];for(let i=0;i<node.inboundLayers.length;i++){let inboundLayer=node.inboundLayers[i],nodeIndex=node.nodeIndices[i],tensorIndex=node.tensorIndices[i],nodeKey2=Container.nodeKey(inboundLayer,nodeIndex),newNodeIndex=nodeConversionMap[nodeKey2];newNodeIndex==null&&(newNodeIndex=0),nodeData.push([inboundLayer.name,newNodeIndex,tensorIndex,kwargs])}filteredInboundNodes.push(nodeData)}}}let dict={};dict.name=layer.name,dict.className=layerClassName,dict.config=layerConfig,dict.inboundNodes=filteredInboundNodes,layerConfigs.push(dict)}config.layers=layerConfigs;let modelInputs=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i],nodeIndex=this.inputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.inputLayersTensorIndices[i];modelInputs.push([layer.name,newNodeIndex,tensorIndex])}config.inputLayers=modelInputs;let modelOutputs=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.outputLayersTensorIndices[i];modelOutputs.push([layer.name,newNodeIndex,tensorIndex])}return config.outputLayers=modelOutputs,config}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let createdLayers={},unprocessedNodes={};function addUnprocessedNode(layer,nodeData){layer.name in unprocessedNodes?unprocessedNodes[layer.name].push(nodeData):unprocessedNodes[layer.name]=[nodeData]}function processNode(layer,nodeData){let inputTensors2=[],kwargs;for(let inputData of nodeData){let inboundLayerName=inputData[0],inboundNodeIndex=inputData[1],inboundTensorIndex=inputData[2];if(kwargs=inputData[3]==null?{}:inputData[3],!(inboundLayerName in createdLayers)){addUnprocessedNode(layer,nodeData);return}let inboundLayer=createdLayers[inboundLayerName];if(inboundLayer.inboundNodes.length<=inboundNodeIndex){addUnprocessedNode(layer,nodeData);return}let inboundNode=inboundLayer.inboundNodes[inboundNodeIndex];inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex])}inputTensors2.length>0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config.customObjects!=null?config.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config.name,layersFromConfig=config.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex<flattenedXs.length;xIndex++)util_exports.assert(flattenedXs[xIndex].shape[0]===batchSize,()=>`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex<flattenedYs.length;yIndex++)util_exports.assert(flattenedYs[yIndex].shape[0]===batchSize,()=>`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data){if(data.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data[0],ys:data[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch<args.epochs;){let epochLogs={};await callbackList.onEpochBegin(epoch);let stepsDone=0,batchIndex=0;for(hasBatchesPerEpoch||(dataIterator=await dataset.iterator());hasBatchesPerEpoch?stepsDone<args.batchesPerEpoch:!0;){let iteratorOut=await dataIterator.next();if(hasBatchesPerEpoch&&iteratorOut.done){console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch*args.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(iteratorOut.value!=null){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),batchLogs={};batchLogs.batch=batchIndex,batchLogs.size=xs[0].shape[0],await callbackList.onBatchBegin(batchIndex,batchLogs);let sampleWeights=[];if(args.classWeight!=null){let standardClassWeights=standardizeClassWeights(args.classWeight,model2.outputNames);for(let i=0;i<standardClassWeights.length;++i)sampleWeights.push(await standardizeWeights(ys[i],null,standardClassWeights[i]))}let ins=xs.concat(ys).concat(sampleWeights),outs=trainFunction(ins);dispose(ins);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),batchIndex++,stepsDone++}if(hasBatchesPerEpoch?stepsDone>=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i<model2.metricsNames.length;++i)epochLogs[`val_${model2.metricsNames[i]}`]=valOuts[i]}break}if(model2.stopTraining_)break}if(await callbackList.onEpochEnd(epoch,epochLogs),epoch++,model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}finally{model2.isTraining=!1}}function getStepsPerEpoch(dataset,args){let stepsPerEpoch=null;return args.batchesPerEpoch!=null?stepsPerEpoch=args.batchesPerEpoch:Number.isFinite(dataset.size)&&(stepsPerEpoch=dataset.size),stepsPerEpoch}function isDatasetObject(dataset){return typeof dataset.iterator=="function"}function isLazyIteratorObject(iterator){return typeof iterator.next=="function"}async function evaluateDataset(model2,dataset,args){args=args||{};let hasBatches=args.batches!=null,f=model2.testFunction,outs=[];if(args.verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch<args.batches:!0;){let iteratorOut=await dataIterator.next();if(outs=tidy(()=>{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));let batchSize=xsAndYs[0].shape[0];for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i],oldScalar=outs[i];outs[i]=tidy(()=>add2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i<outs.length;++i){let oldScalar=outs[i];outs[i]=div(outs[i],numExamples),dispose(oldScalar)}return singletonOrArray(outs)}function checkBatchSize(batchSize){util_exports.assert(batchSize>0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart<size;)batchEnd=batchStart+batchSize,batchEnd>=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range2(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch<epochs;++epoch){await callbackList.onEpochBegin(epoch);let epochLogs={};if(stepsPerEpoch!=null)throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");{if(shuffle2==="batch")throw new NotImplementedError("batch shuffling is not implemneted yet");shuffle2&&util_exports.shuffle(indexArray);let epochIndexArray1D=tensor1d(indexArray),batches=makeBatches(numTrainSamples,batchSize);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchLogs={};if(await callbackList.onBatchBegin(batchIndex,batchLogs),tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}if(batchIndex===batches.length-1&&doValidation){let valOuts=model2.testLoop(valF,valIns,batchSize);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=valOuts[i];keep(out),epochLogs["val_"+label]=out}}}),await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),model2.stopTraining_)break}epochIndexArray1D.dispose()}if(await callbackList.onEpochEnd(epoch,epochLogs),model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}async function fitTensors(model2,x,y,args={}){if(model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;let inputs,targets,inputValX,inputValY,valX,valY,sampleWeights;try{let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!1,standardizedOuts=await model2.standardizeUserData(x,y,args.sampleWeight,args.classWeight,checkBatchAxis,batchSize);inputs=standardizedOuts[0],targets=standardizedOuts[1],sampleWeights=standardizedOuts[2];let doValidation=!1,valIns;if(args.validationData!=null&&args.validationData.length>0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;i<tensors.length;++i){let tensor2=tensors[i];if(tensor2.rank===1)outs.push(expandDims2(tensor2,1));else{if(tensor2.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");outs.push(tensor2)}}return outs}function disposeNewTensors(tensors,refTensors){if(tensors==null)return;let oldTensorIds=[];if(refTensors instanceof Tensor)oldTensorIds.push(refTensors.id);else if(Array.isArray(refTensors))refTensors.forEach(t=>oldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data!=null){let gotUnexpectedData=!1;if(isDataArray(data)&&data.length>0)gotUnexpectedData=!0;else if(isDataDict(data)){for(let key in data)if(data.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`)}return[]}if(data==null)return names.map(name=>null);let arrays;if(isDataDict(data)){data=data,arrays=[];for(let name of names){if(data[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data[name])}}else if(isDataArray(data)){if(data=data,data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);arrays=data}else{if(data=data,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);arrays=[data]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim>=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique2(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique2(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i<targets.length;++i){let y=targets[i],loss=lossFns[i],shape=outputShapes[i];if(loss!=null){if(loss===categoricalCrossentropy&&y.shape[y.shape.length-1]===1)throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(keyLosses.indexOf(loss)!==-1){let slicedYShape=y.shape.slice(1),slicedShape=shape.slice(1);for(let j=0;j<slicedYShape.length;++j){let targetDim=slicedYShape[j],outDim=slicedShape[j];if(outDim!=null&&targetDim!==outDim)throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function checkInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){let arrays;if(Array.isArray(data)){if(data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);arrays=data}else{if(names.length>1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);arrays=[data]}if(shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim!==dim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`)}}}function collectMetrics(metrics2,outputNames){if(metrics2==null||Array.isArray(metrics2)&&metrics2.length===0)return outputNames.map(name=>[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let shape=this.internalOutputShapes[i],name=this.outputNames[i];this.feedOutputNames.push(name),this.feedOutputShapes.push(shape),this.feedLossFns.push(this.lossFunctions[i])}let skipTargetIndices=[];this.metrics=args.metrics,this.metricsNames=["loss"],this.metricsTensors=[],nameScope("loss",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let weightedLoss=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let outputMetrics=nestedMetrics[i];(metrics2=>{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i])}else for(let input2 of this.inputs){let tensorValue=inputs[input2.name];if(tensorValue==null)throw new ValueError(`No value is provided for the model's input ${input2.name}`);feedDict.add(input2,tensorValue)}let executeOutputs=execute(outputSymbolicTensors,feedDict);return outputsIsArray?executeOutputs:executeOutputs[0]}retrieveSymbolicTensors(symbolicTensorNames){let outputSymbolicTensors=pyListRepeat(null,symbolicTensorNames.length),outputsRemaining=symbolicTensorNames.length;for(let layer of this.layers){let layerOutputs=Array.isArray(layer.output)?layer.output:[layer.output],layerOutputNames=layerOutputs.map(output=>output.name);for(let i=0;i<symbolicTensorNames.length;++i){let index=layerOutputNames.indexOf(symbolicTensorNames[i]);if(index!==-1&&(outputSymbolicTensors[i]=layerOutputs[index],outputsRemaining--),outputsRemaining===0)break}if(outputsRemaining===0)break}if(outputsRemaining>0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex<batches.length;++batchIndex)tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;i<insBatch.length;++i)feeds.push({key:this.inputs[i],value:insBatch[i]});else feeds.push({key:this.inputs[0],value:insBatch});let feedDict=new FeedDict(feeds);return execute(this.outputs,feedDict)}).forEach((batchOut,i)=>outsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i<this.feedOutputShapes.length;++i){let outputShape=this.feedOutputShapes[i];this.feedLossFns[i]===sparseCategoricalCrossentropy?outputShapes.push(outputShape.slice(0,outputShape.length-1).concat([1])):outputShapes.push(outputShape)}if(x=standardizeInputData(x,this.feedInputNames,this.feedInputShapes,!1,"input"),y=standardizeInputData(y,this.feedOutputNames,outputShapes,!1,"target"),checkArrayLengths(x,y,null),checkLossAndTargetCompatibility(y,this.feedLossFns,this.feedOutputShapes),this.stateful&&batchSize!=null&&batchSize>0&&x[0].shape[0]%batchSize!=0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i<classWeights.length;++i)standardSampleWeights.push(await standardizeWeights(standardYs[i],null,classWeights[i]))}return[standardXs,standardYs,standardSampleWeights]}testLoop(f,ins,batchSize,verbose=0,steps){return tidy(()=>{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range2(0,numSamples));for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(indexArray,batchStart,batchEnd-batchStart),insBatch=sliceArraysByIndices(ins,batchIds),batchOuts=f(insBatch);if(batchIndex===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i];outs[i]=add2(outs[i],mul(batchEnd-batchStart,batchOut))}}for(let i=0;i<outs.length;++i)outs[i]=div(outs[i],numSamples)}return outs})}getDedupedMetricsNames(){let outLabels=this.metricsNames,dedupedOutLabels=[];for(let i=0;i<outLabels.length;++i){let label=outLabels[i],newLabel=label;count(outLabels,label)>1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data=>{let lossValues=[],inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict,{training:!0}),totalLoss;for(let i=0;i<this.lossFunctions.length;++i){let loss=this.lossFunctions[i](targets[i],outputs[i]);sampleWeights[i]!=null&&(loss=computeWeightedLoss2(loss,sampleWeights[i]));let meanLoss=mean(loss);lossValues.push(meanLoss),i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss)}for(let i=0;i<this.metricsTensors.length;++i){let weightedMetric;if(this.outputs.length>1&&i<this.outputs.length)weightedMetric=lossValues[i];else{let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1];weightedMetric=mean(metric(targets[outputIndex],outputs[outputIndex]))}keep(weightedMetric),metricsValues.push(weightedMetric)}return totalLoss=mean(totalLoss),this.calculateLosses().forEach(regularizerLoss=>{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict);for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=mean(lossFunction(targets[i],outputs[i]));i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss),valOutputs.push(totalLoss)}for(let i=0;i<this.metricsTensors.length;++i){let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1],meanMetric=mean(metric(targets[outputIndex],outputs[outputIndex]));valOutputs.push(meanMetric)}return valOutputs})}async fit(x,y,args={}){return fitTensors(this,x,y,args)}async fitDataset(dataset,args){return fitDataset(this,dataset,args)}async trainOnBatch(x,y){let standardizeOut=await this.standardizeUserData(x,y),inputs=standardizeOut[0],targets=standardizeOut[1],losses4=this.makeTrainFunction()(inputs.concat(targets)),lossValues=[];for(let loss of losses4){let v=await loss.data();lossValues.push(v[0])}return dispose(losses4),singletonOrArray(lossValues)}getNamedWeights(config){let namedWeights=[],trainableOnly=config!=null&&config.trainableOnly,weights=trainableOnly?this.trainableWeights:this.weights,weightValues=this.getWeights(trainableOnly);for(let i=0;i<weights.length;++i)trainableOnly&&!weights[i].trainable||namedWeights.push({name:weights[i].originalName,tensor:weightValues[i]});return namedWeights}set stopTraining(stop){this.stopTraining_=stop}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(optimizer){this.optimizer_!==optimizer&&(this.optimizer_=optimizer,this.isOptimizerOwned=!1)}dispose(){let result=super.dispose();if(result.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let numTensorsBeforeOptmizerDisposal=memory().numTensors;this.optimizer_.dispose(),result.numDisposedVariables+=numTensorsBeforeOptmizerDisposal-memory().numTensors}return result}getLossIdentifiers(){let lossNames;if(typeof this.loss=="string")lossNames=toSnakeCase(this.loss);else if(Array.isArray(this.loss)){for(let loss of this.loss)if(typeof loss!="string")throw new Error("Serialization of non-string loss is not supported.");lossNames=this.loss.map(name=>toSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version2}`,convertedBy:null};if((config==null?!1:config.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config instanceof Array){if(config[0].className==null||config[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config}else util_exports.assert(config.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config.layers,delete config.layers,extraModelConfig=config;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config){return new Sequential(config)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config){return Input(config)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu2(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config={};return config.className="linear",config.config={},deserializeActivation(config)}if(typeof identifier=="string"){let config={};return config.className=identifier,config.config={},deserializeActivation(config)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square2(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config){return new cls({l1:config.l1,l2:config.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i<inputShape.length;++i)axes[i]=inputShape[i];this.inputSpec=[new InputSpec({ndim:inputShape.length,axes})],this.built=!0}call(inputs,kwargs){return inputs=getExactlyOneTensor(inputs),prelu(inputs,this.alpha.read())}getConfig(){let config={alphaInitializer:serializeInitializer(this.alphaInitializer),alphaRegularizer:serializeRegularizer(this.alphaRegularizer),alphaConstraint:serializeConstraint(this.alphaConstraint),sharedAxes:this.sharedAxes},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};PReLU.className="PReLU";serialization_exports.registerClass(PReLU);var ELU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA=1,args==null&&(args={}),args.alpha!=null&&args.alpha!==this.DEFAULT_ALPHA)throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return elu(x)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ELU.className="ELU";serialization_exports.registerClass(ELU);var ThresholdedReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_THETA=1,args==null&&(args={}),this.theta=args.theta==null?this.DEFAULT_THETA:args.theta}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return x.mul(cast2(x.greater(this.theta),"float32"))}computeOutputShape(inputShape){return inputShape}getConfig(){let config={theta:this.theta},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ThresholdedReLU.className="ThresholdedReLU";serialization_exports.registerClass(ThresholdedReLU);var Softmax3=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_AXIS=1,args==null&&(args={}),this.softmax=new Softmax2().apply,this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return this.softmax(x,this.axis)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Softmax3.className="Softmax";serialization_exports.registerClass(Softmax3);function normalizeArray(value,n,name){if(typeof value=="number")return pyListRepeat(value,n);if(value.length!==n)throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);for(let i=0;i<n;++i){let singleValue=value[i];if(!isInteger(singleValue))throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`)}return value}function convOutputLength(inputLength,filterSize,padding,stride,dilation=1){if(inputLength==null)return inputLength;let dilatedFilterSize=filterSize+(filterSize-1)*(dilation-1),outputLength;return padding==="same"?outputLength=inputLength:outputLength=inputLength-dilatedFilterSize+1,Math.floor((outputLength+stride-1)/stride)}function deconvLength(dimSize,strideSize,kernelSize,padding){if(dimSize==null)return null;if(padding==="valid")dimSize=dimSize*strideSize+max2([kernelSize-strideSize,0]);else if(padding==="same")dimSize=dimSize*strideSize;else throw new ValueError(`Unsupport padding mode: ${padding}.`);return dimSize}function preprocessConv2DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i<space.length;++i){let newDim=convOutputLength(space[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);newSpace.push(newDim)}let outputShape=[inputShape[0]];return this.dataFormat==="channelsLast"?(outputShape=outputShape.concat(newSpace),outputShape.push(this.filters)):(outputShape.push(this.filters),outputShape=outputShape.concat(newSpace)),outputShape}getConfig(){let config={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static verifyArgs(args){if(!("filters"in args)||typeof args.filters!="number"||args.filters<1)throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config=super.getConfig();return delete config.dilationRate,config}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config){super(rank,config);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config.kernelInitializer!=null||config.kernelRegularizer!=null||config.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config.padding!=null&&config.padding!=="same"&&config.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);this.depthMultiplier=config.depthMultiplier==null?1:config.depthMultiplier,this.depthwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<this.rank+2)throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(inputShape)}`);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=this.kernelSize.concat([inputDim,this.depthMultiplier]),pointwiseKernelShape=[];for(let i=0;i<this.rank;++i)pointwiseKernelShape.push(1);pointwiseKernelShape.push(inputDim*this.depthMultiplier,this.filters);let trainable=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,trainable,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",pointwiseKernelShape,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,trainable,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,trainable,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config=super.getConfig();return delete config.rank,delete config.kernelInitializer,delete config.kernelRegularizer,delete config.kernelConstraint,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config=super.getConfig();return delete config.rank,delete config.dataFormat,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d3(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d3(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config=super.getConfig();return config.depthMultiplier=this.depthMultiplier,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range2(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;t<timeSteps;++t){let currentInput=perStepInputs[t],stepOutputs=tidy(()=>stepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range2(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;i<numStates;++i)output.push(null);return output}else return this.states_}set states(s){this.states_=s}build(inputShape){let constantShape=null;if(this.numConstants!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let batchSize=this.stateful?inputShape[0]:null,inputDim=inputShape.slice(2);this.inputSpec[0]=new InputSpec({shape:[batchSize,null,...inputDim]});let stepInputShape=[inputShape[0]].concat(inputShape.slice(2));if(constantShape!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");this.cell.build(stepInputShape);let stateSize;if(Array.isArray(this.cell.stateSize)?stateSize=this.cell.stateSize:stateSize=[this.cell.stateSize],this.stateSpec!=null){if(!util_exports.arraysEqual(this.stateSpec.map(spec=>spec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],dim=Array.isArray(this.cell.stateSize)?this.cell.stateSize[index]:this.cell.stateSize,expectedShape=[batchSize,dim];if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims2(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile2(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile2(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cellConfig=config.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return new cls(config)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let matrixX=dot2(inputs,this.kernel.read());this.useBias&&(matrixX=biasAdd(matrixX,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0]));let recurrentKernelValue=this.recurrentKernel.read(),[rk1,rk2]=split(recurrentKernelValue,[2*this.units,this.units],recurrentKernelValue.rank-1),matrixInner=dot2(hTMinus1,rk1),[xZ,xR,xH]=split(matrixX,3,matrixX.rank-1),[recurrentZ,recurrentR]=split(matrixInner,2,matrixInner.rank-1);z=this.recurrentActivation.apply(add2(xZ,recurrentZ)),r=this.recurrentActivation.apply(add2(xR,recurrentR));let recurrentH=dot2(mul(r,hTMinus1),rk2);hh=this.activation.apply(add2(xH,recurrentH));let h=add2(mul(z,hTMinus1),mul(add2(1,neg(z)),hh));return[h,h]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},baseConfig,config)}};GRUCell.className="GRUCell";serialization_exports.registerClass(GRUCell);var GRU=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new GRUCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min2([1,max2([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min2([1,max2([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let z=dot2(inputs,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0])),z=add2(z,dot2(hTMinus1,this.recurrentKernel.read())),this.useBias&&(z=biasAdd(z,this.bias.read()));let[z0,z1,z2,z3]=split(z,4,z.rank-1);i=this.recurrentActivation.apply(z0),f=this.recurrentActivation.apply(z1),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(z2))),o=this.recurrentActivation.apply(z3);let h=mul(o,this.activation.apply(c));return[h,h,c]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},baseConfig,config)}};LSTMCell.className="LSTMCell";serialization_exports.registerClass(LSTMCell);var LSTM=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new LSTMCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i<this.cells.length;++i){let cell=this.cells[i];states=nestedStates[i],i===0?callInputs=[inputs[0]].concat(states):callInputs=[callInputs[0]].concat(states),callInputs=cell.call(callInputs,kwargs),newNestedStates.push(callInputs.slice(1))}states=[];for(let cellStates of newNestedStates.slice().reverse())states.push(...cellStates);return[callInputs[0]].concat(states)})}build(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let outputDim;this.cells.forEach((cell,i)=>{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cells=[];for(let cellConfig of config.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;i<cell.weights.length;++i)tuples.push([cell.weights[i],inputWeights[i]])}batchSetValue(tuples)}};StackedRNNCells.className="StackedRNNCells";serialization_exports.registerClass(StackedRNNCells);function generateDropoutMask(args){let{ones:ones4,rate,training=!1,count:count2=1}=args,droppedInputs=()=>dropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i<p2.length;i++)e.indexOf(p2[i])<0&&Object.prototype.propertyIsEnumerable.call(s,p2[i])&&(t[p2[i]]=s[p2[i]]);return t};var ConvRNN2D=class extends RNN{constructor(args){if(args.unroll)throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(args.cell))throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(args);this.inputSpec=[new InputSpec({ndim:5})]}call(inputs,kwargs){return tidy(()=>{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],expectedShape=stateShape;if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state=>keep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config){return new cls(config)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i<this.noiseShape.length;++i)noiseShape.push(this.noiseShape[i]==null?inputShape[i]:this.noiseShape[i]);return noiseShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0<this.rate&&this.rate<1){let training=kwargs.training==null?!1:kwargs.training,noiseShape=this.getNoiseShape(input2);return inTrainPhase(()=>dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i<input2.rank;++i)permutation.push(i);permutation.push(1),input2=input2.transpose(permutation)}return batchFlatten(input2)})}getConfig(){let config={};this.dataFormat!=null&&(config.dataFormat=this.dataFormat);let baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Flatten.className="Flatten";serialization_exports.registerClass(Flatten);var Activation2=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.activation=getActivation(args.activation)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config={n:this.n},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i<this.targetShape.length;++i)this.isUnknown(this.targetShape[i])&&(this.targetShape[i]=null)}isUnknown(dim){return dim<0||dim==null}fixUnknownDimension(inputShape,outputShape){let errorMsg="Total size of new array must be unchanged.",finalShape=outputShape.slice(),known=1,unknown=null;for(let i=0;i<finalShape.length;++i){let dim=finalShape[i];if(this.isUnknown(dim))if(unknown===null)unknown=i;else throw new ValueError("Can only specifiy one unknown dimension.");else known*=dim}let originalSize=arrayProd(inputShape);if(unknown!==null){if(known===0||originalSize%known!=0)throw new ValueError(errorMsg);finalShape[unknown]=originalSize/known}else if(originalSize!==known)throw new ValueError(errorMsg);return finalShape}computeOutputShape(inputShape){let anyUnknownDims=!1;for(let i=0;i<inputShape.length;++i)if(this.isUnknown(inputShape[i])){anyUnknownDims=!0;break}return anyUnknownDims?inputShape.slice(0,1).concat(this.targetShape):inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape))}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range2(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={maskValue:this.maskValue};return Object.assign(config,baseConfig),config}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k<inLens.length;++k){let s1=inLens[k],s2=inputShape[k+1];if(s1!=null&&s2!=null&&s1!==s2)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);s1==null&&(inLens[i]=s2),i++}}return[inputShape[0],...inLens,this.outputDim]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast2(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length<shape2.length)return this.computeElementwiseOpOutputShape(shape2,shape1);if(shape2.length===0)return shape1;let outputShape=shape1.slice(0,shape1.length-shape2.length);for(let k=0;k<shape2.length;++k){let i=shape1[shape1.length-shape2.length+k],j=shape2[k];if(i==null||j==null||i<0||j<0)outputShape.push(null);else if(i===1)outputShape.push(j);else if(j===1)outputShape.push(i);else{if(i!==j)throw new ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(shape1)+" "+JSON.stringify(shape2));outputShape.push(i)}}return outputShape}build(inputShape){if(Array.isArray(inputShape)&&!Array.isArray(inputShape[0])&&(inputShape=[getExactlyOneShape(inputShape)]),inputShape=inputShape,inputShape.length<2)throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);if(batchSizes=unique2(batchSizes),batchSizes.length>1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let allRanks=inputShape.map(shape=>shape.length);inputShape.indexOf(null)===-1&&unique2(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max2(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k<maxNDim-xNDim;++k)x=expandDims2(x,1);reshapedInputs.push(x)}return this.mergeFunction(reshapedInputs)}else{let transposed=!1;for(let x of inputs){let xNDim=x.rank;if(xNDim==null){let xShape=x.shape,batchSize=xShape[0],newShape=xShape.slice(1).concat([batchSize]),xTransposed=x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));xTransposed=transpose(xTransposed,[1,0]),xTransposed=xTransposed.reshape(newShape),reshapedInputs.push(xTransposed),transposed=!0}else if(xNDim>1){let dims=range2(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range2(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);return batchSizes=unique2(batchSizes),batchSizes.length===1?outputShape=batchSizes.concat(outputShape):outputShape=[null].concat(outputShape),outputShape}computeMask(inputs,mask){return tidy(()=>{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i<mask.length-1;++i)output=logicalAnd(output,mask[i]);return output})}},Add2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return output})}};Add2.className="Add";serialization_exports.registerClass(Add2);var Multiply2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=mul(output,inputs[i]);return output})}};Multiply2.className="Multiply";serialization_exports.registerClass(Multiply2);var Average=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return mul(1/inputs.length,output)})}};Average.className="Average";serialization_exports.registerClass(Average);var Maximum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=maximum(output,inputs[i]);return output})}};Maximum2.className="Maximum";serialization_exports.registerClass(Maximum2);var Minimum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=minimum(output,inputs[i]);return output})}};Minimum2.className="Minimum";serialization_exports.registerClass(Minimum2);var Concatenate=class extends Merge{constructor(args){super(args);this.DEFAULT_AXIS=-1,args==null&&(args={}),this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0]))||inputShape.length===1)throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");inputShape=inputShape;let allNoneShape=!0;for(let shape of inputShape)if(shape!=null){allNoneShape=!1;break}if(allNoneShape)return;let shapeSet=[];for(let i=0;i<inputShape.length;++i){let shapeWithoutConcatAxis=inputShape[i].slice();shapeWithoutConcatAxis.splice(this.axis,1);let exists=!1;for(let shape of shapeSet)if(util_exports.arraysEqual(shape,shapeWithoutConcatAxis)){exists=!0;break}exists||shapeSet.push(shapeWithoutConcatAxis)}if(shapeSet.length>1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i<inputs.length;++i)mask[i]==null?outputMasks.push(onesLike(inputs[i]).asType("bool")):mask[i].rank<inputs[i].rank?outputMasks.push(expandDims(mask[i],-1)):outputMasks.push(mask[i]);let concatenatedMasks=concat(outputMasks,this.axis);return all(concatenatedMasks,-1,!1)})}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Concatenate.className="Concatenate";serialization_exports.registerClass(Concatenate);function interpretAxis(axis,dim){for(;axis<0;)axis+=dim;return axis}function batchDot(x,y,axes){if(x.shape.length>3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);y=y.reshape(y.shape.concat(diffShape))}else if(yNDim>xNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);x=x.reshape(x.shape.concat(diffShape))}else diff=0;let out;if(x.shape.length===2&&y.shape.length===2)axesArray[0]===axesArray[1]?out=x.mul(y).sum(axesArray[0]):out=x.transpose([1,0]).mul(y).sum(axesArray[1]);else{let adjX=axesArray[0]!==x.shape.length-1,adjY=axesArray[1]===y.shape.length-1;out=x.matMul(y,adjX,adjY)}if(diff>0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i<idx+diff;++i)squeezeAxes.push(i);out=out.squeeze(squeezeAxes)}return out.shape.length===1&&(out=out.expandDims(1)),out})}var Dot=class extends Merge{constructor(args){super(args);this.axes=args.axes,this.normalize=args.normalize==null?!1:args.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={stddev:this.stddev};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale2=1.0507009873554805,alphaP=-alpha*scale2,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast2(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon3=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon3);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon3),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range2(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon3),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range2(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range2(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range2(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=nDims);for(let axis of this.axis)if(axis<0||axis>=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique2(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale2=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i<nDims;++i)this.axis.indexOf(i)!==-1?(momentsTiling.push(inputShape[i]),scaleOffsetTiling.push(1)):(momentsTiling.push(1),scaleOffsetTiling.push(inputShape[i]));return mean4=mean4.tile(momentsTiling),variance=variance.tile(momentsTiling),scale2=scale2.tile(scaleOffsetTiling),offset=offset.tile(scaleOffsetTiling),batchNormalization(input2,mean4,variance,offset,scale2,this.epsilon)})}getConfig(){let config={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LayerNormalization.className="LayerNormalization";serialization_exports.registerClass(LayerNormalization);function spatial2dPadding(x,padding,dataFormat){return tidy(()=>{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims2(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config,customObjects={}){let layerConfig=config.layer,layer=deserialize(layerConfig,customObjects);delete config.layer;let newConfig={layer};return Object.assign(newConfig,config),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static fromConfig(cls,config){let rnnLayer=deserialize(config.layer);if(delete config.layer,config.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu3(args){return new ELU(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu2(args){return new PReLU(args)}function softmax2(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d3(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d4(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape2(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add3(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum2(args){return new Maximum2(args)}function minimum2(args){return new Minimum2(args)}function multiply(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config){return new L1L2(config)}function l12(config){return l1(config)}function l22(config){return l2(config)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less2(currVal,prevVal){return currVal<prevVal}function greater2(currVal,prevVal){return currVal>prevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less2:this.mode==="max"?this.monitorFunc=greater2:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater2:this.monitorFunc=less2,this.monitorFunc===less2&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less2?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping};var DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node,tensorMap,context,resourceManager){let inputParam=node.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node.inputNames.slice(start)[0],tensorMap,context,resourceManager),data=tensor2.dataSync();return inputParam.type==="number"?data[0]:util_exports.toNestedArray(tensor2.shape,data)}let attrParam=node.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node,tensorMap,context){let pad3=getParamValue("pad",node,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var basic_math_exports={};__export(basic_math_exports,{json:()=>json2});var json2=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var control_exports={};__export(control_exports,{json:()=>json3});var json3=[{tfOpName:"EmptyTensorList",category:"control",inputs:[{start:0,name:"elementShape",type:"shape"},{start:1,name:"maxNumElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"LoopCond",category:"control",inputs:[{start:0,name:"pred",type:"tensor"}]},{tfOpName:"Switch",category:"control",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"pred",type:"tensor"}]},{tfOpName:"Merge",category:"control",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"Enter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"frame_name",name:"frameName",type:"string"},{tfName:"is_constant",name:"isConstant",type:"bool"}]},{tfOpName:"Exit",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NextIteration",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayV3",category:"control",inputs:[{start:0,name:"size",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"},{tfName:"dynamic_size",name:"dynamicSize",type:"bool"},{tfName:"clear_after_read",name:"clearAfterRead",type:"bool"},{tfName:"identical_element_shapes",name:"identicalElementShapes",type:"bool"},{tfName:"tensor_array_name",name:"name",type:"string"}]},{tfOpName:"TensorArrayWriteV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayReadV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayGatherV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"}]},{tfOpName:"TensorArrayScatterV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArrayConcatV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape_except0",name:"elementShapeExcept0",type:"shape",notSupported:!0}]},{tfOpName:"TensorArraySplitV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"tensor",type:"tensor"},{start:2,name:"lengths",type:"number[]"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArraySizeV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}]},{tfOpName:"TensorArrayCloseV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"}]},{tfOpName:"StatelessIf",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"If",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"StatelessWhile",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"While",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"TensorListScatter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListScatterV2",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"},{start:3,name:"numElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListGather",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfN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convolution_exports={};__export(convolution_exports,{json:()=>json4});var json4=[{tfOpName:"AvgPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[],notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPoolWithArgmax",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"include_batch_in_index",name:"includeBatchInIndex",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AvgPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Conv1D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"stride",name:"stride",type:"number"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NWC"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"dilation",name:"dilation",type:"number",defaultValue:1}]},{tfOpName:"Conv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"useCudnnOnGpu",name:"useCudnnOnGpu",type:"bool"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"_FusedConv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"use_cudnn_on_gpu",name:"useCudnnOnGpu",type:"bool",defaultValue:!0},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"leakyrelu_alpha",name:"leakyreluAlpha",type:"number"}]},{tfOpName:"Conv2DBackpropInput",category:"convolution",inputs:[{start:2,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:0,name:"outputShape",type:"number[]"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]",notSupported:!0}]},{tfOpName:"DepthwiseConv2d",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"DepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"FusedDepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]}]},{tfOpName:"Conv3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"Dilation2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"rates",name:"dilations",type:"number[]"},{tfName:"padding",name:"pad",type:"string"}]}];var creation_exports={};__export(creation_exports,{json:()=>json5});var json5=[{tfOpName:"Fill",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"},{start:1,name:"value",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"LinSpace",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"num",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"OneHot",category:"creation",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"depth",type:"number"},{start:2,name:"onValue",type:"number",defaultValue:1},{start:3,name:"offValue",type:"number",defaultValue:0}],attrs:[{tfName:"axis",name:"axis",type:"number",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ones",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"OnesLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"RandomUniform",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"minval",name:"minval",type:"number",defaultValue:0},{tfName:"maxval",name:"maxval",type:"number",defaultValue:1},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"seed",name:"seed",type:"number",defaultValue:0},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Range",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"step",type:"number",defaultValue:0}],attrs:[{tfName:"Tidx",name:"dtype",type:"dtype"}]},{tfOpName:"TruncatedNormal",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"means",name:"mean",type:"number",defaultValue:0},{tfName:"stddev",name:"stdDev",type:"number",defaultValue:1},{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Zeros",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"ZerosLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"Multinomial",category:"creation",inputs:[{start:0,name:"logits",type:"tensor"},{start:1,name:"numSamples",type:"number"}],attrs:[{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number"},{tfName:"T",name:"dtype",type:"dtype"},{tfName:"output_dtype",name:"output_dtype",type:"dtype"}]}];var dynamic_exports={};__export(dynamic_exports,{json:()=>json6});var json6=[{tfOpName:"NonMaxSuppressionV2",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV3",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV4",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"T_threshold",name:"threshold",type:"dtype",notSupported:!0},{tfName:"pad_to_max_output_size",name:"padToMaxOutputSize",type:"bool"}]},{tfOpName:"NonMaxSuppressionV5",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"},{start:5,name:"softNmsSigma",type:"number"}]},{tfOpName:"Where",category:"dynamic",inputs:[{start:0,name:"condition",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ListDiff",category:"dynamic",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var evaluation_exports={};__export(evaluation_exports,{json:()=>json7});var json7=[{tfOpName:"TopKV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"k",type:"number"}],attrs:[{tfName:"sorted",name:"sorted",type:"bool"}]},{tfOpName:"Unique",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"UniqueV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]}];var graph_exports={};__export(graph_exports,{json:()=>json8});var json8=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}];var hash_table_exports={};__export(hash_table_exports,{json:()=>json9});var json9=[{tfOpName:"HashTable",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"HashTableV2",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"LookupTableImport",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableImportV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFind",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFindV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]}];var image_exports={};__export(image_exports,{json:()=>json10});var json10=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"half_pixel_centers",name:"halfPixelCenters",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"half_pixel_centers",name:"halfPixelCenters",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}];var logical_exports={};__export(logical_exports,{json:()=>json11});var json11=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var matrices_exports={};__export(matrices_exports,{json:()=>json12});var json12=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var normalization_exports={};__export(normalization_exports,{json:()=>json13});var json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}];var reduction_exports={};__export(reduction_exports,{json:()=>json14});var json14=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}];var slice_join_exports={};__export(slice_join_exports,{json:()=>json15});var json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}];var spectral_exports={};__export(spectral_exports,{json:()=>json16});var json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}];var transformation_exports={};__export(transformation_exports,{json:()=>json17});var json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];var OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op.startsWith("Placeholder")?placeholders.push(map[node.name]):node.op==="Const"?weights.push(map[node.name]):(node.input==null||node.input.length===0)&&initNodes.push(map[node.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node=nodes[key];node.children.length===0&&outputs.push(node)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node!=null&&(node.signatureKey=outputNodeNameToKey[name],outputs.push(node))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node&&(node.signatureKey=inputNodeNameToKey[name],inputs.push(node))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node){let mapper=getRegisteredOp(node.op)||this.opMappers[node.op]||{};node.attr==null&&(node.attr={});let newNode={name:node.name,op:node.op,category:mapper.category,inputNames:(node.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op==="Const"&&weights.push(map[node.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node.signatureKey=arg.name,inputs.push(node),nodes[nodeName]=node}),Object.keys(nodes).forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node=nodes[nodeName];node!=null&&(node.defaultOutput=index,outputs.push(node))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){switch(typeof value=="string"&&(value=DataType[value]),value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&&param.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&&param.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&&param.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&&param.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&&param.list&&param.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node,tensorMap,context){this.node=node,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node.inputNames.map(name=>this.getInput(name)),node.rawAttrs!=null&&(this.attrs=Object.keys(node.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}};var executeOp=(node,tensorMap,context)=>{switch(node.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp2=(node,tensorMap,context)=>{switch(node.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node,tensorMap,context),getParamValue("imag",node,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node,tensorMap,context))];case"Log":return[log(getParamValue("x",node,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node,tensorMap,context))];case"Real":return[real(getParamValue("x",node,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node,tensorMap,context))];case"Round":return[round2(getParamValue("x",node,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node,tensorMap,context))];case"Square":return[square(getParamValue("x",node,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node,tensorMap,context),getParamValue("clipValueMin",node,tensorMap,context),getParamValue("clipValueMax",node,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node,tensorMap,context),getParamValue("axes",node,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==-1&&n2[i]!==-1&&n1[i]!==n2[i])return!1;return!0}var TensorArray=class{constructor(name,dtype,maxSize,elementShape,identicalElementShapes,dynamicSize,clearAfterRead){this.name=name,this.dtype=dtype,this.maxSize=maxSize,this.elementShape=elementShape,this.identicalElementShapes=identicalElementShapes,this.dynamicSize=dynamicSize,this.clearAfterRead=clearAfterRead,this.tensors=[],this.closed_=!1,this.idTensor=scalar(0),keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
           because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=tensor2.shape),assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${index}.`),t.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);if(t.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);t.tensor=tensor2,keep(tensor2),t.written=!0,this.tensors[index]=t}writeMany(indices,tensors){if(indices.length!==tensors.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);indices.forEach((i,index)=>this.write(i,tensors[index]))}gather(indices,dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);if(indices)indices=indices.slice(0,this.size());else{indices=[];for(let i=0;i<this.size();i++)indices.push(i)}if(indices.length===0)return tensor([],[0].concat(this.elementShape));let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,"TensorArray shape mismatch: "),stack(tensors,0)}concat(dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);if(this.size()===0)return tensor([],[0].concat(this.elementShape));let indices=[];for(let i=0;i<this.size();i++)indices.push(i);let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`),concat(tensors,0)}scatter(indices,tensor2){if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);if(indices.length!==tensor2.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);let maxIndex=Math.max(...indices);if(!this.dynamicSize&&maxIndex>=this.maxSize)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);this.writeMany(indices,unstack(tensor2,0))}split(length,tensor2){if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
         ${totalLength}, and tensor's shape is: ${tensor2.shape}`);if(!this.dynamicSize&&length.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=[];tidy(()=>{tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices2=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors[i]=reshape(slice(tensor2,indices2,sizes),this.elementShape)}return tensors});let indices=[];for(let i=0;i<length.length;i++)indices[i]=i;this.writeMany(indices,tensors)}};var TensorList=class{constructor(tensors,elementShape,elementDtype,maxNumElements=-1){this.tensors=tensors,this.elementShape=elementShape,this.elementDtype=elementDtype,tensors!=null&&tensors.forEach(tensor2=>{if(elementDtype!==tensor2.dtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);assertShapesMatchAllowUndefinedSize(elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2)}),this.idTensor=scalar(0),this.maxNumElements=maxNumElements,keep(this.idTensor)}get id(){return this.idTensor.id}copy(){return new TensorList([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.id))&&tensor2.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(elementShape,elementDtype,numElements=-1){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(numElements!==-1&&this.tensors.length!==numElements)throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);return assertShapesMatchAllowUndefinedSize(elementShape,this.elementShape,"TensorList shape mismatch: "),tidy(()=>{let reshapedTensors=this.tensors.map(tensor2=>reshape(tensor2,elementShape));return stack(reshapedTensors,0)})}popBack(elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let tensor2=this.tensors.pop();return assertShapesMatchAllowUndefinedSize(tensor2.shape,elementShape,"TensorList shape mismatch: "),reshape(tensor2,elementShape)}pushBack(tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(assertShapesMatchAllowUndefinedSize(tensor2.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");keep(tensor2),this.tensors.push(tensor2)}resize(size){if(size<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);if(this.maxNumElements!==-1&&size>this.maxNumElements)throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=size}getItem(elementIndex,elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||elementIndex>this.tensors.length)throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);if(this.tensors[elementIndex]==null)throw new Error(`element at index ${elementIndex} is null.`);return assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape,elementShape,"TensorList shape mismatch: "),this.tensors[elementIndex]}setItem(elementIndex,tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||this.maxNumElements!==-1&&elementIndex>=this.maxNumElements)throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2),this.tensors[elementIndex]=tensor2}gather(indices,elementDtype,elementShape){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),indices=indices.slice(0,this.size()),indices.length===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=indices.map(i=>reshape(this.tensors[i],elementShape));return stack(tensors,0)})}concat(elementDtype,elementShape){if(!!elementDtype&&elementDtype!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),this.size()===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=this.tensors.map(t=>reshape(t,elementShape));return concat(tensors,0)})}};function fromTensor(tensor2,elementShape,elementDtype){let dtype=tensor2.dtype;if(tensor2.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);if(tensor2.dtype!==elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);let outputShape=tensor2.shape.slice(1);assertShapesMatchAllowUndefinedSize(outputShape,elementShape,"TensorList shape mismatch: ");let tensorList=unstack(tensor2);return new TensorList(tensorList,elementShape,dtype)}function reserve(elementShape,elementDtype,numElements){return new TensorList([],elementShape,elementDtype,numElements)}function scatter(tensor2,indices,elementShape,numElements){if(indices.length!==tensor2.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);let maxIndex=Math.max(...indices);if(numElements!=null&&numElements!==-1&&maxIndex>=numElements)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);let list=new TensorList([],elementShape,tensor2.dtype,numElements),tensors=unstack(tensor2,0);return indices.forEach((value,index)=>{list.setItem(value,tensors[index])}),list}function split2(tensor2,length,elementShape){let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
-        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors2[i]=reshape(slice(tensor2,indices,sizes),elementShape)}return tensor2.dispose(),tensors2}),list=new TensorList([],elementShape,tensor2.dtype,length.length);for(let i=0;i<tensors.length;i++)list.setItem(i,tensors[i]);return list}var executeOp3=async(node,tensorMap,context)=>{switch(node.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node,tensorMap,context),elseFunc=getParamValue("elseBranch",node,tensorMap,context),cond=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node,tensorMap,context),condFunc=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node,tensorMap,context),data=getParamValue("data",node,tensorMap,context);return data.kept||(data=cloneTensor(data)),(await pred.data())[0]?[void 0,data]:[data,void 0]}case"Merge":{let inputName=node.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data=getTensor(inputName,tensorMap,context);return[cloneTensor(data)]}return}case"Enter":{let frameId=getParamValue("frameName",node,tensorMap,context),data=getParamValue("tensor",node,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data)]}case"Exit":{let data=getParamValue("tensor",node,tensorMap,context);return context.exitFrame(),[cloneTensor(data)]}case"NextIteration":{let data=getParamValue("tensor",node,tensorMap,context);return context.nextIteration(),[cloneTensor(data)]}case"TensorArrayV3":{let size=getParamValue("size",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node,tensorMap,context),name=getParamValue("name",node,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),gatherDtype=getParamValue("dtype",node,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node,tensorMap,context),scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node,tensorMap,context),splitTensor=getParamValue("tensor",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElementsParam;node.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node,tensorMap,context)=>{switch(node.op){case"Conv1D":{let stride=getParamValue("stride",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node,tensorMap,context);return[conv1d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node,tensorMap,context),stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context);return[conv2dTranspose(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv3d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp5=(node,tensorMap,context)=>{switch(node.op){case"Fill":{let shape=getParamValue("shape",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),value=getParamValue("value",node,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),num=getParamValue("num",node,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node,tensorMap,context),numSamples=getParamValue("numSamples",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node,tensorMap,context),depth=getParamValue("depth",node,tensorMap,context),onValue=getParamValue("onValue",node,tensorMap,context),offValue=getParamValue("offValue",node,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node,tensorMap,context),getParamValue("minval",node,tensorMap,context),getParamValue("maxval",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"Range":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),step4=getParamValue("step",node,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node,tensorMap,context),mean4=getParamValue("mean",node,tensorMap,context),stdDev=getParamValue("stdDev",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function nmsParams(node,tensorMap,context){let boxes=getParamValue("boxes",node,tensorMap,context),scores=getParamValue("scores",node,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node,tensorMap,context)=>{switch(node.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context));default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp7=(node,tensorMap,context)=>{switch(node.op){case"TopKV2":{let x=getParamValue("x",node,tensorMap,context),k=getParamValue("k",node,tensorMap,context),sorted=getParamValue("sorted",node,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp8=(node,tensorMap,context)=>{switch(node.op){case"Const":return tensorMap[node.name];case"PlaceholderWithDefault":let def=getParamValue("default",node,tensorMap,context);return[getTensor(node.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data2=getParamValue("x",node,tensorMap,context);return[cloneTensor(data2)]}case"IdentityN":return getParamValue("x",node,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node,tensorMap,context),data=getParamValue("data",node,tensorMap,context),message=getParamValue("message",node,tensorMap,context),summarize=getParamValue("summarize",node,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;i<data.length;i++)console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0,summarize));return[input2];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var HashTable=class{constructor(keyDType,valueDType){this.keyDType=keyDType,this.valueDType=valueDType,this.handle=scalar(0),this.tensorMap=new Map,keep(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i<keysLength;i++){let key=$keys[i],value=$values[i];keep(value),this.tensorMap.set(key,value)}return this.handle})}async find(keys,defaultValue){this.checkKeyAndValueTensor(keys,defaultValue);let $keys=await keys.data();return tidy(()=>{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}};var executeOp9=async(node,tensorMap,context,resourceManager)=>{switch(node.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node,tensorMap,context),valueDType=getParamValue("valueDType",node,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp10=(node,tensorMap,context)=>{switch(node.op){case"ResizeBilinear":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node,tensorMap,context),boxes=getParamValue("boxes",node,tensorMap,context),boxInd=getParamValue("boxInd",node,tensorMap,context),cropSize=getParamValue("cropSize",node,tensorMap,context),method=getParamValue("method",node,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp11=(node,tensorMap,context)=>{switch(node.op){case"Equal":return[equal(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Less":return[less(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node,tensorMap,context),getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp12=(node,tensorMap,context)=>{switch(node.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context),getParamValue("transposeA",node,tensorMap,context),getParamValue("transposeB",node,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node,tensorMap,context),getParamValue("perm",node,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node,tensorMap,context),b:getParamValue("b",node,tensorMap,context),transposeA:getParamValue("transposeA",node,tensorMap,context),transposeB:getParamValue("transposeB",node,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp13=(node,tensorMap,context)=>{switch(node.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node,tensorMap,context),getParamValue("radius",node,tensorMap,context),getParamValue("bias",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context),getParamValue("beta",node,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node,tensorMap,context),getParamValue("outputShape",node,tensorMap,context),getParamValue("sparseValues",node,tensorMap,context),getParamValue("defaultValue",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp14=(node,tensorMap,context)=>{switch(node.op){case"Max":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[max(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[mean(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[min(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[sum2(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[all(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[any(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node,tensorMap,context);return[argMax(getParamValue("x",node,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node,tensorMap,context);return[argMin(getParamValue("x",node,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[prod(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node,tensorMap,context),exclusive=getParamValue("exclusive",node,tensorMap,context),reverse5=getParamValue("reverse",node,tensorMap,context);return[cumsum(getParamValue("x",node,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node,tensorMap,context),weights=getParamValue("weights",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node,tensorMap,context),weights2=getParamValue("weights",node,tensorMap,context),size2=getParamValue("size",node,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp15=(node,tensorMap,context)=>{switch(node.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),inputs=getParamValue("tensors",node,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node,tensorMap,context),batchDims=getParamValue("batchDims",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node,tensorMap,context),axis=[];for(let i=0;i<dims.length;i++)dims[i]&&axis.push(i);let input2=getParamValue("x",node,tensorMap,context);return[reverse(input2,axis)]}case"ReverseV2":{let axis=getParamValue("axis",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context);return[reverse(input2,axis)]}case"Slice":{let begin=getParamValue("begin",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[slice(getParamValue("x",node,tensorMap,context),begin,size)]}case"StridedSlice":{let begin=getParamValue("begin",node,tensorMap,context),end=getParamValue("end",node,tensorMap,context),strides=getParamValue("strides",node,tensorMap,context),beginMask=getParamValue("beginMask",node,tensorMap,context),endMask=getParamValue("endMask",node,tensorMap,context),ellipsisMask=getParamValue("ellipsisMask",node,tensorMap,context),newAxisMask=getParamValue("newAxisMask",node,tensorMap,context),shrinkAxisMask=getParamValue("shrinkAxisMask",node,tensorMap,context),tensor2=getParamValue("x",node,tensorMap,context);return[stridedSlice(tensor2,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)]}case"Pack":return tidy(()=>{let axis=getParamValue("axis",node,tensorMap,context),tensors=getParamValue("tensors",node,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node,tensorMap,context),tensor2=getParamValue("tensor",node,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node,tensorMap,context);return[tile(getParamValue("x",node,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node,tensorMap,context),tensor2=getParamValue("x",node,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),shape=getParamValue("shape",node,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node,tensorMap,context),shape=getParamValue("outputShape",node,tensorMap,context),sparseValues=getParamValue("sparseValues",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp16=(node,tensorMap,context)=>{switch(node.op){case"FFT":return[fft(getParamValue("x",node,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp17=(node,tensorMap,context)=>{switch(node.op){case"Cast":return[cast(getParamValue("x",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node,tensorMap,context);return[expandDims(getParamValue("x",node,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node,tensorMap,context);return[squeeze(getParamValue("x",node,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("mode",node,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("constantValue",node,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),paddings=getParamValue("paddings",node,tensorMap,context);return[spaceToBatchND(getParamValue("x",node,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),crops=getParamValue("crops",node,tensorMap,context);return[batchToSpaceND(getParamValue("x",node,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function executeOp18(node,tensorMap,context,resourceManager){let value=((node2,tensorMap2,context2)=>{switch(node2.category){case"arithmetic":return tidy(()=>executeOp(node2,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node2,tensorMap2,context2));case"control":return executeOp3(node2,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node2,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node2,tensorMap2,context2));case"dynamic":return executeOp6(node2,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node2,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node2,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node2,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node2,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node2,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node2,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node2,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node2,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node2,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node2,tensorMap2,context2));case"hash_table":return executeOp9(node2,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node2.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node2,tensorMap2,context2));throw TypeError(`Custom op ${node2.op} is not registered.`);default:throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node,tensorMap,context);return util_exports.isPromise(value)?value.then(data=>[].concat(data)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;i<this.contexts.length-1;i++){let contexts2=this.contexts.slice(0,this.contexts.length-i);names.push(this.contextIdforContexts(contexts2))}names.push(""),this._currentContextIds=names}contextIdforContexts(contexts2){return contexts2?contexts2.map(context=>context.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node=>parseNodeName(node.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node=frontier.pop();if(((isControlFlow(node)||isDynamicShape(node)||isHashTable(node))&&(dynamicNode==null&&(dynamicNode=node,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name)))),usedNodes.add(node.name),weightMap[node.name]==null)&&(inputNodeNames.indexOf(node.name)===-1&&initNodeNames.indexOf(node.name)===-1)){if(node.inputs.length===0){missingInputs.push(node.name);continue}node.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node=>{usedNodes.has(node.name)&&frontier.push(node)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node=frontier.pop();seen.add(node.name),weightMap[node.name]||orderedNodes.push(node),node.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node){return CONTROL_FLOW_OPS.indexOf(node.op)>=0}function isDynamicShape(node){return DYNAMIC_SHAPE_OPS.indexOf(node.op)>=0}function isHashTable(node){return HASH_TABLE_OPS.indexOf(node.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node=>node.signatureKey||node.name)}get outputNodes(){return this._outputs.map(node=>{let name=node.signatureKey||node.name;return node.defaultOutput?`${name}:${node.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node=>node.name).sort(),sortedOutputs=outputs.map(node=>node.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;i<orderedNodes.length;i++){let node=orderedNodes[i];if(!tensorsMap[node.name]){let tensors=executeOp18(node,tensorsMap,context,this._resourceManager);if(util_exports.isPromise(tensors))throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`);tensorsMap[node.name]=tensors,this.checkTensorForDisposal(node.name,node,tensorsMap,context,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount)}}return this.parent==null&&context.dispose(tensorsToKeep),outputs.map(name=>getTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node.children.length)}),node.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node=>({node,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node=>!isControlFlow(node)&&!getTensor(node.name,tensorsMap,context)).map(node=>node.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node,stack2,context,tensorMap,added,usedNodes){node.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node=this.graph.nodes[nodeName];if(node.attrParams.shape&&node.attrParams.shape.value){let shape=node.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node.attrParams.dtype&&node.attrParams.dtype.value&&util_exports.assert(input2.dtype===node.attrParams.dtype.value,()=>`The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}};var TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&(modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`));let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version3="2.8.2";var dist_exports={};__export(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version4,webcam:()=>webcam,zip:()=>zip});var seedrandom3=__toModule(require_seedrandom2()),seedrandom2=__toModule(require_seedrandom2());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}};var GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;i<len;i++)newData[i]=this.get(this.wrap(this.begin+i));this.data=newData,this.capacity=newCapacity,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=len}};GrowingRingBuffer.INITIAL_CAPACITY=32;function iteratorFromItems(items){return new ArrayIterator(items)}function iteratorFromFunction(func2){return new FunctionCallIterator(func2)}function iteratorFromConcatenated(baseIterators,baseErrorHandler){return new ChainedIterator(baseIterators,baseErrorHandler)}function iteratorFromZipped(iterators,mismatchMode=ZipMismatchMode.FAIL){return new ZipIterator(iterators,mismatchMode)}var LazyIterator=class{async toArray(){let result=[],x=await this.next();for(;!x.done;)result.push(x.value),x=await this.next();return result}async toArrayForTest(){let stream=this.prefetch(100),result=[],x=await stream.next();for(;!x.done;)result.push(x.value),x=await stream.next();return result}async resolveFully(){let x=await this.next();for(;!x.done;)x=await this.next()}async resolveWhile(predicate){let x=await this.next(),shouldContinue=predicate(x.value);for(;!x.done&&shouldContinue;)x=await this.next(),shouldContinue=predicate(x.value)}handleErrors(handler){return new ErrorHandlingLazyIterator(this,handler)}filter(predicate){return new FilterIterator(this,predicate)}map(transform){return new MapIterator(this,transform)}mapAsync(transform){return new AsyncMapIterator(this,transform)}serialMapAsync(transform){return new AsyncMapIterator(this,transform).serial()}flatmap(transform){return new FlatmapIterator(this,transform)}async forEachAsync(f){return this.map(f).resolveFully()}async serialForEach(f){return this.serialMapAsync(f).resolveWhile(x=>x===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let skipped=await this.upstream.next();if(skipped.done)return skipped;dispose(skipped.value)}return this.upstream.next()}},TakeIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length<this.batchSize;){let item=await this.upstream.next();if(item.done)return this.enableSmallLastBatch&&batch.length>0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom2.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}};var Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is
+        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors2[i]=reshape(slice(tensor2,indices,sizes),elementShape)}return tensor2.dispose(),tensors2}),list=new TensorList([],elementShape,tensor2.dtype,length.length);for(let i=0;i<tensors.length;i++)list.setItem(i,tensors[i]);return list}var executeOp3=async(node,tensorMap,context)=>{switch(node.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node,tensorMap,context),elseFunc=getParamValue("elseBranch",node,tensorMap,context),cond=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node,tensorMap,context),condFunc=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node,tensorMap,context),data=getParamValue("data",node,tensorMap,context);return data.kept||(data=cloneTensor(data)),(await pred.data())[0]?[void 0,data]:[data,void 0]}case"Merge":{let inputName=node.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data=getTensor(inputName,tensorMap,context);return[cloneTensor(data)]}return}case"Enter":{let frameId=getParamValue("frameName",node,tensorMap,context),data=getParamValue("tensor",node,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data)]}case"Exit":{let data=getParamValue("tensor",node,tensorMap,context);return context.exitFrame(),[cloneTensor(data)]}case"NextIteration":{let data=getParamValue("tensor",node,tensorMap,context);return context.nextIteration(),[cloneTensor(data)]}case"TensorArrayV3":{let size=getParamValue("size",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node,tensorMap,context),name=getParamValue("name",node,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),gatherDtype=getParamValue("dtype",node,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node,tensorMap,context),scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node,tensorMap,context),splitTensor=getParamValue("tensor",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElementsParam;node.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node,tensorMap,context)=>{switch(node.op){case"Conv1D":{let stride=getParamValue("stride",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node,tensorMap,context);return[conv1d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node,tensorMap,context),stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context);return[conv2dTranspose(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv3d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp5=(node,tensorMap,context)=>{switch(node.op){case"Fill":{let shape=getParamValue("shape",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),value=getParamValue("value",node,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),num=getParamValue("num",node,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node,tensorMap,context),numSamples=getParamValue("numSamples",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node,tensorMap,context),depth=getParamValue("depth",node,tensorMap,context),onValue=getParamValue("onValue",node,tensorMap,context),offValue=getParamValue("offValue",node,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node,tensorMap,context),getParamValue("minval",node,tensorMap,context),getParamValue("maxval",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"Range":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),step4=getParamValue("step",node,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node,tensorMap,context),mean4=getParamValue("mean",node,tensorMap,context),stdDev=getParamValue("stdDev",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function nmsParams(node,tensorMap,context){let boxes=getParamValue("boxes",node,tensorMap,context),scores=getParamValue("scores",node,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node,tensorMap,context)=>{switch(node.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context));default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp7=(node,tensorMap,context)=>{switch(node.op){case"TopKV2":{let x=getParamValue("x",node,tensorMap,context),k=getParamValue("k",node,tensorMap,context),sorted=getParamValue("sorted",node,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp8=(node,tensorMap,context)=>{switch(node.op){case"Const":return tensorMap[node.name];case"PlaceholderWithDefault":let def=getParamValue("default",node,tensorMap,context);return[getTensor(node.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data2=getParamValue("x",node,tensorMap,context);return[cloneTensor(data2)]}case"IdentityN":return getParamValue("x",node,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node,tensorMap,context),data=getParamValue("data",node,tensorMap,context),message=getParamValue("message",node,tensorMap,context),summarize=getParamValue("summarize",node,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;i<data.length;i++)console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0,summarize));return[input2];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var HashTable=class{constructor(keyDType,valueDType){this.keyDType=keyDType,this.valueDType=valueDType,this.handle=scalar(0),this.tensorMap=new Map,keep(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i<keysLength;i++){let key=$keys[i],value=$values[i];keep(value),this.tensorMap.set(key,value)}return this.handle})}async find(keys,defaultValue){this.checkKeyAndValueTensor(keys,defaultValue);let $keys=await keys.data();return tidy(()=>{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}};var executeOp9=async(node,tensorMap,context,resourceManager)=>{switch(node.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node,tensorMap,context),valueDType=getParamValue("valueDType",node,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp10=(node,tensorMap,context)=>{switch(node.op){case"ResizeBilinear":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node,tensorMap,context),boxes=getParamValue("boxes",node,tensorMap,context),boxInd=getParamValue("boxInd",node,tensorMap,context),cropSize=getParamValue("cropSize",node,tensorMap,context),method=getParamValue("method",node,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp11=(node,tensorMap,context)=>{switch(node.op){case"Equal":return[equal(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Less":return[less(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node,tensorMap,context),getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp12=(node,tensorMap,context)=>{switch(node.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context),getParamValue("transposeA",node,tensorMap,context),getParamValue("transposeB",node,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node,tensorMap,context),getParamValue("perm",node,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node,tensorMap,context),b:getParamValue("b",node,tensorMap,context),transposeA:getParamValue("transposeA",node,tensorMap,context),transposeB:getParamValue("transposeB",node,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp13=(node,tensorMap,context)=>{switch(node.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node,tensorMap,context),getParamValue("radius",node,tensorMap,context),getParamValue("bias",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context),getParamValue("beta",node,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node,tensorMap,context),getParamValue("outputShape",node,tensorMap,context),getParamValue("sparseValues",node,tensorMap,context),getParamValue("defaultValue",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp14=(node,tensorMap,context)=>{switch(node.op){case"Max":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[max(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[mean(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[min(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[sum2(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[all(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[any(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node,tensorMap,context);return[argMax(getParamValue("x",node,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node,tensorMap,context);return[argMin(getParamValue("x",node,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[prod(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node,tensorMap,context),exclusive=getParamValue("exclusive",node,tensorMap,context),reverse5=getParamValue("reverse",node,tensorMap,context);return[cumsum(getParamValue("x",node,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node,tensorMap,context),weights=getParamValue("weights",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node,tensorMap,context),weights2=getParamValue("weights",node,tensorMap,context),size2=getParamValue("size",node,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp15=(node,tensorMap,context)=>{switch(node.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),inputs=getParamValue("tensors",node,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node,tensorMap,context),batchDims=getParamValue("batchDims",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node,tensorMap,context),axis=[];for(let i=0;i<dims.length;i++)dims[i]&&axis.push(i);let input2=getParamValue("x",node,tensorMap,context);return[reverse(input2,axis)]}case"ReverseV2":{let axis=getParamValue("axis",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context);return[reverse(input2,axis)]}case"Slice":{let begin=getParamValue("begin",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[slice(getParamValue("x",node,tensorMap,context),begin,size)]}case"StridedSlice":{let begin=getParamValue("begin",node,tensorMap,context),end=getParamValue("end",node,tensorMap,context),strides=getParamValue("strides",node,tensorMap,context),beginMask=getParamValue("beginMask",node,tensorMap,context),endMask=getParamValue("endMask",node,tensorMap,context),ellipsisMask=getParamValue("ellipsisMask",node,tensorMap,context),newAxisMask=getParamValue("newAxisMask",node,tensorMap,context),shrinkAxisMask=getParamValue("shrinkAxisMask",node,tensorMap,context),tensor2=getParamValue("x",node,tensorMap,context);return[stridedSlice(tensor2,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)]}case"Pack":return tidy(()=>{let axis=getParamValue("axis",node,tensorMap,context),tensors=getParamValue("tensors",node,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node,tensorMap,context),tensor2=getParamValue("tensor",node,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node,tensorMap,context);return[tile(getParamValue("x",node,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node,tensorMap,context),tensor2=getParamValue("x",node,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),shape=getParamValue("shape",node,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node,tensorMap,context),shape=getParamValue("outputShape",node,tensorMap,context),sparseValues=getParamValue("sparseValues",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp16=(node,tensorMap,context)=>{switch(node.op){case"FFT":return[fft(getParamValue("x",node,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp17=(node,tensorMap,context)=>{switch(node.op){case"Cast":return[cast(getParamValue("x",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node,tensorMap,context);return[expandDims(getParamValue("x",node,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node,tensorMap,context);return[squeeze(getParamValue("x",node,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("mode",node,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("constantValue",node,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),paddings=getParamValue("paddings",node,tensorMap,context);return[spaceToBatchND(getParamValue("x",node,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),crops=getParamValue("crops",node,tensorMap,context);return[batchToSpaceND(getParamValue("x",node,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function executeOp18(node,tensorMap,context,resourceManager){let value=((node2,tensorMap2,context2)=>{switch(node2.category){case"arithmetic":return tidy(()=>executeOp(node2,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node2,tensorMap2,context2));case"control":return executeOp3(node2,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node2,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node2,tensorMap2,context2));case"dynamic":return executeOp6(node2,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node2,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node2,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node2,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node2,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node2,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node2,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node2,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node2,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node2,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node2,tensorMap2,context2));case"hash_table":return executeOp9(node2,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node2.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node2,tensorMap2,context2));throw TypeError(`Custom op ${node2.op} is not registered.`);default:throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node,tensorMap,context);return util_exports.isPromise(value)?value.then(data=>[].concat(data)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;i<this.contexts.length-1;i++){let contexts2=this.contexts.slice(0,this.contexts.length-i);names.push(this.contextIdforContexts(contexts2))}names.push(""),this._currentContextIds=names}contextIdforContexts(contexts2){return contexts2?contexts2.map(context=>context.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node=>parseNodeName(node.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node=frontier.pop();if((isControlFlow(node)||isDynamicShape(node)||isHashTable(node))&&dynamicNode==null&&(dynamicNode=node,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node.name),weightMap[node.name]==null&&inputNodeNames.indexOf(node.name)===-1&&initNodeNames.indexOf(node.name)===-1){if(node.inputs.length===0){missingInputs.push(node.name);continue}node.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node=>{usedNodes.has(node.name)&&frontier.push(node)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node=frontier.pop();seen.add(node.name),weightMap[node.name]||orderedNodes.push(node),node.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node){return CONTROL_FLOW_OPS.indexOf(node.op)>=0}function isDynamicShape(node){return DYNAMIC_SHAPE_OPS.indexOf(node.op)>=0}function isHashTable(node){return HASH_TABLE_OPS.indexOf(node.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node=>node.signatureKey||node.name)}get outputNodes(){return this._outputs.map(node=>{let name=node.signatureKey||node.name;return node.defaultOutput?`${name}:${node.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node=>node.name).sort(),sortedOutputs=outputs.map(node=>node.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;i<orderedNodes.length;i++){let node=orderedNodes[i];if(!tensorsMap[node.name]){let tensors=executeOp18(node,tensorsMap,context,this._resourceManager);if(util_exports.isPromise(tensors))throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`);tensorsMap[node.name]=tensors,this.checkTensorForDisposal(node.name,node,tensorsMap,context,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount)}}return this.parent==null&&context.dispose(tensorsToKeep),outputs.map(name=>getTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node.children.length)}),node.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node=>({node,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node=>!isControlFlow(node)&&!getTensor(node.name,tensorsMap,context)).map(node=>node.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node,stack2,context,tensorMap,added,usedNodes){node.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node=this.graph.nodes[nodeName];if(node.attrParams.shape&&node.attrParams.shape.value){let shape=node.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node.attrParams.dtype&&node.attrParams.dtype.value&&util_exports.assert(input2.dtype===node.attrParams.dtype.value,()=>`The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}};var ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}};var TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version3="2.8.3";var dist_exports={};__export(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version4,webcam:()=>webcam,zip:()=>zip});var seedrandom3=__toModule(require_seedrandom2());var seedrandom2=__toModule(require_seedrandom2());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}};var GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;i<len;i++)newData[i]=this.get(this.wrap(this.begin+i));this.data=newData,this.capacity=newCapacity,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=len}};GrowingRingBuffer.INITIAL_CAPACITY=32;function iteratorFromItems(items){return new ArrayIterator(items)}function iteratorFromFunction(func2){return new FunctionCallIterator(func2)}function iteratorFromConcatenated(baseIterators,baseErrorHandler){return new ChainedIterator(baseIterators,baseErrorHandler)}function iteratorFromZipped(iterators,mismatchMode=ZipMismatchMode.FAIL){return new ZipIterator(iterators,mismatchMode)}var LazyIterator=class{async toArray(){let result=[],x=await this.next();for(;!x.done;)result.push(x.value),x=await this.next();return result}async toArrayForTest(){let stream=this.prefetch(100),result=[],x=await stream.next();for(;!x.done;)result.push(x.value),x=await stream.next();return result}async resolveFully(){let x=await this.next();for(;!x.done;)x=await this.next()}async resolveWhile(predicate){let x=await this.next(),shouldContinue=predicate(x.value);for(;!x.done&&shouldContinue;)x=await this.next(),shouldContinue=predicate(x.value)}handleErrors(handler){return new ErrorHandlingLazyIterator(this,handler)}filter(predicate){return new FilterIterator(this,predicate)}map(transform){return new MapIterator(this,transform)}mapAsync(transform){return new AsyncMapIterator(this,transform)}serialMapAsync(transform){return new AsyncMapIterator(this,transform).serial()}flatmap(transform){return new FlatmapIterator(this,transform)}async forEachAsync(f){return this.map(f).resolveFully()}async serialForEach(f){return this.serialMapAsync(f).resolveWhile(x=>x===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let skipped=await this.upstream.next();if(skipped.done)return skipped;dispose(skipped.value)}return this.upstream.next()}},TakeIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length<this.batchSize;){let item=await this.upstream.next();if(item.done)return this.enableSmallLastBatch&&batch.length>0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom2.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}};var Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is
       ${batchSize}`);let size;return this.size===Infinity||this.size==null?size=this.size:smallLastBatch?size=Math.ceil(this.size/batchSize):size=Math.floor(this.size/batchSize),datasetFromIteratorFn(async()=>(await base2.iterator()).columnMajorBatch(batchSize,smallLastBatch,deepBatchConcat),size)}concatenate(dataset){let base2=this,size;return this.size===Infinity||dataset.size===Infinity?size=Infinity:this.size!=null&&dataset.size!=null?size=this.size+dataset.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).concatenate(await dataset.iterator()),size)}filter(predicate){let base2=this,size;return this.size===Infinity?size=Infinity:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).filter(x=>tidy(()=>predicate(x))),size)}async forEachAsync(f){return(await this.iterator()).forEachAsync(f)}map(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).map(x=>tidy(()=>transform(x))),this.size)}mapAsync(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).mapAsync(transform),this.size)}prefetch(bufferSize){if(bufferSize==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).prefetch(bufferSize),this.size)}repeat(count2){let base2=this,size;return this.size!=null&&count2>0?size=this.size*count2:count2===0?size=0:this.size!=null&&(count2===void 0||count2<0)?size=Infinity:size=null,datasetFromIteratorFn(async()=>{let iteratorIterator=iteratorFromFunction(async()=>({value:await base2.iterator(),done:!1}));return iteratorFromConcatenated(iteratorIterator.take(count2))},size)}skip(count2){let base2=this,size;return this.size!=null&&count2>=0&&this.size>=count2?size=this.size-count2:this.size!=null&&(this.size<count2||count2===void 0||count2<0)?size=0:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).skip(count2),size)}shuffle(bufferSize,seed,reshuffleEachIteration=!0){if(bufferSize==null||bufferSize<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let base2=this,random=seedrandom3.alea(seed||util_exports.now().toString());return datasetFromIteratorFn(async()=>{let seed2=random.int32();return reshuffleEachIteration&&(seed2+=random.int32()),(await base2.iterator()).shuffle(bufferSize,seed2.toString())},this.size)}take(count2){let base2=this,size;return this.size!=null&&this.size>count2?size=count2:this.size!=null&&this.size<=count2?size=this.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).take(count2),size)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Dataset.MAX_BUFFER_SIZE=1e4;function datasetFromIteratorFn(iteratorFn,size=null){return new class extends Dataset{constructor(){super(...arguments);this.size=size}async iterator(){return iteratorFn()}}}function array(items){return datasetFromIteratorFn(async()=>iteratorFromItems(items),items.length)}function zip(datasets){if(!isIterable2(datasets))throw new Error("The argument to zip() must be an object or array.");let size;if(Array.isArray(datasets))for(let i=0;i<datasets.length;i++)size=size==null?datasets[i].size:Math.min(size,datasets[i].size);else if(datasets instanceof Object)for(let ds in datasets)size=size==null?datasets[ds].size:Math.min(size,datasets[ds].size);return datasetFromIteratorFn(async()=>{let streams=await deepMapAndAwaitAll(datasets,d=>{if(d instanceof Dataset)return{value:d.iterator(),recurse:!1};if(isIterable2(d))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return iteratorFromZipped(streams,ZipMismatchMode.SHORTEST)},size)}function deepBatchConcat(rows){if(rows===null)return null;let exampleRow=rows[0];return canTensorify(exampleRow)?{value:batchConcat(rows),recurse:!1}:{value:null,recurse:!0}}function batchConcat(arrays){if(arrays.length===0)throw new Error("Can't make a batch of zero elements.");return arrays[0]instanceof Tensor?stack(arrays):tensor(arrays)}var TextLineDataset=class extends Dataset{constructor(input2){super();this.input=input2}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}};var CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i<this.fullColumnNames.length;i++){let key=this.fullColumnNames[i],config=this.columnConfigs?this.columnConfigs[key]:null;if(!(this.configuredColumnsOnly&&!config)){let value=values[i],parsedValue=null;if(value==="")if(config&&config.default!==void 0)parsedValue=config.default;else{if(config&&(config.required||config.isLabel))throw new Error(`Required column ${key} is empty in this line: ${line}`);parsedValue=void 0}else{let valueAsNum=Number(value);if(isNaN(valueAsNum))config&&config.dtype==="bool"?parsedValue=this.getBoolean(value):parsedValue=value;else if(!config||!config.dtype)parsedValue=valueAsNum;else switch(config.dtype){case"float32":parsedValue=valueAsNum;break;case"int32":parsedValue=Math.floor(valueAsNum);break;case"bool":parsedValue=this.getBoolean(value);break;default:parsedValue=valueAsNum}}config&&config.isLabel?labels[key]=parsedValue:features[key]=parsedValue}}return Object.keys(labels).length===0?features:{xs:features,ys:labels}}getBoolean(value){return value==="1"||value.toLowerCase()==="true"?1:0}parseRow(line,validateElementCount=!0){let result=[],readOffset=0,readLength=line.length,currentState=STATE_OUT;for(let i=0;i<readLength;i++)switch(currentState){case STATE_OUT:switch(line.charAt(i)){case CODE_QUOTE:readOffset=i+1,currentState=STATE_QUOTE;break;case this.delimiter:if(readOffset=i+1,this.delimiter===" "&&this.delimWhitespace)break;result.push(""),currentState=STATE_OUT;break;default:currentState=STATE_FIELD,readOffset=i;break}break;case STATE_FIELD:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i)),currentState=STATE_OUT,readOffset=i+1;break;default:}break;case STATE_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE_AFTER_QUOTE;break;default:}break;case STATE_QUOTE_AFTER_QUOTE:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i-1)),currentState=STATE_OUT,readOffset=i+1;break;case CODE_QUOTE:currentState=STATE_QUOTE;break;default:currentState=STATE_WITHIN_QUOTE_IN_QUOTE;break}break;case STATE_WITHIN_QUOTE_IN_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE;break;default:}break;default:}if(currentState===STATE_QUOTE_AFTER_QUOTE?result.push(line.substring(readOffset,readLength-1)):result.push(line.substring(readOffset)),validateElementCount&&result.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);return result}};var MicrophoneIterator=class extends LazyIterator{constructor(microphoneConfig){super();this.microphoneConfig=microphoneConfig,this.isClosed=!1,this.fftSize=microphoneConfig.fftSize||1024;let fftSizeLog2=Math.log2(this.fftSize);if(this.fftSize<0||fftSizeLog2<4||fftSizeLog2>14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data,i)=>freqData.set(data,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}};var WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var DataSource=class{};var StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}};var ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}};var FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data=fileReader.result;if(data instanceof ArrayBuffer&&(data=new Uint8Array(data)),!(data instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}};var URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version4="2.8.2";function assertNotComplex(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(`
+`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}};var CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i<this.fullColumnNames.length;i++){let key=this.fullColumnNames[i],config=this.columnConfigs?this.columnConfigs[key]:null;if(!(this.configuredColumnsOnly&&!config)){let value=values[i],parsedValue=null;if(value==="")if(config&&config.default!==void 0)parsedValue=config.default;else{if(config&&(config.required||config.isLabel))throw new Error(`Required column ${key} is empty in this line: ${line}`);parsedValue=void 0}else{let valueAsNum=Number(value);if(isNaN(valueAsNum))config&&config.dtype==="bool"?parsedValue=this.getBoolean(value):parsedValue=value;else if(!config||!config.dtype)parsedValue=valueAsNum;else switch(config.dtype){case"float32":parsedValue=valueAsNum;break;case"int32":parsedValue=Math.floor(valueAsNum);break;case"bool":parsedValue=this.getBoolean(value);break;default:parsedValue=valueAsNum}}config&&config.isLabel?labels[key]=parsedValue:features[key]=parsedValue}}return Object.keys(labels).length===0?features:{xs:features,ys:labels}}getBoolean(value){return value==="1"||value.toLowerCase()==="true"?1:0}parseRow(line,validateElementCount=!0){let result=[],readOffset=0,readLength=line.length,currentState=STATE_OUT;for(let i=0;i<readLength;i++)switch(currentState){case STATE_OUT:switch(line.charAt(i)){case CODE_QUOTE:readOffset=i+1,currentState=STATE_QUOTE;break;case this.delimiter:if(readOffset=i+1,this.delimiter===" "&&this.delimWhitespace)break;result.push(""),currentState=STATE_OUT;break;default:currentState=STATE_FIELD,readOffset=i;break}break;case STATE_FIELD:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i)),currentState=STATE_OUT,readOffset=i+1;break;default:}break;case STATE_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE_AFTER_QUOTE;break;default:}break;case STATE_QUOTE_AFTER_QUOTE:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i-1)),currentState=STATE_OUT,readOffset=i+1;break;case CODE_QUOTE:currentState=STATE_QUOTE;break;default:currentState=STATE_WITHIN_QUOTE_IN_QUOTE;break}break;case STATE_WITHIN_QUOTE_IN_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE;break;default:}break;default:}if(currentState===STATE_QUOTE_AFTER_QUOTE?result.push(line.substring(readOffset,readLength-1)):result.push(line.substring(readOffset)),validateElementCount&&result.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);return result}};var MicrophoneIterator=class extends LazyIterator{constructor(microphoneConfig){super();this.microphoneConfig=microphoneConfig,this.isClosed=!1,this.fftSize=microphoneConfig.fftSize||1024;let fftSizeLog2=Math.log2(this.fftSize);if(this.fftSize<0||fftSizeLog2<4||fftSizeLog2>14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data,i)=>freqData.set(data,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}};var WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var DataSource=class{};var StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}};var ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}};var FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data=fileReader.result;if(data instanceof ArrayBuffer&&(data=new Uint8Array(data)),!(data instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}};var URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version4="2.8.3";function assertNotComplex(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(`
 ============================
 Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
-============================`));let dataId={};return this.data.set(dataId,{values,dtype,refCount:1}),dataId}makeTensorInfo(shape,dtype,values){let outId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));outId=this.write(encodedValues,shape,dtype)}else outId=this.write(values,shape,dtype);return{dataId:outId,shape,dtype}}incRef(dataId){let tensorData=this.data.get(dataId);tensorData.refCount++}decRef(dataId){if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--}}move(dataId,values,shape,dtype){this.data.set(dataId,{values,dtype,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{dtype,complexTensorInfos}=this.data.get(dataId);if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);return backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}return this.data.get(dataId).values}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}makeOutput(values,shape,dtype){let dataId=this.write(values,shape,dtype);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}disposeData(dataId){if(this.data.has(dataId)){let{complexTensorInfos}=this.data.get(dataId);complexTensorInfos!=null&&(this.disposeData(complexTensorInfos.real.dataId),this.disposeData(complexTensorInfos.imag.dataId)),this.data.delete(dataId)}}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--,tensorData.refCount<1&&this.disposeData(dataId)}}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(condition){assertNotComplex([condition],"where");let condVals=this.readSync(condition.dataId);return whereImpl2(condition.shape,condVals)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},shared_exports={};__export(shared_exports,{addImpl:()=>addImpl,bincountImpl:()=>bincountImpl,bincountReduceImpl:()=>bincountReduceImpl,ceilImpl:()=>ceilImpl,concatImpl:()=>concatImpl,expImpl:()=>expImpl,expm1Impl:()=>expm1Impl,floorImpl:()=>floorImpl,gatherV2Impl:()=>gatherV2Impl,greaterImpl:()=>greaterImpl,lessImpl:()=>lessImpl,linSpaceImpl:()=>linSpaceImpl,logImpl:()=>logImpl,maxImpl:()=>maxImpl,maximumImpl:()=>maximumImpl,minimumImpl:()=>minimumImpl,multiplyImpl:()=>multiplyImpl,negImpl:()=>negImpl,notEqualImpl:()=>notEqualImpl,prodImpl:()=>prodImpl,rangeImpl:()=>rangeImpl,rsqrtImpl:()=>rsqrtImpl,simpleAbsImpl:()=>simpleAbsImpl,sliceImpl:()=>sliceImpl,squaredDifferenceImpl:()=>squaredDifferenceImpl,stridedSliceImpl:()=>stridedSliceImpl,subImpl:()=>subImpl,tileImpl:()=>tileImpl,topKImpl:()=>topKImpl,transposeImpl:()=>transposeImpl,uniqueImpl:()=>uniqueImpl});function simpleAbsImpl(vals){let resultValues=new Float32Array(vals.length);for(let i=0;i<vals.length;++i)resultValues[i]=Math.abs(vals[i]);return resultValues}var abs2=args=>{let{x}=args.inputs,cpuBackend=args.backend;assertNotComplex(x,"abs");let resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),values=cpuBackend.data.get(x.dataId).values;return resultValues=simpleAbsImpl(values),cpuBackend.makeOutput(resultValues,x.shape,"float32")},absConfig={kernelName:Abs,backendName:"cpu",kernelFunc:abs2};function createSimpleBinaryKernelImpl(op2){return(aShape,bShape,aVals,bVals,dtype)=>{let newShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultRank=newShape.length,resultStrides=util_exports.computeStrides(newShape),resultSize=util_exports.sizeFromShape(newShape),result=util_exports.getTypedArrayFromDType(dtype,resultSize),aRank=aShape.length,bRank=bShape.length,aStrides=util_exports.computeStrides(aShape),bStrides=util_exports.computeStrides(bShape),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,newShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<result.length;++i)result[i]=op2(aVals[i%aVals.length],bVals[i%bVals.length]);else for(let i=0;i<result.length;++i){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides);result[i]=op2(aVals[aIndex],bVals[bIndex])}return[result,newShape]}}function complex2(args){let{inputs,backend:backend2}=args,{real:real4,imag:imag4}=inputs,realVals=backend2.data.get(real4.dataId).values,imagVals=backend2.data.get(imag4.dataId).values,complexInfo=backend2.makeTensorInfo(real4.shape,"complex64"),complex4=backend2.data.get(complexInfo.dataId);return complex4.complexTensorInfos={real:backend2.makeTensorInfo(real4.shape,"float32",realVals),imag:backend2.makeTensorInfo(imag4.shape,"float32",imagVals)},complexInfo}var complexConfig={kernelName:Complex,backendName:"cpu",kernelFunc:complex2};function zeros3(backend2,shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros3(backend2,shape,"float32"),imag4=zeros3(backend2,shape,"float32");return complex2({inputs:{real:real4,imag:imag4},backend:backend2})}let values=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape),dtype);return backend2.makeTensorInfo(shape,dtype,values)}function identity2(args){let{inputs,backend:backend2}=args,{x}=inputs;return backend2.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig={kernelName:Identity,backendName:"cpu",kernelFunc:identity2};function real2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,real4=backend2.data.get(input2.dataId).complexTensorInfos.real,realVal=backend2.data.get(real4.dataId).values;return backend2.makeTensorInfo(real4.shape,real4.dtype,realVal)}var realConfig={kernelName:Real,backendName:"cpu",kernelFunc:real2};function cast3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity2({inputs:{x},backend:backend2});let zerosTensorInfo=zeros3(backend2,x.shape,x.dtype),floatX=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex2({inputs:{real:floatX,imag:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),result=cast3({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity2({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32"){let values=backend2.data.get(x.dataId).values,resultValues=Int32Array.from(values);return backend2.makeTensorInfo(x.shape,"int32",resultValues)}if(dtype==="bool"){let xVals=backend2.data.get(x.dataId).values,zero=util_exports.toTypedArray([0],x.dtype),[resultData,resultShape]=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0)(x.shape,[],xVals,zero,"bool");return backend2.makeTensorInfo(resultShape,"bool",resultData)}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig={kernelName:Cast,backendName:"cpu",kernelFunc:cast3};function binaryKernelFunc(name,simpleImpl,complexImpl,dtype){return complexImpl==null?({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;assertNotComplex([a,b],name);let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}:({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;if(a.dtype==="complex64"||b.dtype==="complex64"){let $aComplex=cast3({inputs:{x:a},backend:cpuBackend,attrs:{dtype:"complex64"}}),$aComplexVals=cpuBackend.data.get($aComplex.dataId),aReal=$aComplexVals.complexTensorInfos.real,aImag=$aComplexVals.complexTensorInfos.imag,aRealVals=cpuBackend.data.get(aReal.dataId).values,aImagVals=cpuBackend.data.get(aImag.dataId).values,$bComplex=cast3({inputs:{x:b},backend:cpuBackend,attrs:{dtype:"complex64"}}),$bComplexVals=cpuBackend.data.get($bComplex.dataId),bReal=$bComplexVals.complexTensorInfos.real,bImag=$bComplexVals.complexTensorInfos.imag,bRealVals=cpuBackend.data.get(bReal.dataId).values,bImagVals=cpuBackend.data.get(bImag.dataId).values,[resultRealData,resultImagData,resultShape]=complexImpl(a.shape,b.shape,aRealVals,aImagVals,bRealVals,bImagVals),resultReal=cpuBackend.makeTensorInfo(resultShape,"float32",resultRealData),resultImag=cpuBackend.makeTensorInfo(resultShape,"float32",resultImagData),result=complex2({inputs:{real:resultReal,imag:resultImag},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($aComplex),cpuBackend.disposeIntermediateTensorInfo($bComplex),cpuBackend.disposeIntermediateTensorInfo(resultReal),cpuBackend.disposeIntermediateTensorInfo(resultImag),result}else{let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}}}function createComplexBinaryKernelImpl(op2){return(aShape,bShape,aRealVals,aImagVals,bRealVals,bImagVals)=>{let resultShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultSize=util_exports.sizeFromShape(resultShape),resultRank=resultShape.length,resultStrides=util_exports.computeStrides(resultShape),resultRealVals=util_exports.getTypedArrayFromDType("float32",resultSize),resultImagVals=util_exports.getTypedArrayFromDType("float32",resultSize),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,resultShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,resultShape),aVals=backend_util_exports.mergeRealAndImagArrays(aRealVals,aImagVals),bVals=backend_util_exports.mergeRealAndImagArrays(bRealVals,bImagVals),aRank=aShape.length,aStrides=util_exports.computeStrides(aShape),bRank=bShape.length,bStrides=util_exports.computeStrides(bShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resultRealVals.length;i++){let aIdx=i%aVals.length,bIdx=i%bVals.length,result=op2(aVals[aIdx*2],aVals[aIdx*2+1],bVals[bIdx*2],bVals[bIdx*2+1]);resultRealVals[i]=result.real,resultImagVals[i]=result.imag}else for(let i=0;i<resultRealVals.length;i++){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides),opResult=op2(aVals[aIndex*2],aVals[aIndex*2+1],bVals[bIndex*2],bVals[bIndex*2+1]);resultRealVals[i]=opResult.real,resultImagVals[i]=opResult.imag}return[resultRealVals,resultImagVals,resultShape]}}var addImpl=createSimpleBinaryKernelImpl((a,b)=>a+b),addComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal+bReal,imag:aImag+bImag})),add4=binaryKernelFunc(Add,addImpl,addComplexImpl),addConfig={kernelName:Add,backendName:"cpu",kernelFunc:add4};function bincountImpl(xVals,weightsVals,weightsDtype,weightsShape,size){let weightsSize=util_exports.sizeFromShape(weightsShape),outVals=util_exports.makeZerosTypedArray(size,weightsDtype);for(let i=0;i<xVals.length;i++){let value=xVals[i];if(value<0)throw new Error("Input x must be non-negative!");value>=size||(weightsSize>0?outVals[value]+=weightsVals[i]:outVals[value]+=1)}return outVals}function bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput=!1){let numRows=xBuf.shape[0],numCols=xBuf.shape[1],outBuf=buffer([numRows,size],weightsBuf.dtype);for(let i=0;i<numRows;i++)for(let j=0;j<numCols;j++){let value=xBuf.get(i,j);if(value<0)throw new Error("Input x must be non-negative!");value>=size||(binaryOutput?outBuf.set(1,i,value):weightsBuf.size>0?outBuf.set(outBuf.get(i,value)+weightsBuf.get(i,j),i,value):outBuf.set(outBuf.get(i,value)+1,i,value))}return outBuf}function createSimpleUnaryImpl(op2){return(values,dtype,attrs)=>{let newValues=util_exports.getTypedArrayFromDType(dtype,values.length);for(let i=0;i<values.length;++i)newValues[i]=op2(values[i],attrs);return newValues}}function unaryKernelFunc(name,op2,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),$dtype=dtype||x.dtype,newValues=util_exports.getArrayFromDType($dtype,xSize);for(let i=0;i<xSize;++i)newValues[i]=op2(values[i],attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}function unaryKernelFuncFromImpl(name,unaryImpl,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,$dtype=dtype||x.dtype,newValues=unaryImpl(values,$dtype,attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}var ceilImpl=createSimpleUnaryImpl(xi=>Math.ceil(xi)),ceil2=unaryKernelFuncFromImpl(Ceil,ceilImpl),ceilConfig={kernelName:Ceil,backendName:"cpu",kernelFunc:ceil2};function concatImpl(inputs,outShape,dtype,simplyConcat){let outVals=util_exports.getArrayFromDType(dtype,util_exports.sizeFromShape(outShape));if(simplyConcat&&dtype!=="string"){let offset=0;inputs.forEach(input2=>{let size=util_exports.sizeFromShape(input2.shape);outVals.set(input2.vals,offset),offset+=size})}else{let colOffset=0;inputs.forEach(input2=>{let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(input2.vals):input2.vals,tIdx=0;for(let row=0;row<input2.shape[0];++row){let resIdx=row*outShape[1]+colOffset;for(let col=0;col<input2.shape[1];++col)outVals[resIdx+col]=decodedData[tIdx++]}colOffset+=input2.shape[1]})}return outVals}var expImpl=createSimpleUnaryImpl(xi=>Math.exp(xi)),exp2=unaryKernelFuncFromImpl(Exp,expImpl),expConfig={kernelName:Exp,backendName:"cpu",kernelFunc:exp2};var expm1Impl=createSimpleUnaryImpl(xi=>Math.expm1(xi)),expm12=unaryKernelFuncFromImpl(Expm1,expm1Impl),expm1Config={kernelName:Expm1,backendName:"cpu",kernelFunc:expm12};var floorImpl=createSimpleUnaryImpl(xi=>Math.floor(xi)),floor2=unaryKernelFuncFromImpl(Floor,floorImpl),floorConfig={kernelName:Floor,backendName:"cpu",kernelFunc:floor2};function gatherV2Impl(xBuf,indicesBuf,flattenOutputShape){let outBuf=buffer(flattenOutputShape,xBuf.dtype);for(let i=0;i<outBuf.size;++i){let originalLoc=outBuf.indexToLoc(i).slice(),batchIdx=originalLoc[0],indicesIdx=originalLoc[2],indicesIndex=indicesBuf.locToIndex([batchIdx,indicesIdx]);originalLoc[2]=indicesBuf.values[indicesIndex];let originalIndex=xBuf.locToIndex(originalLoc);outBuf.values[i]=xBuf.values[originalIndex]}return outBuf}var greaterImpl=createSimpleBinaryKernelImpl((a,b)=>a>b?1:0),greater3=binaryKernelFunc(Greater,greaterImpl,null,"bool"),greaterConfig={kernelName:Greater,backendName:"cpu",kernelFunc:greater3};var lessImpl=createSimpleBinaryKernelImpl((a,b)=>a<b?1:0),less3=binaryKernelFunc(Less,lessImpl,null,"bool"),lessConfig={kernelName:Less,backendName:"cpu",kernelFunc:less3};function linSpaceImpl(start,stop,num){let step4=(stop-start)/(num-1),values=util_exports.makeZerosTypedArray(num,"float32");values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var logImpl=createSimpleUnaryImpl(xi=>Math.log(xi)),log3=unaryKernelFuncFromImpl(Log,logImpl),logConfig={kernelName:Log,backendName:"cpu",kernelFunc:log3};function maxImpl(aVals,reduceSize,outShape,dtype){let vals=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(outShape));for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value)}vals[i]=max6}return vals}var maximumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.max(aValue,bValue)),maximum3=binaryKernelFunc(Maximum,maximumImpl),maximumConfig={kernelName:Maximum,backendName:"cpu",kernelFunc:maximum3};var minimumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.min(aValue,bValue)),minimum3=binaryKernelFunc(Minimum,minimumImpl),minimumConfig={kernelName:Minimum,backendName:"cpu",kernelFunc:minimum3};var multiplyImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue*bValue),multiplyComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal*bReal-aImag*bImag,imag:aReal*bImag+aImag*bReal})),multiply2=binaryKernelFunc(Multiply,multiplyImpl,multiplyComplexImpl),multiplyConfig={kernelName:Multiply,backendName:"cpu",kernelFunc:multiply2};function negImpl(xVals,xShape,xDtype){let minusOne=util_exports.createScalarValue(-1,xDtype);return multiplyImpl([],xShape,minusOne,xVals,xDtype)}function neg2(args){let{inputs,backend:backend2}=args,{x}=inputs;assertNotComplex(x,"neg");let xVals=backend2.data.get(x.dataId).values,[res,newShape]=negImpl(xVals,x.shape,x.dtype);return backend2.makeTensorInfo(newShape,x.dtype,res)}var negConfig={kernelName:Neg,backendName:"cpu",kernelFunc:neg2};var notEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0),notEqual2=binaryKernelFunc(NotEqual,notEqualImpl,null,"bool"),notEqualConfig={kernelName:NotEqual,backendName:"cpu",kernelFunc:notEqual2};function transposeImpl(xVals,xShape,dtype,perm,newShape){let xRank=xShape.length,xSize=util_exports.sizeFromShape(xShape),xStrides=util_exports.computeStrides(xShape),newStrides=util_exports.computeStrides(newShape),result=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(newShape));for(let i=0;i<xSize;++i){let loc=util_exports.indexToLoc(i,xRank,xStrides),newLoc=new Array(loc.length);for(let i2=0;i2<newLoc.length;i2++)newLoc[i2]=loc[perm[i2]];let newIndex=util_exports.locToIndex(newLoc,xRank,newStrides);result[newIndex]=xVals[i]}return result}function transpose2(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{perm}=attrs;assertNotComplex(x,"transpose");let xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let values=backend2.data.get(x.dataId).values,result=transposeImpl(values,x.shape,x.dtype,perm,newShape);return{dataId:backend2.write(result,newShape,x.dtype),shape:newShape,dtype:x.dtype}}var transposeConfig={kernelName:Transpose,backendName:"cpu",kernelFunc:transpose2};function prodImpl(xShape,xDtype,xVals,reductionAxes){let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,reductionAxes),outDtype=upcastType(xDtype,"int32"),outVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),outDtype),reduceSize=util_exports.sizeFromShape(reduceShape);for(let i=0;i<outVals.length;++i){let offset=i*reduceSize,prod5=1;for(let j=0;j<reduceSize;++j)prod5*=xVals[offset+j];outVals[i]=prod5}return{outVals,outShape,outDtype}}function prod2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"prod");let xRank=x.shape.length,axes=util_exports.parseAxisParam(axis,x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=x,intermediateTensorInfos=[];permutation!=null&&(permutedX=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}),intermediateTensorInfos.push(permutedX),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank));let xVals=backend2.data.get(permutedX.dataId).values,{outVals,outShape,outDtype}=prodImpl(permutedX.shape,permutedX.dtype,xVals,reductionAxes),resultShape=outShape;return keepDims&&(resultShape=backend_util_exports.expandShapeToKeepDim(outShape,axes)),intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(resultShape,outDtype,outVals)}var prodConfig={kernelName:Prod,backendName:"cpu",kernelFunc:prod2};function rangeImpl(start,stop,step4,dtype){let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step4<0,decreasingRangePositiveStep=stop<start&&step4>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return util_exports.makeZerosTypedArray(0,dtype);let numElements=Math.abs(Math.ceil((stop-start)/step4)),values=util_exports.makeZerosTypedArray(numElements,dtype);stop<start&&step4===1&&(step4=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var rsqrtImpl=createSimpleUnaryImpl(xi=>1/Math.sqrt(xi)),rsqrt2=unaryKernelFuncFromImpl(Rsqrt,rsqrtImpl),rsqrtConfig={kernelName:Rsqrt,backendName:"cpu",kernelFunc:rsqrt2};function sliceImpl(vals,begin,size,shape,dtype){let isContinous=slice_util_exports.isSliceContinous(shape,begin,size),length=util_exports.sizeFromShape(size),xStrides=util_exports.computeStrides(shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin,xStrides);return dtype==="string"?vals.slice(flatOffset,flatOffset+length):vals.subarray(flatOffset,flatOffset+length)}let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(vals):vals,inBuf=buffer(shape,dtype,decodedData),outBuf=buffer(size,dtype);for(let i=0;i<outBuf.size;++i){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.map((idx,j)=>idx+begin[j]);outBuf.set(inBuf.get(...inLoc),...outLoc)}return dtype==="string"?backend_util_exports.fromStringArrayToUint8(outBuf.values):outBuf.values}function slice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs;assertNotComplex(x,"slice");let[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);slice_util_exports.assertParamsValid(x,$begin,$size);let vals=backend2.data.get(x.dataId).values,outVals=sliceImpl(vals,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outVals)}var sliceConfig={kernelName:Slice,backendName:"cpu",kernelFunc:slice2};var squaredDifferenceImpl=createSimpleBinaryKernelImpl((a,b)=>{let diff=a-b;return diff*diff}),squaredDifference2=binaryKernelFunc(SquaredDifference,squaredDifferenceImpl),squaredDifferenceConfig={kernelName:SquaredDifference,backendName:"cpu",kernelFunc:squaredDifference2};function stridedSliceImpl(outShape,xBuf,strides,begin){let outBuf=buffer(outShape,xBuf.dtype);for(let i=0;i<outBuf.size;i++){let loc=outBuf.indexToLoc(i),newLoc=new Array(loc.length);for(let j=0;j<newLoc.length;j++)newLoc[j]=loc[j]*strides[j]+begin[j];outBuf.set(xBuf.get(...newLoc),...loc)}return outBuf}var subImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue-bValue),subComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal-bReal,imag:aImag-bImag})),sub2=binaryKernelFunc(Sub,subImpl,subComplexImpl),subConfig={kernelName:Sub,backendName:"cpu",kernelFunc:sub2};function tileImpl(xBuf,reps){let newShape=new Array(xBuf.rank);for(let i=0;i<newShape.length;i++)newShape[i]=xBuf.shape[i]*reps[i];let result=buffer(newShape,xBuf.dtype);for(let i=0;i<result.values.length;++i){let newLoc=result.indexToLoc(i),originalLoc=new Array(xBuf.rank);for(let j=0;j<originalLoc.length;j++)originalLoc[j]=newLoc[j]%xBuf.shape[j];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result}function topKImpl(x,xShape,xDtype,k,sorted){let lastDim=xShape[xShape.length-1],[batch,size]=[x.length/lastDim,lastDim],allTopKVals=util_exports.getTypedArrayFromDType(xDtype,batch*k),allTopKIndices=util_exports.getTypedArrayFromDType("int32",batch*k);for(let b=0;b<batch;b++){let offset=b*size,vals=x.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value);let outOffset=b*k,topKVals=allTopKVals.subarray(outOffset,outOffset+k),topKIndices=allTopKIndices.subarray(outOffset,outOffset+k);for(let i=0;i<k;i++)topKVals[i]=valAndInd[i].value,topKIndices[i]=valAndInd[i].index}let outputShape=xShape.slice();return outputShape[outputShape.length-1]=k,[buffer(outputShape,xDtype,allTopKVals),buffer(outputShape,"int32",allTopKIndices)]}function uniqueImpl(values,axis,shape,dtype){let $axis=util_exports.parseAxisParam(axis,shape)[0],newShape=[1,shape[0],1];for(let i=0;i<$axis;i++)newShape[0]*=shape[i];newShape[1]=shape[$axis];for(let i=$axis+1;i<shape.length;i++)newShape[2]*=shape[i];let uniqueElements={},indices=new Int32Array(shape[$axis]),inputBuffer=new TensorBuffer(newShape,dtype,values),uniqueIndices=[],is1DTensor=newShape[0]===1&&newShape[2]===1;for(let i=0;i<shape[$axis];i++){let element;if(is1DTensor)element=values[i].toString();else{let axisValues=[];for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)axisValues.push(inputBuffer.get(m,i,n));element=axisValues.join(",")}if(uniqueElements[element]!==void 0)indices[i]=uniqueElements[element];else{let uniqueIndex=Object.keys(uniqueElements).length;uniqueElements[element]=uniqueIndex,indices[i]=uniqueIndex,uniqueIndices.push(i)}}let outputTmpShape=newShape.slice();outputTmpShape[1]=Object.keys(uniqueElements).length;let outputBuffer=new TensorBuffer(outputTmpShape,dtype);uniqueIndices.forEach((uniqueElementIndex,i)=>{for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)outputBuffer.set(inputBuffer.get(m,uniqueElementIndex,n),m,i,n)});let outputShape=shape.slice();return outputShape[$axis]=outputTmpShape[1],{outputValues:outputBuffer.values,outputShape,indices}}var version5="2.8.2";registerBackend("cpu",()=>new MathBackendCPU,1);var elu4=unaryKernelFunc(Elu,xi=>xi>=0?xi:Math.exp(xi)-1),eluConfig={kernelName:Elu,backendName:"cpu",kernelFunc:elu4};function leakyRelu2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{alpha}=attrs;assertNotComplex([x],"leakyRelu");let xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outVals=util_exports.getTypedArrayFromDType("float32",xSize);for(let i=0;i<xVals.length;i++)outVals[i]=xVals[i]<0?alpha*xVals[i]:xVals[i];return backend2.makeTensorInfo(x.shape,"float32",outVals)}var leakyReluConfig={kernelName:LeakyRelu,backendName:"cpu",kernelFunc:leakyRelu2};var preluImpl=createSimpleBinaryKernelImpl((xValue,aValue)=>xValue<0?aValue*xValue:xValue);function prelu3(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs;assertNotComplex([x,alpha],"prelu");let aVals=backend2.data.get(x.dataId).values,bVals=backend2.data.get(alpha.dataId).values,[resultData,resultShape]=preluImpl(x.shape,alpha.shape,aVals,bVals,x.dtype);return backend2.makeTensorInfo(resultShape,x.dtype,resultData)}var preluConfig={kernelName:Prelu,backendName:"cpu",kernelFunc:prelu3};var relu2=unaryKernelFunc(Relu,xi=>Math.max(0,xi)),reluConfig={kernelName:Relu,backendName:"cpu",kernelFunc:relu2};var relu62=unaryKernelFunc(Relu6,xi=>Math.min(Math.max(0,xi),6)),relu6Config={kernelName:Relu6,backendName:"cpu",kernelFunc:relu62};function applyActivation2(backend2,x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return identity2({inputs:{x},backend:backend2});if(activation2==="relu")return relu2({inputs:{x},backend:backend2});if(activation2==="elu")return elu4({inputs:{x},backend:backend2});if(activation2==="relu6")return relu62({inputs:{x},backend:backend2});if(activation2==="prelu")return prelu3({inputs:{x,alpha:preluActivationWeights},backend:backend2});if(activation2==="leakyrelu")return leakyRelu2({inputs:{x},backend:backend2,attrs:{alpha:leakyreluAlpha}});throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`)}function reshape3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`),backend2.incRef(x.dataId);let xData=backend2.data.get(x.dataId);if(xData.complexTensorInfos!=null){let real4=xData.complexTensorInfos.real,imag4=xData.complexTensorInfos.imag;real4.shape=$shape,imag4.shape=$shape}return{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig={kernelName:Reshape,backendName:"cpu",kernelFunc:reshape3};function batchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;assertNotComplex([a,b],"matMul");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape3({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape3({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),sharedDim=transposeA?a3d.shape[1]:a3d.shape[2],leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),a3dValues=backend2.data.get(a3d.dataId).values,b3dValues=backend2.data.get(b3d.dataId).values,a3dStrides=util_exports.computeStrides(a3d.shape),b3dStrides=util_exports.computeStrides(b3d.shape),[aBatch,aOuterStep,aInnerStep]=transposeA?[a3dStrides[0],1,a3dStrides[1]]:[a3dStrides[0],a3dStrides[1],1],[bInnerStep,bOuterStep,bBatch]=transposeB?[1,b3dStrides[1],b3dStrides[0]]:[b3dStrides[1],1,b3dStrides[0]],size=leftDim*rightDim,result=buffer([batchDim,leftDim,rightDim],a3d.dtype),resVals=result.values,blockSize=backend2.blockSize;for(let bi=0;bi<batchDim;bi++)for(let i0=0;i0<leftDim;i0+=blockSize)for(let j0=0;j0<rightDim;j0+=blockSize)for(let k0=0;k0<sharedDim;k0+=blockSize){let iBlock=Math.min(i0+blockSize,leftDim),jBlock=Math.min(j0+blockSize,rightDim),kBlock=Math.min(k0+blockSize,sharedDim);for(let i=i0;i<iBlock;i++)for(let j=j0;j<jBlock;j++){let sum6=0;for(let k=k0;k<kBlock;k++){let batchOffsetA=Math.min(bi,batchDimA-1)*aBatch,batchOffsetB=Math.min(bi,batchDimB-1)*bBatch,aVal=a3dValues[batchOffsetA+i*aOuterStep+k*aInnerStep],bVal=b3dValues[k*bInnerStep+j*bOuterStep+batchOffsetB];sum6+=aVal*bVal}resVals[bi*size+(i*rightDim+j)]+=sum6}}return backend2.disposeIntermediateTensorInfo(a3d),backend2.disposeIntermediateTensorInfo(b3d),backend2.makeTensorInfo(outShape,result.dtype,result.values)}var batchMatMulConfig={kernelName:BatchMatMul,backendName:"cpu",kernelFunc:batchMatMul};function _fusedMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs,{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,current,addRes,activationRes,intermediates=[];current=batchMatMul({inputs:{a,b},attrs:{transposeA,transposeB},backend:backend2}),bias&&(addRes=add4({inputs:{a:current,b:bias},backend:backend2}),intermediates.push(current),current=addRes),activation2&&(activationRes=applyActivation2(backend2,current,activation2,preluActivationWeights,leakyreluAlpha),intermediates.push(current),current=activationRes);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return current}var _fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"cpu",kernelFunc:_fusedMatMul};var acos2=unaryKernelFunc(Acos,xi=>Math.acos(xi)),acosConfig={kernelName:Acos,backendName:"cpu",kernelFunc:acos2};var acosh2=unaryKernelFunc(Acosh,xi=>Math.acosh(xi)),acoshConfig={kernelName:Acosh,backendName:"cpu",kernelFunc:acosh2};function addN2(args){let{inputs,backend:backend2}=args,tensors=inputs;assertNotComplex(inputs,"addN");let vals=tensors.map(t=>backend2.data.get(t.dataId).values),outBuf=buffer(tensors[0].shape,tensors[0].dtype),outVals=outBuf.values;for(let i=0;i<tensors.length;i++){let currVals=vals[i];for(let j=0;j<outVals.length;j++)outVals[j]+=currVals[j]}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var addNConfig={kernelName:AddN,backendName:"cpu",kernelFunc:addN2};function all2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"all");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("all",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,all4=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];all4=all4&&value}vals[i]=all4}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var allConfig={kernelName:All,backendName:"cpu",kernelFunc:all2};function any2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"any");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("any",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,anyVal=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];anyVal=anyVal||value}vals[i]=anyVal}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var anyConfig={kernelName:Any,backendName:"cpu",kernelFunc:any2};function argMax2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMax");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMax",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset],maxIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value,maxIndex=j)}vals[i]=maxIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMaxConfig={kernelName:ArgMax,backendName:"cpu",kernelFunc:argMax2};function argMin2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMin");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMin",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset],minIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value,minIndex=j)}vals[i]=minIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMinConfig={kernelName:ArgMin,backendName:"cpu",kernelFunc:argMin2};var asin2=unaryKernelFunc(Asin,xi=>Math.asin(xi)),asinConfig={kernelName:Asin,backendName:"cpu",kernelFunc:asin2};var asinh2=unaryKernelFunc(Asinh,xi=>Math.asinh(xi)),asinhConfig={kernelName:Asinh,backendName:"cpu",kernelFunc:asinh2};var atan3=unaryKernelFunc(Atan,xi=>Math.atan(xi)),atanConfig={kernelName:Atan,backendName:"cpu",kernelFunc:atan3};var atan2Impl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.atan2(aValue,bValue)),atan22=binaryKernelFunc(Atan2,atan2Impl),atan2Config={kernelName:Atan2,backendName:"cpu",kernelFunc:atan22};var atanh2=unaryKernelFunc(Atanh,xi=>Math.atanh(xi)),atanhConfig={kernelName:Atanh,backendName:"cpu",kernelFunc:atanh2};function pool2(xValues,xShape,dtype,strides,convInfo,poolType){let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3],outputRowStrides=convInfo.outShape[2]*convInfo.outShape[3],outputColStrides=convInfo.outShape[3];for(let b=0;b<convInfo.batchSize;++b){let outputBatchOffset=b*outputBatchStrides,inputBatchOffset=b*strides[0];for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=Math.max(0,xRCorner),xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner),outputRowOffset=outputBatchOffset+yR*outputRowStrides;for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=Math.max(0,xCCorner),xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),minMaxValue=initialValue,avgValue=0,count2=0;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let xROffset=inputBatchOffset+xR*strides[1];for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let xCOffset=xROffset+xC*strides[2],pixel=xValues[xCOffset+d];poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++)}if(isNaN(minMaxValue))break}let outputOffset=outputRowOffset+yC*outputColStrides+d;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}return output}function maxPoolPositions(xValues,xShape,dtype,convInfo,flattenPositions=!1,includeBatchInIndex=!1){let maxPositions=buffer(convInfo.outShape,"int32"),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=buffer(xShape,dtype,xValues);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=xRCorner;for(;xRMin<0;)xRMin+=dilationHeight;let xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner);for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=xCCorner;for(;xCMin<0;)xCMin+=dilationWidth;let xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let wR=xR-xRCorner;for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let wC=xC-xCCorner,pixel=xBuf.get(b,xR,xC,d);pixel>maxValue&&(maxValue=pixel,flattenPositions?maxPosition=includeBatchInIndex?((b*convInfo.inHeight+xR)*convInfo.inWidth+xC)*convInfo.inChannels+d:(xR*convInfo.inWidth+xC)*convInfo.inChannels+d:maxPosition=wR*effectiveFilterWidth+wC)}}maxPositions.set(maxPosition,b,yR,yC,d)}}return maxPositions}function pool3d2(xValues,xShape,dtype,strides,convInfo,poolType){let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputDepthStrides=convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputRowStrides=convInfo.outShape[3]*convInfo.outShape[4],outputColStrides=convInfo.outShape[4];for(let batch=0;batch<convInfo.batchSize;++batch){let outputBatchOffset=batch*outputBatchStrides,inputBatchOffset=batch*strides[0];for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner),outputDepthOffset=outputBatchOffset+yDepth*outputDepthStrides;for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner),outputRowOffset=outputDepthOffset+yRow*outputRowStrides;for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),outputColOffset=outputRowOffset+yCol*outputColStrides,minMaxValue=initialValue,avgValue=0,count2=0;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let xDepthOffset=inputBatchOffset+xDepth*strides[1];for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let xRowOffset=xDepthOffset+xRow*strides[2];for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let xColOffset=xRowOffset+xCol*strides[3],pixel=xValues[xColOffset+channel];if(poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++),isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}let outputOffset=outputColOffset+channel;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}}return output}function maxPool3dPositions(xBuf,convInfo){let maxPositions=buffer(convInfo.outShape,"int32"),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner);for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner);for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let wDepth=xDepth-xDepthCorner;for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let wRow=xRow-xRowCorner;for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let wCol=xCol-xColCorner,pixel=xBuf.get(batch,xDepth,xRow,xCol,channel);pixel>=maxValue&&(maxValue=pixel,maxPosition=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterHeight+wCol)}}}maxPositions.set(maxPosition,batch,yDepth,yRow,yCol,channel)}}}return maxPositions}function avgPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"avgPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"avg");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var avgPoolConfig={kernelName:AvgPool,backendName:"cpu",kernelFunc:avgPool2};function avgPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"avgPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"avg");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var avgPool3DConfig={kernelName:AvgPool3D,backendName:"cpu",kernelFunc:avgPool3D};function avgPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"avgPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),avgMultiplier=1/(filterDepth*filterHeight*filterWidth),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol||(dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel))}}}dx.set(dotProd*avgMultiplier,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPool3DGradConfig2={kernelName:AvgPool3DGrad,backendName:"cpu",kernelFunc:avgPool3DGrad};function avgPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex([dy,input2],"avgPoolGrad");let{filterSize,strides,pad:pad3}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterHeight*filterWidth),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC||(dotProd+=dyBuf.get(b,dyR,dyC,d))}}dx.set(dotProd*avgMultiplier,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPoolGradConfig2={kernelName:AvgPoolGrad,backendName:"cpu",kernelFunc:avgPoolGrad2};function batchNorm2(args){let{inputs,backend:backend2,attrs}=args,{x,scale:scale2,offset,mean:mean4,variance}=inputs;util_exports.assert(mean4.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean4.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale2==null||mean4.shape.length===scale2.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),assertNotComplex([x,mean4,variance,scale2,offset],"batchNorm");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let xVals=backend2.data.get(x.dataId).values,mVals=backend2.data.get(mean4.dataId).values,varVals=backend2.data.get(variance.dataId).values,sVals=scale2?backend2.data.get(scale2.dataId).values:new Float32Array([1]),offVals=offset?backend2.data.get(offset.dataId).values:new Float32Array([0]),outVals=new Float32Array(xVals.length),offValsLength=offVals.length,sValsLength=sVals.length,varValsLength=varVals.length,mValsLength=mVals.length,offi=0,mi=0,si=0,vi=0;for(let i=0;i<xVals.length;++i)outVals[i]=offVals[offi++]+(xVals[i]-mVals[mi++])*sVals[si++]/Math.sqrt(varVals[vi++]+varianceEpsilon),offi>=offValsLength&&(offi=0),mi>=mValsLength&&(mi=0),si>=sValsLength&&(si=0),vi>=varValsLength&&(vi=0);return backend2.makeTensorInfo(x.shape,x.dtype,outVals)}var batchNormConfig={kernelName:FusedBatchNorm,backendName:"cpu",kernelFunc:batchNorm2};function batchToSpaceND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;assertNotComplex([x],"batchToSpaceND");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),xReshaped=reshape3({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),xTransposed=transpose2({inputs:{x:xReshaped},backend:backend2,attrs:{perm:permuted}}),xTransposedReshaped=reshape3({inputs:{x:xTransposed},backend:backend2,attrs:{shape:reshapedPermuted}}),result=slice2({inputs:{x:xTransposedReshaped},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return backend2.disposeIntermediateTensorInfo(xReshaped),backend2.disposeIntermediateTensorInfo(xTransposed),backend2.disposeIntermediateTensorInfo(xTransposedReshaped),result}var batchToSpaceNDConfig={kernelName:BatchToSpaceND,backendName:"cpu",kernelFunc:batchToSpaceND2};function bincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig={kernelName:Bincount,backendName:"cpu",kernelFunc:bincount2};var clip=unaryKernelFunc(ClipByValue,(xi,attrs)=>{let clipAttrs=attrs;return xi>clipAttrs.clipValueMax?clipAttrs.clipValueMax:xi<clipAttrs.clipValueMin?clipAttrs.clipValueMin:xi}),clipConfig={kernelName:ClipByValue,backendName:"cpu",kernelFunc:clip};var complexAbs=args=>{let{x}=args.inputs,cpuBackend=args.backend,resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),complexVals=cpuBackend.data.get(x.dataId),real4=complexVals.complexTensorInfos.real,imag4=complexVals.complexTensorInfos.imag,realVals=cpuBackend.data.get(real4.dataId).values,imagVals=cpuBackend.data.get(imag4.dataId).values;for(let i=0;i<realVals.length;i++){let real5=realVals[i],imag5=imagVals[i];resultValues[i]=Math.hypot(real5,imag5)}return cpuBackend.makeOutput(resultValues,x.shape,"float32")},complexAbsConfig={kernelName:ComplexAbs,backendName:"cpu",kernelFunc:complexAbs};function imag2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,imag4=backend2.data.get(input2.dataId).complexTensorInfos.imag,imagVal=backend2.data.get(imag4.dataId).values;return backend2.makeTensorInfo(imag4.shape,imag4.dtype,imagVal)}var imagConfig={kernelName:Imag,backendName:"cpu",kernelFunc:imag2};function concat2(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity2({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,$axis),$inputs[0].dtype==="complex64"){let reals=$inputs.map(t=>real2({inputs:{input:t},backend:backend2})),imags=$inputs.map(t=>imag2({inputs:{input:t},backend:backend2})),realConcated=concat2({inputs:reals,backend:backend2,attrs:{axis:$axis}}),imagConcated=concat2({inputs:imags,backend:backend2,attrs:{axis:$axis}}),result=complex2({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result}let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice($axis));return reshape3({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.data.get(t.dataId).values,shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),$axis),outInfo=backend2.makeTensorInfo(finalOutShape,inputs[0].dtype,outVals);return inputs2D.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}var concatConfig={kernelName:Concat,backendName:"cpu",kernelFunc:concat2};function conv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"conv2d");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,padLeft=convInfo.padInfo.left,padTop=convInfo.padInfo.top,isChannelsLast=convInfo.dataFormat==="channelsLast",y=new TensorBuffer(convInfo.outShape,x.dtype),xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),xBatchStride=xStrides[0],xRowStride=isChannelsLast?xStrides[1]:xStrides[2],xColStride=isChannelsLast?xStrides[2]:1,xChannelStride=isChannelsLast?1:xStrides[1],yBatchStride=y.strides[0],yRowStride=isChannelsLast?y.strides[1]:y.strides[2],yColStride=isChannelsLast?y.strides[2]:1,yChannelStride=isChannelsLast?1:y.strides[1],xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xBatchStride,yOffset1=b*yBatchStride;for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*yRowStride,xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(!(xR<0||xR>=convInfo.inHeight)){let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xRowStride;for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*yColStride,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(!(xC<0||xC>=convInfo.inWidth)){let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*xColStride,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1*xChannelStride];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset3+d2*yChannelStride]+=xVal*wVals[wOffset3+d2];wOffset3+=convInfo.outChannels}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,yVals)}var conv2DConfig={kernelName:Conv2D,backendName:"cpu",kernelFunc:conv2D};function conv2DBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"conv2dBackpropFilter");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,isChannelsLast=convInfo.dataFormat==="channelsLast",dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,xVals=backend2.data.get(x.dataId).values,dyVals=backend2.data.get(dy.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d1=0;d1<convInfo.inChannels;++d1)for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;isChannelsLast?dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2):dotProd+=xBuf.get(b,d1,xR,xC)*dyBuf.get(b,d2,yR,yC)}}dW.set(dotProd,wR,wC,d1,d2)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var conv2DBackpropFilterConfig={kernelName:Conv2DBackpropFilter,backendName:"cpu",kernelFunc:conv2DBackpropFilter2};function conv2DBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{inputShape,strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;assertNotComplex([dy,filter],"conv2dBackpropInput");let filterStrides=util_exports.computeStrides(filter.shape),dyStrides=util_exports.computeStrides(dy.shape),$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,dyValues=backend2.data.get(dy.dataId).values,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo;$dataFormat=convInfo.dataFormat;let topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=$dataFormat==="channelsLast",xBatchStride=dx.strides[0],xRowStride=isChannelsLast?dx.strides[1]:dx.strides[2],xColStride=isChannelsLast?dx.strides[2]:1,xChannelStride=isChannelsLast?1:dx.strides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1];for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=yBatchStride*b+yRowStride*yR+yColStride*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+yChannelStride*d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}let dxOffset=xBatchStride*b+xRowStride*xR+xColStride*xC+xChannelStride*d1;dxValues[dxOffset]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv2DBackpropInputConfig={kernelName:Conv2DBackpropInput,backendName:"cpu",kernelFunc:conv2DBackpropInput2};function conv3D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs;assertNotComplex([x,filter],"conv3d");let convInfo=backend_util_exports.computeConv3DInfo(x.shape,filter.shape,strides,dilations,pad3),{filterDepth,filterHeight,filterWidth,dilationDepth,dilationHeight,dilationWidth,padInfo}=convInfo,padFront=padInfo.front,padLeft=padInfo.left,padTop=padInfo.top,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values,xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yF=0;yF<convInfo.outDepth;++yF){let yOffset2=yOffset1+yF*y.strides[1],xFCorner=yF*convInfo.strideDepth-padFront;for(let wF=0;wF<filterDepth;++wF){let xF=xFCorner+wF*dilationDepth;if(!(xF<0||xF>=convInfo.inDepth)){let wOffset1=wF*filterStrides[0],xOffset2=xOffset1+xF*xStrides[1];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset3=yOffset2+yR*y.strides[2],xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(!(xR<0||xR>=convInfo.inHeight)){let wOffset2=wOffset1+wR*filterStrides[1],xOffset3=xOffset2+xR*xStrides[2];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset4=yOffset3+yC*convInfo.outChannels,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(!(xC<0||xC>=convInfo.inWidth)){let wOffset3=wOffset2+wC*filterStrides[2],xOffset4=xOffset3+xC*convInfo.inChannels,wOffset4=wOffset3;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset4+d1];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset4+d2]+=xVal*wVals[wOffset4+d2];wOffset4+=convInfo.outChannels}}}}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var conv3DConfig={kernelName:Conv3D,backendName:"cpu",kernelFunc:conv3D};function conv3DBackpropFilterV2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,filterShape}=attrs;assertNotComplex([x,dy],"conv3dBackpropFilterV2");let xStrides=util_exports.computeStrides(x.shape),dyStrides=util_exports.computeStrides(dy.shape),convInfo=backend_util_exports.computeConv3DInfo(x.shape,filterShape,strides,1,pad3),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dw=new TensorBuffer(convInfo.filterShape,"float32"),dwValues=dw.values,[dwS0,dwS1,dwS2,dwS3]=dw.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,xValues=backend2.data.get(x.dataId).values,[xS0,xS1,xS2,xS3]=xStrides,frontPad=convInfo.padInfo.front,leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top;for(let wF=0;wF<filterDepth;++wF){let yFMin=Math.max(0,Math.ceil((frontPad-wF)/strideDepth)),yFMax=Math.min(convInfo.outDepth,(convInfo.inDepth+frontPad-wF)/strideDepth),wOffset1=wF*dwS0;for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight),wOffset2=wR*dwS1+wOffset1;for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth),wOffset3=wC*dwS2+wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let wOffset4=d1*dwS3+wOffset3;for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xS0,yOffset1=b*dyS0;for(let yF=yFMin;yF<yFMax;++yF){let xOffset2=(wF+yF*strideDepth-frontPad)*xS1+xOffset1,yOffset2=yF*dyS1+yOffset1;for(let yR=yRMin;yR<yRMax;++yR){let xOffset3=(wR+yR*strideHeight-topPad)*xS2+xOffset2,yOffset3=yR*dyS2+yOffset2;for(let yC=yCMin;yC<yCMax;++yC){let xOffset4=(wC+yC*strideWidth-leftPad)*xS3+xOffset3,yOffset4=yC*dyS3+yOffset3;dotProd+=xValues[xOffset4+d1]*dyValues[yOffset4+d2]}}}}dwValues[wOffset4+d2]=dotProd}}}}}return backend2.makeTensorInfo(dw.shape,dw.dtype,dw.values)}var conv3DBackpropFilterV2Config={kernelName:Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:conv3DBackpropFilterV2};function conv3DBackpropInputV2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{pad:pad3,strides,inputShape}=attrs;assertNotComplex([dy],"conv3dBackpropInputV2");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv3DInfo(inputShape,filter.shape,strides,1,pad3),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2,dxS3]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2,fltS3]=filterStrides,{batchSize,filterDepth,filterHeight,filterWidth,inChannels,inDepth,inHeight,inWidth,outChannels,outDepth,outHeight,outWidth,strideDepth,strideHeight,strideWidth}=convInfo,frontPad=filterDepth-1-convInfo.padInfo.front,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xF=0;xF<inDepth;++xF){let xFCorner=xF-frontPad,xFMin=Math.max(0,Math.ceil(xFCorner/strideDepth)),yFMax=Math.min(outDepth,(filterDepth+xFCorner)/strideDepth);for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yF=xFMin;yF<yFMax;++yF){let wF=yF*strideDepth-xFCorner;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yF+dyS2*yR+dyS3*yC,fltOffset=fltS0*(filterDepth-1-wF)+fltS1*(filterHeight-1-wR)+fltS2*(filterWidth-1-wC)+fltS3*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}}dxValues[dxS0*b+dxS1*xF+dxS2*xR+dxS3*xC+d1]=dotProd}}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv3DBackpropInputV2Config={kernelName:Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:conv3DBackpropInputV2};var cos2=unaryKernelFunc(Cos,xi=>Math.cos(xi)),cosConfig={kernelName:Cos,backendName:"cpu",kernelFunc:cos2};var cosh2=unaryKernelFunc(Cosh,xi=>Math.cosh(xi)),coshConfig={kernelName:Cosh,backendName:"cpu",kernelFunc:cosh2};function cropAndResize2(args){let{inputs,backend:backend2,attrs}=args,{image:image3,boxes,boxInd}=inputs,{cropSize,method,extrapolationValue}=attrs,[batch,imageHeight,imageWidth,numChannels]=image3.shape,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,output=buffer([numBoxes,cropHeight,cropWidth,numChannels],"float32"),boxVals=backend2.data.get(boxes.dataId).values,boxIndVals=backend2.data.get(boxInd.dataId).values,imageVals=backend2.data.get(image3.dataId).values,inStride=util_exports.computeStrides(image3.shape),outStride=util_exports.computeStrides(output.shape);for(let b=0;b<numBoxes;b++){let startInd=b*4,y1=boxVals[startInd],x1=boxVals[startInd+1],y2=boxVals[startInd+2],x2=boxVals[startInd+3],bInd=boxIndVals[b];if(!(bInd>=batch)){let heightScale=cropHeight>1?(y2-y1)*(imageHeight-1)/(cropHeight-1):0,widthScale=cropWidth>1?(x2-x1)*(imageWidth-1)/(cropWidth-1):0;for(let y=0;y<cropHeight;y++){let yInd=cropHeight>1?y1*(imageHeight-1)+y*heightScale:.5*(y1+y2)*(imageHeight-1);if(yInd<0||yInd>imageHeight-1){for(let x=0;x<cropWidth;x++)for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}if(method==="bilinear"){let topInd=Math.floor(yInd),bottomInd=Math.ceil(yInd),yLerp=yInd-topInd;for(let x=0;x<cropWidth;x++){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let leftInd=Math.floor(xInd),rightInd=Math.ceil(xInd),xLerp=xInd-leftInd;for(let c=0;c<numChannels;c++){let ind=c+leftInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0],topLeft=imageVals[ind];ind=c+rightInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];let topRight=imageVals[ind];ind=c+leftInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomLeft=imageVals[ind];ind=c+rightInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomRight=imageVals[ind],top=topLeft+(topRight-topLeft)*xLerp,bottom=bottomLeft+(bottomRight-bottomLeft)*xLerp;ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0],output.values[ind]=top+(bottom-top)*yLerp}}}else for(let x=0;x<cropWidth;++x){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let closestX=Math.round(xInd),closestY=Math.round(yInd);for(let c=0;c<numChannels;c++){let inInd=c+closestX*inStride[2]+closestY*inStride[1]+bInd*inStride[0],outInd=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[outInd]=imageVals[inInd]}}}}}return backend2.makeTensorInfo(output.shape,output.dtype,output.values)}var cropAndResizeConfig={kernelName:CropAndResize,backendName:"cpu",kernelFunc:cropAndResize2};function cumsum2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs;assertNotComplex(x,"cumsum");let permutation=backend_util_exports.getAxesPermutation([axis],x.shape.length),$x=x;permutation!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,x.shape.length)[0];if(permutedAxis!==$x.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length-1} but got axis=${permutedAxis}`);let resultDtype=upcastType($x.dtype,"int32"),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape),resultDtype),aVals=backend2.data.get($x.dataId).values,finalDim=$x.shape[$x.shape.length-1],indexAdjuster=reverse5?(i,j)=>i+finalDim-j-1:(i,j)=>i+j;for(let i=0;i<aVals.length;i+=finalDim)for(let j=0;j<finalDim;j++){let idx=indexAdjuster(i,j);if(j===0)vals[idx]=exclusive?0:aVals[idx];else{let prevIdx=indexAdjuster(i,j-1);vals[idx]=exclusive?aVals[prevIdx]+vals[prevIdx]:aVals[idx]+vals[prevIdx]}}let result=backend2.makeTensorInfo($x.shape,resultDtype,vals);if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose2({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo($x),reverseTransposedResult}return result}var cumsumConfig={kernelName:Cumsum,backendName:"cpu",kernelFunc:cumsum2};function denseBincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig={kernelName:DenseBincount,backendName:"cpu",kernelFunc:denseBincount2};function depthToSpace2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(dataFormat==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`),util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=x.shape[1],inputWidth=x.shape[2],inputDepth=x.shape[3],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),xValues=backend2.data.get(x.dataId).values,result=new Float32Array(batchSize*outputHeight*outputWidth*outputDepth),outputIdx=0;for(let b=0;b<batchSize;++b)for(let h=0;h<outputHeight;++h){let inH=Math.floor(h/blockSize),offsetH=h%blockSize;for(let w=0;w<outputWidth;++w){let inW=Math.floor(w/blockSize),offsetW=w%blockSize,offsetD=(offsetH*blockSize+offsetW)*outputDepth;for(let d=0;d<outputDepth;++d){let inputIdx=d+offsetD+inputDepth*(inW+inputWidth*(inH+inputHeight*b));result[outputIdx++]=xValues[inputIdx]}}}return backend2.makeTensorInfo([batchSize,outputHeight,outputWidth,outputDepth],x.dtype,result)}var depthToSpaceConfig={kernelName:DepthToSpace,backendName:"cpu",kernelFunc:depthToSpace2};function depthwiseConv2dNative(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"depthwiseConv2DNative");let xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),{filterHeight,filterWidth,dilationHeight,dilationWidth,padInfo}=convInfo,padLeft=padInfo.left,padTop=padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*y.strides[1],xRCorner=yR*convInfo.strideHeight-padLeft;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(!(xR<0||xR>=convInfo.inHeight)){let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xStrides[1];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*y.strides[2],xCCorner=yC*convInfo.strideWidth-padTop;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(!(xC<0||xC>=convInfo.inWidth)){let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*convInfo.inChannels,yOffset4=yOffset3,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1];for(let q=0;q<chMul;++q)yVals[yOffset4+q]+=xVal*wVals[wOffset3+q];yOffset4+=chMul,wOffset3+=chMul}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var depthwiseConv2dNativeConfig={kernelName:DepthwiseConv2dNative,backendName:"cpu",kernelFunc:depthwiseConv2dNative};function depthwiseConv2dNativeBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"depthwiseConv2dNativeBackpropFilter");let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,dilations,pad3,dimRoundingMode,!0),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,xVals=backend2.data.get(x.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyVals=backend2.data.get(dy.dataId).values,dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d2=0;d2<convInfo.outChannels;++d2){let d1=Math.trunc(d2/chMul),dm=d2%chMul,dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2)}}dW.set(dotProd,wR,wC,d1,dm)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var depthwiseConv2dNativeBackpropFilterConfig={kernelName:DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropFilter2};function depthwiseConv2dNativeBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,inputShape}=attrs;assertNotComplex([dy,filter],"depthwiseConv2DNativeBackpropInput");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,chMul=outChannels/inChannels;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yR+dyS2*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let dm=0;dm<chMul;++dm){let d2=d1*chMul+dm,pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+dm];dotProd+=pixel*weight}}}dxValues[dxS0*b+dxS1*xR+dxS2*xC+d1]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var depthwiseConv2dNativeBackpropInputConfig={kernelName:DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropInput2};function diag2(args){let{inputs,backend:backend2}=args,{x}=inputs,xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outBuf=buffer([xSize,xSize],x.dtype),vals=outBuf.values;for(let i=0;i<xVals.length;i++)vals[i*xSize+i]=xVals[i];let outShape=[...x.shape,...x.shape];return backend2.makeTensorInfo(outShape,outBuf.dtype,outBuf.values)}var diagConfig={kernelName:Diag,backendName:"cpu",kernelFunc:diag2};var dilation2dConfig={kernelName:Dilation2D,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,xVals=cpuBackend.data.get(x.dataId).values,xRank=x.shape.length,filterVals=cpuBackend.data.get(filter.dataId).values,filterRank=filter.shape.length,{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations),outSize=util_exports.sizeFromShape(outShape),outRank=outShape.length,outputVals=util_exports.getArrayFromDType(x.dtype,outSize);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let xIndex=util_exports.locToIndex([b,hIn,wIn,d],xRank,util_exports.computeStrides(x.shape)),filterIndex=util_exports.locToIndex([h,w,d],filterRank,util_exports.computeStrides(filter.shape)),val=xVals[xIndex]+filterVals[filterIndex];val>curVal&&(curVal=val)}}}let outputIndex=util_exports.locToIndex([b,hOut,wOut,d],outRank,util_exports.computeStrides(outShape));outputVals[outputIndex]=curVal}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(outputVals,x.dtype),outShape,x.dtype),shape:outShape,dtype:x.dtype}}};var dilation2dBackpropFilterConfig={kernelName:Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(filter.shape,filter.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hMax=0,wMax=0;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hMax=h,wMax=w)}}}gradients[hMax][wMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),filter.shape,filter.dtype),shape:filter.shape,dtype:filter.dtype}}};var dilation2dBackpropInputConfig={kernelName:Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(x.shape,x.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hInMax=hBeg<0?0:hBeg,wInMax=wBeg<0?0:wBeg;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hInMax=hIn,wInMax=wIn)}}}gradients[b][hInMax][wInMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}};function eluGrad(args){let{inputs,backend:backend2}=args,{dy,y}=inputs;assertNotComplex([dy,y],"eluGrad");let resultValues=new Float32Array(util_exports.sizeFromShape(y.shape)),values=backend2.data.get(y.dataId).values,dyValues=backend2.data.get(dy.dataId).values;for(let i=0;i<values.length;++i){let v=values[i];v>=1?resultValues[i]=dyValues[i]:resultValues[i]=dyValues[i]*(v+1)}return backend2.makeTensorInfo(y.shape,"float32",resultValues)}var eluGradConfig2={kernelName:EluGrad,backendName:"cpu",kernelFunc:eluGrad};var equalImpl=createSimpleBinaryKernelImpl((a,b)=>a===b?1:0),equal2=binaryKernelFunc(Equal,equalImpl,null,"bool"),equalConfig={kernelName:Equal,backendName:"cpu",kernelFunc:equal2};var p=backend_util_exports.ERF_P,a1=backend_util_exports.ERF_A1,a2=backend_util_exports.ERF_A2,a3=backend_util_exports.ERF_A3,a4=backend_util_exports.ERF_A4,a5=backend_util_exports.ERF_A5,erf2=unaryKernelFunc(Erf,xi=>{let sign4=Math.sign(xi),v=Math.abs(xi),t=1/(1+p*v);return sign4*(1-((((a5*t+a4)*t+a3)*t+a2)*t+a1)*t*Math.exp(-v*v))}),erfConfig={kernelName:Erf,backendName:"cpu",kernelFunc:erf2};function expandDims3(args){let{inputs,backend:backend2,attrs}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig={kernelName:ExpandDims,backendName:"cpu",kernelFunc:expandDims3};var realDivImpl=createSimpleBinaryKernelImpl((a,b)=>a/b),div2=binaryKernelFunc(RealDiv,realDivImpl),realDivConfig={kernelName:RealDiv,backendName:"cpu",kernelFunc:div2};function fftBatch(input2,inverse,cpuBackend){let inputShape=input2.shape,batch=inputShape[0],innerDim=inputShape[1],inputVals=cpuBackend.data.get(input2.dataId),real2D=inputVals.complexTensorInfos.real,imag2D=inputVals.complexTensorInfos.imag,resultShape=[batch,innerDim],resultSize=util_exports.sizeFromShape(resultShape),resultReal=util_exports.getTypedArrayFromDType("float32",resultSize),resultImag=util_exports.getTypedArrayFromDType("float32",resultSize);for(let b=0;b<batch;b++){let r=slice2({inputs:{x:real2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),i=slice2({inputs:{x:imag2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),input3=complex2({inputs:{real:r,imag:i},backend:cpuBackend}),{real:real4,imag:imag4}=fftImpl(input3,inverse,cpuBackend),res=backend_util_exports.mergeRealAndImagArrays(real4,imag4);for(let d=0;d<innerDim;d++){let c=backend_util_exports.getComplexWithIndex(res,d);resultReal[b*innerDim+d]=c.real,resultImag[b*innerDim+d]=c.imag}cpuBackend.disposeIntermediateTensorInfo(r),cpuBackend.disposeIntermediateTensorInfo(i),cpuBackend.disposeIntermediateTensorInfo(input3)}let $realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultReal),$imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultImag),result=complex2({inputs:{real:$realInfo,imag:$imagInfo},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($realInfo),cpuBackend.disposeIntermediateTensorInfo($imagInfo),result}function fftImpl(input2,inverse,cpuBackend){let inputSize=util_exports.sizeFromShape(input2.shape),inputVals=cpuBackend.data.get(input2.dataId),realVals=cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values,imagVals=cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;if(isExponentOf2(inputSize)){let result=fftRadix2(realVals,imagVals,inputSize,inverse,cpuBackend),resultShape=[input2.shape[0],input2.shape[1]];if(inverse){let realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.real),imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.imag),sizeInfo=cpuBackend.makeTensorInfo([],"float32",util_exports.createScalarValue(inputSize,"float32")),sizeInfoCopy=identity2({inputs:{x:sizeInfo},backend:cpuBackend}),divRealInfo=realDivConfig.kernelFunc({inputs:{a:realInfo,b:sizeInfo},backend:cpuBackend}),divImagInfo=realDivConfig.kernelFunc({inputs:{a:imagInfo,b:sizeInfoCopy},backend:cpuBackend}),divRealVals=cpuBackend.data.get(divRealInfo.dataId).values,divImagVals=cpuBackend.data.get(divImagInfo.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(realInfo),cpuBackend.disposeIntermediateTensorInfo(imagInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy),cpuBackend.disposeIntermediateTensorInfo(divRealInfo),cpuBackend.disposeIntermediateTensorInfo(divImagInfo),{real:divRealVals,imag:divImagVals}}return result}else{let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),rawOutput=fourierTransformByMatmul(data,inputSize,inverse);return backend_util_exports.splitRealAndImagArrays(rawOutput)}}function isExponentOf2(size){return(size&size-1)===0}function fftRadix2(realVals,imagVals,size,inverse,cpuBackend){if(size===1)return{real:realVals,imag:imagVals};let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),half=size/2,evenComplex=backend_util_exports.complexWithEvenIndex(data),evenRealVals=evenComplex.real,evenImagVals=evenComplex.imag,evenShape=[evenRealVals.length],evenRealInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenRealVals),evenImagInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenImagVals),evenTensorInfo=complex2({inputs:{real:evenRealInfo,imag:evenImagInfo},backend:cpuBackend}),oddComplex=backend_util_exports.complexWithOddIndex(data),oddRealVals=oddComplex.real,oddImagVals=oddComplex.imag,oddShape=[oddRealVals.length],oddRealInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddRealVals),oddImagInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddImagVals),oddTensorInfo=complex2({inputs:{real:oddRealInfo,imag:oddImagInfo},backend:cpuBackend}),$evenComplex=fftRadix2(evenRealVals,evenImagVals,half,inverse,cpuBackend),$evenRealVals=$evenComplex.real,$evenImagVals=$evenComplex.imag,$evenShape=[$evenRealVals.length],$evenRealInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenRealVals),$evenImagInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenImagVals),$evenTensorInfo=complex2({inputs:{real:$evenRealInfo,imag:$evenImagInfo},backend:cpuBackend}),$oddComplex=fftRadix2(oddRealVals,oddImagVals,half,inverse,cpuBackend),$oddRealVals=$oddComplex.real,$oddImagVals=$oddComplex.imag,$oddShape=[$oddRealVals.length],$oddRealInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddRealVals),$oddImagInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddImagVals),$oddTensorInfo=complex2({inputs:{real:$oddRealInfo,imag:$oddImagInfo},backend:cpuBackend}),e=backend_util_exports.exponents(size,inverse),eShape=[e.real.length],eRealInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.real),eImagInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.imag),complexInfo=complex2({inputs:{real:eRealInfo,imag:eImagInfo},backend:cpuBackend}),exponentInfo=multiply2({inputs:{a:complexInfo,b:$oddTensorInfo},backend:cpuBackend}),addPart=add4({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),subPart=sub2({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),addPartReal=real2({inputs:{input:addPart},backend:cpuBackend}),subPartReal=real2({inputs:{input:subPart},backend:cpuBackend}),addPartImag=imag2({inputs:{input:addPart},backend:cpuBackend}),subPartImag=imag2({inputs:{input:subPart},backend:cpuBackend}),$real=concat2({inputs:[addPartReal,subPartReal],backend:cpuBackend,attrs:{axis:0}}),$imag=concat2({inputs:[addPartImag,subPartImag],backend:cpuBackend,attrs:{axis:0}}),$realVals=cpuBackend.data.get($real.dataId).values,$imagVals=cpuBackend.data.get($imag.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(evenRealInfo),cpuBackend.disposeIntermediateTensorInfo(evenImagInfo),cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo(oddRealInfo),cpuBackend.disposeIntermediateTensorInfo(oddImagInfo),cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo($evenRealInfo),cpuBackend.disposeIntermediateTensorInfo($evenImagInfo),cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo($oddRealInfo),cpuBackend.disposeIntermediateTensorInfo($oddImagInfo),cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo(eRealInfo),cpuBackend.disposeIntermediateTensorInfo(eImagInfo),cpuBackend.disposeIntermediateTensorInfo(complexInfo),cpuBackend.disposeIntermediateTensorInfo(exponentInfo),cpuBackend.disposeIntermediateTensorInfo(addPart),cpuBackend.disposeIntermediateTensorInfo(subPart),cpuBackend.disposeIntermediateTensorInfo(addPartReal),cpuBackend.disposeIntermediateTensorInfo(addPartImag),cpuBackend.disposeIntermediateTensorInfo(subPartReal),cpuBackend.disposeIntermediateTensorInfo(subPartImag),cpuBackend.disposeIntermediateTensorInfo($real),cpuBackend.disposeIntermediateTensorInfo($imag),{real:$realVals,imag:$imagVals}}function fourierTransformByMatmul(data,size,inverse){let ret=new Float32Array(size*2);for(let r=0;r<size;r++){let real4=0,imag4=0;for(let c=0;c<size;c++){let e=backend_util_exports.exponent(r*c,size,inverse),term=backend_util_exports.getComplexWithIndex(data,c);real4+=term.real*e.real-term.imag*e.imag,imag4+=term.real*e.imag+term.imag*e.real}inverse&&(real4/=size,imag4/=size),backend_util_exports.assignToTypedArray(ret,real4,imag4,r)}return ret}function fft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!1,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var fftConfig={kernelName:FFT,backendName:"cpu",kernelFunc:fft2};function fill2(args){let{backend:backend2,attrs}=args,{shape,value,dtype}=attrs,$dtype=dtype||util_exports.inferDtype(value),values=util_exports.getArrayFromDType($dtype,util_exports.sizeFromShape(shape));return fillValues(values,value,$dtype),backend2.makeTensorInfo(shape,$dtype,values)}var fillConfig={kernelName:Fill,backendName:"cpu",kernelFunc:fill2};function fillValues(values,value,dtype){values.fill(value)}var flipLeftRightConfig={kernelName:FlipLeftRight,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let x=[batch,row,col,channel][2],coordX=Math.round(imageWidth-x),outIdx=batchOffset+rowOffset+colOffset+channel,outputValue=imageVals[outIdx];if(coordX>=0&&coordX<imageWidth){let rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}};var floorDivImpl=createSimpleBinaryKernelImpl((a,b)=>Math.floor(a/b)),floorDiv2=binaryKernelFunc(FloorDiv,floorDivImpl,null,"int32"),floorDivConfig={kernelName:FloorDiv,backendName:"cpu",kernelFunc:floorDiv2};function fusedConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=conv2D({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let resultOld=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(resultOld)}if(activation2){let resultOld=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(resultOld)}return result}var fusedConv2DConfig={kernelName:FusedConv2D,backendName:"cpu",kernelFunc:fusedConv2D};function fusedDepthwiseConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=depthwiseConv2dNative({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let oldResult=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(oldResult)}if(activation2){let oldResult=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(oldResult)}return result}var fusedDepthwiseConv2DConfig={kernelName:FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:fusedDepthwiseConv2D};function gatherNd(args){let{inputs,backend:backend2}=args,{params,indices}=inputs,paramsSize=util_exports.sizeFromShape(params.shape),indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(params,indices);if(numSlices===0)return backend2.makeTensorInfo(resultShape,params.dtype,[]);let outBuf=buffer([numSlices,sliceSize],params.dtype),indicesData=backend2.data.get(indices.dataId).values,paramsData=backend2.data.get(params.dataId).values;for(let i=0;i<numSlices;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];flattenIndex+=dim*strides[j],index.push(dim)}if(flattenIndex<0||flattenIndex>=paramsSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${params.shape}`);for(let k=0;k<sliceSize;k++)outBuf.values[i*sliceSize+k]=paramsData[flattenIndex*sliceSize+k]}return backend2.makeTensorInfo(resultShape,outBuf.dtype,outBuf.values)}var gatherNdConfig={kernelName:GatherNd,backendName:"cpu",kernelFunc:gatherNd};function gatherV2(args){let{inputs,backend:backend2,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs;assertNotComplex([x,indices],"gatherV2");let $batchDims=batchDims;batchDims==null&&($batchDims=0);let indicesSize=util_exports.sizeFromShape(indices.shape),parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,$batchDims),flattenX=reshape3({inputs:{x},backend:backend2,attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]}}),flattenIndex=reshape3({inputs:{x:indices},backend:backend2,attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]}}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],indicesBuf=backend2.bufferSync(flattenIndex),xBuf=backend2.bufferSync(flattenX),outBuf=gatherV2Impl(xBuf,indicesBuf,flattenOutputShape);return backend2.disposeIntermediateTensorInfo(flattenX),backend2.disposeIntermediateTensorInfo(flattenIndex),backend2.makeTensorInfo(shapeInfo.outputShape,outBuf.dtype,outBuf.values)}var gatherV2Config={kernelName:GatherV2,backendName:"cpu",kernelFunc:gatherV2};var greaterEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a>=b?1:0),greaterEqual2=binaryKernelFunc(GreaterEqual,greaterEqualImpl,null,"bool"),greaterEqualConfig={kernelName:GreaterEqual,backendName:"cpu",kernelFunc:greaterEqual2};function ifft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!0,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var ifftConfig={kernelName:IFFT,backendName:"cpu",kernelFunc:ifft2};var isFinite3=unaryKernelFunc(IsFinite,xi=>Number.isFinite(xi)?1:0,"bool"),isFiniteConfig={kernelName:IsFinite,backendName:"cpu",kernelFunc:isFinite3};var isInf2=unaryKernelFunc(IsInf,xi=>Math.abs(xi)===Infinity?1:0,"bool"),isInfConfig={kernelName:IsInf,backendName:"cpu",kernelFunc:isInf2};var isNaN3=unaryKernelFunc(IsNan,xi=>Number.isNaN(xi)?1:0,"bool"),isNaNConfig={kernelName:IsNan,backendName:"cpu",kernelFunc:isNaN3};var lessEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a<=b?1:0),lessEqual2=binaryKernelFunc(LessEqual,lessEqualImpl,null,"bool"),lessEqualConfig={kernelName:LessEqual,backendName:"cpu",kernelFunc:lessEqual2};function linSpace(args){let{backend:backend2,attrs}=args,{start,stop,num}=attrs,outVals=linSpaceImpl(start,stop,num);return backend2.makeTensorInfo([outVals.length],"float32",outVals)}var linSpaceConfig={kernelName:LinSpace,backendName:"cpu",kernelFunc:linSpace};var log1p2=unaryKernelFunc(Log1p,xi=>Math.log1p(xi)),log1pConfig={kernelName:Log1p,backendName:"cpu",kernelFunc:log1p2};var logicalAndImpl=createSimpleBinaryKernelImpl((a,b)=>a&&b),logicalAnd2=binaryKernelFunc(LogicalAnd,logicalAndImpl,null,"bool"),logicalAndConfig={kernelName:LogicalAnd,backendName:"cpu",kernelFunc:logicalAnd2};var logicalNot2=unaryKernelFunc(LogicalNot,xi=>xi?0:1,"bool"),logicalNotConfig={kernelName:LogicalNot,backendName:"cpu",kernelFunc:logicalNot2};var logicalOrImpl=createSimpleBinaryKernelImpl((a,b)=>a||b),logicalOr2=binaryKernelFunc(LogicalOr,logicalOrImpl,null,"bool"),logicalOrConfig={kernelName:LogicalOr,backendName:"cpu",kernelFunc:logicalOr2};function lRN(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(x,"LRN");let channels=x.shape[3],maxD=channels-1,xValues=backend2.data.get(x.dataId).values,size=util_exports.sizeFromShape(x.shape),result=new Float32Array(size);function sumAcrossChannels(offset){let currentChannel=offset%channels,beginSumOffset=offset-currentChannel+Math.max(0,currentChannel-depthRadius),endSumOffset=offset-currentChannel+Math.min(currentChannel+depthRadius,maxD),sum6=0;for(;beginSumOffset<=endSumOffset;beginSumOffset++){let z=xValues[beginSumOffset];sum6+=z*z}return sum6}for(let offset=0;offset<size;offset++){let sum6=sumAcrossChannels(offset),val=xValues[offset]*Math.pow(bias+alpha*sum6,-beta);result[offset]=val}return backend2.makeTensorInfo(x.shape,x.dtype,result)}var lRNConfig={kernelName:LRN,backendName:"cpu",kernelFunc:lRN};function lRNGrad(args){let{inputs,backend:backend2,attrs}=args,{x,y,dy}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(dy,"LRNGrad");let dySize=util_exports.sizeFromShape(dy.shape),channels=dy.shape[3],dyValues=backend2.data.get(dy.dataId).values,xValues=backend2.data.get(x.dataId).values,yValues=backend2.data.get(y.dataId).values,result=new Float32Array(dySize),size=dySize;for(let offset=0;offset<size;offset++){let currentChannel=offset%channels,depthBegin=offset-currentChannel+Math.max(0,currentChannel-depthRadius),depthEnd=offset-currentChannel+Math.min(channels,currentChannel+depthRadius+1),norm2=0;for(let k=depthBegin;k<depthEnd;k++)norm2+=Math.pow(xValues[k],2);norm2=alpha*norm2+bias;for(let k=depthBegin;k<depthEnd;k++){let dyi=-2*alpha*beta*xValues[k]*yValues[offset]/norm2;offset===k&&(dyi+=Math.pow(norm2,-beta)),dyi*=dyValues[offset],result[k]+=dyi}}return backend2.makeTensorInfo(dy.shape,x.dtype,result)}var lRNGradConfig={kernelName:LRNGrad,backendName:"cpu",kernelFunc:lRNGrad};function max3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reductionIndices,keepDims}=attrs,cpuBackend=backend2,xShape=x.shape,xRank=xShape.length,origAxes=util_exports.parseAxisParam(reductionIndices,xShape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xVals=cpuBackend.data.get(x.dataId).values;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];xVals=transposeImpl(xVals,xShape,x.dtype,permutedAxes,newShape),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xShape=newShape}assertNotComplex(x,"max"),backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),result=maxImpl(xVals,reduceSize,maxOutShape,x.dtype),dataId=cpuBackend.write(result,maxOutShape,x.dtype),outShape=maxOutShape;return keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes)),{dataId,shape:outShape,dtype:x.dtype}}var maxConfig={kernelName:Max,backendName:"cpu",kernelFunc:max3};function maxPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"maxPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"max");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var maxPoolConfig={kernelName:MaxPool,backendName:"cpu",kernelFunc:maxPool2};function maxPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"maxPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"max");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var maxPool3DConfig={kernelName:MaxPool3D,backendName:"cpu",kernelFunc:maxPool3D};function maxPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"maxPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),inputBuf=backend2.bufferSync(input2),maxPosBuf=maxPool3dPositions(inputBuf,convInfo),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(!(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)){let maxPos=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(batch,dyDepth,dyRow,dyCol,channel),curPos=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterWidth+wCol,mask=maxPos===curPos?1:0;mask!==0&&(dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel)*mask)}}}}dx.set(dotProd,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPool3DGradConfig2={kernelName:MaxPool3DGrad,backendName:"cpu",kernelFunc:maxPool3DGrad};function maxPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex([input2,output],"maxPoolGrad");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),xValues=backend2.data.get(x.dataId).values,maxPosBuf=buffer(convInfo.outShape,x.dtype,maxPoolPositions(xValues,x.shape,x.dtype,convInfo).values),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(!(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)){let maxPos=effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(b,dyR,dyC,d),curPos=wR*effectiveFilterWidth+wC,mask=maxPos===curPos?1:0;mask!==0&&(dotProd+=dyBuf.get(b,dyR,dyC,d)*mask)}}}dx.set(dotProd,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPoolGradConfig2={kernelName:MaxPoolGrad,backendName:"cpu",kernelFunc:maxPoolGrad2};function maxPoolWithArgmaxImpl(xValues,xShape,dtype,includeBatchInIndex,convInfo){let strides=util_exports.computeStrides(xShape),maxPools=pool2(xValues,xShape,dtype,strides,convInfo,"max"),maxPositions=maxPoolPositions(xValues,xShape,dtype,convInfo,!0,includeBatchInIndex);return[maxPools.values,maxPositions.values]}var maxPoolWithArgmaxConfig={kernelName:MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{x}=inputs,{filterSize,strides,pad:pad3,includeBatchInIndex}=attrs,cpuBackend=backend2;assertNotComplex(x,"MaxPoolWithArgmax");let values=cpuBackend.data.get(x.dataId).values,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,[1,1],pad3),[pooled,indexes]=maxPoolWithArgmaxImpl(values,x.shape,x.dtype,includeBatchInIndex,convInfo),pooledDataId=cpuBackend.write(pooled,convInfo.outShape,x.dtype),indexesDataId=cpuBackend.write(indexes,convInfo.outShape,x.dtype);return[{dataId:pooledDataId,shape:convInfo.outShape,dtype:x.dtype},{dataId:indexesDataId,shape:convInfo.outShape,dtype:"int32"}]}};function sum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"sum");let $x;x.dtype==="bool"?$x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"int32"}}):$x=identity2({inputs:{x},backend:backend2});let xRank=$x.shape.length,axes=util_exports.parseAxisParam(axis,$x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose2({inputs:{x:$x},backend:backend2,attrs:{perm:permutation}}),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank)),backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,permutedX.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(permutedX.shape,reductionAxes),resultDtype=backend_util_exports.upcastType(permutedX.dtype,"int32"),result=zeros3(backend2,outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=backend2.data.get(result.dataId).values,aVals=backend2.data.get(permutedX.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,sum6=0;for(let j=0;j<reduceSize;++j)sum6+=aVals[offset+j];vals[i]=sum6}if(keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(result.shape,axes),oldResult=result;result=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:newShape}}),backend2.disposeIntermediateTensorInfo(oldResult)}return backend2.disposeIntermediateTensorInfo($x),permutation!=null&&backend2.disposeIntermediateTensorInfo(permutedX),result}var sumConfig={kernelName:Sum,backendName:"cpu",kernelFunc:sum3};function mean2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs,axes=util_exports.parseAxisParam(axis,x.shape),reduceShape=backend_util_exports.computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=util_exports.sizeFromShape(reduceShape),toDispose=[],reduceSizeScalar=backend2.makeTensorInfo([],"float32",new Float32Array([reduceSize]));toDispose.push(reduceSizeScalar);let $x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}});toDispose.push($x);let res=div2({inputs:{a:$x,b:reduceSizeScalar},backend:backend2});toDispose.push(res);let result=sum3({inputs:{x:res},backend:backend2,attrs:{axis,keepDims}});return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var meanConfig={kernelName:Mean,backendName:"cpu",kernelFunc:mean2};function min3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"min");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("min",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value)}vals[i]=min6}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var minConfig={kernelName:Min,backendName:"cpu",kernelFunc:min3};function mirrorPad2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,mode}=attrs;assertNotComplex(x,"mirrorPad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),end=paddings.map((p2,i)=>p2[0]+x.shape[i]),offset=mode==="reflect"?0:1,xVals=backend2.data.get(x.dataId).values,xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);for(let i=0;i<resultSize;i++){let coords2=util_exports.indexToLoc(i,resultRank,resultStrides);for(let i2=0;i2<resultRank;i2++)coords2[i2]<start[i2]?coords2[i2]=start[i2]*2-coords2[i2]-offset:coords2[i2]>=end[i2]&&(coords2[i2]=(end[i2]-1)*2-coords2[i2]+offset);coords2=coords2.map((c,i2)=>c-start[i2]);let inIndex=util_exports.locToIndex(coords2,xRank,xStrides);resVals[i]=xVals[inIndex]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var mirrorPadConfig={kernelName:MirrorPad,backendName:"cpu",kernelFunc:mirrorPad2};var modImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>{let rem=aValue%bValue;return aValue<0&&bValue<0||aValue>=0&&bValue>=0?rem:(rem+bValue)%bValue}),mod2=binaryKernelFunc(Mod,modImpl),modConfig={kernelName:Mod,backendName:"cpu",kernelFunc:mod2},seedrandom4=__toModule(require_seedrandom2());function softmax3(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{dim}=attrs,logitsRank=logits.shape.length,$dim=dim;if($dim===-1&&($dim=logitsRank-1),$dim!==logitsRank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);let axes=util_exports.parseAxisParam([$dim],logits.shape),maxLogit=max3({inputs:{x:logits},backend:backend2,attrs:{reductionIndices:axes,keepDims:!1}}),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),maxLogitReshaped=reshape3({inputs:{x:maxLogit},backend:backend2,attrs:{shape:expandedShape}}),a=sub2({inputs:{a:logits,b:maxLogitReshaped},backend:backend2}),b=exp2({inputs:{x:a},backend:backend2}),sumExp=sum3({inputs:{x:b},backend:backend2,attrs:{axis:axes,keepDims:!1}}),sumReshaped=reshape3({inputs:{x:sumExp},backend:backend2,attrs:{shape:expandedShape}}),result=div2({inputs:{a:b,b:sumReshaped},backend:backend2});return backend2.disposeIntermediateTensorInfo(maxLogit),backend2.disposeIntermediateTensorInfo(maxLogitReshaped),backend2.disposeIntermediateTensorInfo(a),backend2.disposeIntermediateTensorInfo(b),backend2.disposeIntermediateTensorInfo(sumExp),backend2.disposeIntermediateTensorInfo(sumReshaped),result}var softmaxConfig={kernelName:Softmax,backendName:"cpu",kernelFunc:softmax3};function multinomial2(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{numSamples,seed,normalized}=attrs;assertNotComplex(logits,"multinomial");let probabilities=normalized?logits:softmax3({inputs:{logits},backend:backend2,attrs:{dim:-1}}),batchSize=probabilities.shape[0],numEvents=probabilities.shape[1],probVals=backend2.data.get(probabilities.dataId).values,resShape=[batchSize,numSamples],resVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape),"int32");for(let b=0;b<batchSize;++b){let offset=b*numEvents,cdf=new Float32Array(numEvents-1);cdf[0]=probVals[offset];for(let event=1;event<cdf.length;++event)cdf[event]=cdf[event-1]+probVals[offset+event];let random=seedrandom4.alea(seed.toString()),outOffset=b*numSamples;for(let sampleId=0;sampleId<numSamples;++sampleId){let r=random();resVals[outOffset+sampleId]=cdf.length;for(let event=0;event<cdf.length;event++)if(r<cdf[event]){resVals[outOffset+sampleId]=event;break}}}return normalized||backend2.disposeIntermediateTensorInfo(probabilities),backend2.makeTensorInfo(resShape,"int32",resVals)}var multinomialConfig={kernelName:Multinomial,backendName:"cpu",kernelFunc:multinomial2};var nonMaxSuppressionV3Impl2=kernel_impls_exports.nonMaxSuppressionV3Impl;function nonMaxSuppressionV3(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold}=attrs;assertNotComplex(boxes,"NonMaxSuppression");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices}=nonMaxSuppressionV3Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices))}var nonMaxSuppressionV3Config={kernelName:NonMaxSuppressionV3,backendName:"cpu",kernelFunc:nonMaxSuppressionV3};var nonMaxSuppressionV4Impl2=kernel_impls_exports.nonMaxSuppressionV4Impl;function nonMaxSuppressionV4(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs;assertNotComplex(boxes,"NonMaxSuppressionPadded");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([],"int32",new Int32Array([validOutputs]))]}var nonMaxSuppressionV4Config={kernelName:NonMaxSuppressionV4,backendName:"cpu",kernelFunc:nonMaxSuppressionV4};var nonMaxSuppressionV5Impl2=kernel_impls_exports.nonMaxSuppressionV5Impl;function nonMaxSuppressionV5(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs;assertNotComplex(boxes,"NonMaxSuppressionWithScore");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl2(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([selectedScores.length],"float32",new Float32Array(selectedScores))]}var nonMaxSuppressionV5Config={kernelName:NonMaxSuppressionV5,backendName:"cpu",kernelFunc:nonMaxSuppressionV5};function oneHot2(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs;assertNotComplex(indices,"oneHot");let indicesSize=util_exports.sizeFromShape(indices.shape),res=new Float32Array(indicesSize*depth);res.fill(offValue);let indicesVal=backend2.data.get(indices.dataId).values;for(let event=0;event<indicesSize;++event)indicesVal[event]>=0&&indicesVal[event]<depth&&(res[event*depth+indicesVal[event]]=onValue);return backend2.makeTensorInfo([...indices.shape,depth],"int32",res)}var oneHotConfig={kernelName:OneHot,backendName:"cpu",kernelFunc:oneHot2};function zerosLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=zerosLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:0,dtype:x.dtype}})}var zerosLikeConfig={kernelName:ZerosLike,backendName:"cpu",kernelFunc:zerosLike2};function onesLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=onesLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:1,dtype:x.dtype}})}var onesLikeConfig={kernelName:OnesLike,backendName:"cpu",kernelFunc:onesLike2};function pack(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims3({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let intermediateTensorInfos=[],expandedTensors=inputs.map(t=>{let expandedT=expandDims3({inputs:{input:t},backend:backend2,attrs:{dim:axis}});return intermediateTensorInfos.push(expandedT),expandedT}),result=concat2({inputs:expandedTensors,backend:backend2,attrs:{axis}});return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var packConfig={kernelName:Pack,backendName:"cpu",kernelFunc:pack};function padV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,constantValue}=attrs;assertNotComplex(x,"pad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),xVals=backend2.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);constantValue!==0&&resVals.fill(constantValue);for(let i=0;i<xSize;i++){let outCoords=util_exports.indexToLoc(i,xRank,xStrides).map((c,i2)=>c+start[i2]),outIndex=util_exports.locToIndex(outCoords,resultRank,resultStrides);resVals[outIndex]=xVals[i]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var padV2Config={kernelName:PadV2,backendName:"cpu",kernelFunc:padV2};var powImpl=createSimpleBinaryKernelImpl((a,b)=>Math.pow(a,b)),pow2=binaryKernelFunc(Pow,powImpl),powConfig={kernelName:Pow,backendName:"cpu",kernelFunc:pow2};function range3(args){let{backend:backend2,attrs}=args,{start,stop,dtype,step:step4}=attrs,values=rangeImpl(start,stop,step4,dtype);return backend2.makeTensorInfo([values.length],dtype,values)}var rangeConfig={kernelName:Range,backendName:"cpu",kernelFunc:range3};var reciprocal2=unaryKernelFunc(Reciprocal,xi=>1/xi),reciprocalConfig={kernelName:Reciprocal,backendName:"cpu",kernelFunc:reciprocal2};function resizeBilinear2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeBilinear");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,result=new Float32Array(util_exports.sizeFromShape([batch,newHeight,newWidth,numChannels])),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],outputIdx=0,effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];for(let b=0;b<batch;b++)for(let r=0;r<newHeight;r++){let sourceFracRow;halfPixelCenters?sourceFracRow=effectiveRowSizeRatio*(r+.5)-.5:sourceFracRow=effectiveRowSizeRatio*r;let sourceRowFloor=Math.max(0,Math.floor(sourceFracRow)),rowFrac=sourceFracRow-sourceRowFloor,sourceRowCeil=Math.min(oldHeight-1,Math.ceil(sourceFracRow)),topRowOffset=b*imagesStrides[0]+sourceRowFloor*imagesStrides[1],botRowOffset=b*imagesStrides[0]+sourceRowCeil*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol;halfPixelCenters?sourceFracCol=effectiveColSizeRatio*(c+.5)-.5:sourceFracCol=effectiveColSizeRatio*c;let sourceColFloor=Math.max(0,Math.floor(sourceFracCol)),colFrac=sourceFracCol-sourceColFloor,sourceColCeil=Math.min(oldWidth-1,Math.ceil(sourceFracCol)),topLeftOffest=topRowOffset+sourceColFloor*imagesStrides[2],botLeftOffset=botRowOffset+sourceColFloor*imagesStrides[2],topRightOffset=topRowOffset+sourceColCeil*imagesStrides[2],botRightOffest=botRowOffset+sourceColCeil*imagesStrides[2];for(let d=0;d<numChannels;d++){let topLeft=xValues[topLeftOffest+d],bottomLeft=xValues[botLeftOffset+d],topRight=xValues[topRightOffset+d],bottomRight=xValues[botRightOffest+d],top=topLeft+(topRight-topLeft)*colFrac,bottom=bottomLeft+(bottomRight-bottomLeft)*colFrac,newValue=top+(bottom-top)*rowFrac;result[outputIdx++]=newValue}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],"float32",result)}var resizeBilinearConfig={kernelName:ResizeBilinear,backendName:"cpu",kernelFunc:resizeBilinear2};function resizeBilinearGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeBilinearGrad");let imagesStrides=util_exports.computeStrides(images.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],dyValues=backend2.data.get(dy.dataId).values,offset=0;for(let b=0;b<batch;b++){let bOffset=b*imagesStrides[0];for(let r=0;r<yHeight;r++){let dxR=r*heightScale,topDxRIndex=Math.floor(dxR),bottomDxRIndex=Math.min(Math.ceil(dxR),xHeight-1),topDxROffset=bOffset+topDxRIndex*imagesStrides[1],bottomDxROffset=bOffset+bottomDxRIndex*imagesStrides[1],dxRLerp=dxR-topDxRIndex,inverseDxRLerp=1-dxRLerp;for(let c=0;c<yWidth;c++){let dxC=c*widthScale,leftDxCIndex=Math.floor(dxC),rightDxCIndex=Math.min(Math.ceil(dxC),xWidth-1),dxCLerp=dxC-leftDxCIndex,inverseDxCLerp=1-dxCLerp,topLeftRCOffset=topDxROffset+leftDxCIndex*imagesStrides[2],topRightRCOffset=topDxROffset+rightDxCIndex*imagesStrides[2],bottomLeftRCOffset=bottomDxROffset+leftDxCIndex*imagesStrides[2],bottomRightRCOffset=bottomDxROffset+rightDxCIndex*imagesStrides[2],inverseDxRLerpTimesInverseDxCLerp=inverseDxRLerp*inverseDxCLerp,inverseDxRLerpTimesDxCLerp=inverseDxRLerp*dxCLerp,dxRLerpTimesInverseDxCLerp=dxRLerp*inverseDxCLerp,dxRLerpTimesDxCLerp=dxRLerp*dxCLerp;for(let d=0;d<depth;d++){let dyVal=dyValues[offset++];output[topLeftRCOffset+d]+=dyVal*inverseDxRLerpTimesInverseDxCLerp,output[topRightRCOffset+d]+=dyVal*inverseDxRLerpTimesDxCLerp,output[bottomLeftRCOffset+d]+=dyVal*dxRLerpTimesInverseDxCLerp,output[bottomRightRCOffset+d]+=dyVal*dxRLerpTimesDxCLerp}}}}return backend2.makeTensorInfo([batch,xWidth,xHeight,depth],"float32",output)}var resizeBilinearGradConfig2={kernelName:ResizeBilinearGrad,backendName:"cpu",kernelFunc:resizeBilinearGrad};function resizeNearestNeighbor2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeNearestNeighbor");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,output=new Float32Array(batch*newHeight*newWidth*numChannels),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1],outputOffset=0;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<newHeight;r++){let sourceFracRow=halfPixelCenters?effectiveRowSizeRatio*(r+.5):effectiveRowSizeRatio*r,sourceNearestRow=Math.min(oldHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));halfPixelCenters&&(sourceNearestRow=Math.max(0,sourceNearestRow));let rowOffset=batchOffset+sourceNearestRow*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=halfPixelCenters?effectiveColSizeRatio*(c+.5):effectiveColSizeRatio*c,sourceNearestCol=Math.min(oldWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));halfPixelCenters&&(sourceNearestCol=Math.max(0,sourceNearestCol));let colOffset=rowOffset+sourceNearestCol*imagesStrides[2];for(let d=0;d<numChannels;d++){let newVal=xValues[colOffset+d];output[outputOffset++]=newVal}}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],images.dtype,output)}var resizeNearestNeighborConfig={kernelName:ResizeNearestNeighbor,backendName:"cpu",kernelFunc:resizeNearestNeighbor2};function resizeNearestNeighborGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeNearestNeighborGrad");let imagesStrides=util_exports.computeStrides(images.shape),dyStrides=util_exports.computeStrides(dy.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),dyValues=backend2.data.get(dy.dataId).values,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<xHeight;r++){let rowOffset=batchOffset+r*imagesStrides[1],startRLerp=Math.floor(r*invHeightScale),startDyR=Math.floor(startRLerp-winHeight/2);for(let c=0;c<xWidth;c++){let colOffset=rowOffset+c*imagesStrides[2],startCLerp=Math.floor(c*invWidthScale),startDyC=Math.floor(startCLerp-winWidth/2);for(let d=0;d<depth;d++){let accum=0;for(let dyRIndex=0;dyRIndex<winHeight;dyRIndex++){let dyR=dyRIndex+startDyR;if(!(dyR<0||dyR>=yHeight)){let dyROffset=batchOffset+dyR*dyStrides[1],sourceFracRow=dyR*heightScale,sourceNearestRow=Math.min(xHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));if(r===sourceNearestRow)for(let dyCIndex=0;dyCIndex<winWidth;dyCIndex++){let dyC=dyCIndex+startDyC;if(!(dyC<0||dyC>=yWidth)){let dyCOffset=dyROffset+dyC*dyStrides[2],sourceFracCol=dyC*widthScale,sourceNearestCol=Math.min(xWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));c===sourceNearestCol&&(accum+=dyValues[dyCOffset+d])}}}}output[colOffset+d]=accum}}}}return backend2.makeTensorInfo(images.shape,images.dtype,output)}var resizeNearestNeighborGradConfig2={kernelName:ResizeNearestNeighborGrad,backendName:"cpu",kernelFunc:resizeNearestNeighborGrad};function reverse2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs;assertNotComplex(x,"reverse");let xRank=x.shape.length,$dims=util_exports.parseAxisParam(dims,x.shape);if(xRank===0)return identity2({inputs:{x},backend:backend2});let outBuf=new TensorBuffer(x.shape,x.dtype),xBuf=backend2.bufferSync(x);for(let i=0;i<outBuf.size;i++){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.slice();$dims.forEach(d=>inLoc[d]=x.shape[d]-1-inLoc[d]),outBuf.set(xBuf.get(...inLoc),...outLoc)}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var reverseConfig={kernelName:Reverse,backendName:"cpu",kernelFunc:reverse2};var rotateWithOffsetConfig={kernelName:RotateWithOffset,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fullOpacityValue=255,sinFactor=Math.sin(radians),cosFactor=Math.cos(radians),imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],y=coords2[1],coordX=(x-centerX)*cosFactor-(y-centerY)*sinFactor,coordY=(x-centerX)*sinFactor+(y-centerY)*cosFactor;coordX=Math.round(coordX+centerX),coordY=Math.round(coordY+centerY);let outputValue=fillValue;if(typeof fillValue!="number"&&(channel===3?outputValue=fullOpacityValue:outputValue=fillValue[channel]),coordX>=0&&coordX<imageWidth&&coordY>=0&&coordY<imageHeight){let rotatedRowOffset=coordY*(imageWidth*numChannels),rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rotatedRowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}let outIdx=batchOffset+rowOffset+colOffset+channel;output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}};var round3=unaryKernelFunc(Round,xi=>{let base2=Math.floor(xi);return xi-base2<.5?Math.floor(xi):xi-base2>.5?Math.ceil(xi):base2%2===0?base2:base2+1}),roundConfig={kernelName:Round,backendName:"cpu",kernelFunc:round3};function scatterImpl(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices){let flattenShape=[outputSize/sliceSize,sliceSize],indicesData=indices.values,updatesData=updates.values;if(outputSize===0)return buffer(shape,updates.dtype);let outBuf=buffer(flattenShape,updates.dtype);outBuf.values.fill(defaultValue);for(let i=0;i<numUpdates;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];index.push(dim),flattenIndex+=dim*strides[j]}if(flattenIndex<0||flattenIndex>=outputSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${shape}`);for(let k=0;k<sliceSize;k++)sumDupeIndices?outBuf.values[flattenIndex*sliceSize+k]+=updatesData[i*sliceSize+k]:outBuf.values[flattenIndex*sliceSize+k]=updates.rank===0?updatesData[0]:updatesData[i*sliceSize+k]}return outBuf}function scatterNd(args){let{inputs,backend:backend2,attrs}=args,{indices,updates}=inputs,{shape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),sumDupeIndices=!0,indicesBuf=backend2.bufferSync(indices),updatesBuf=backend2.bufferSync(updates),outBuf=scatterImpl(indicesBuf,updatesBuf,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,0,sumDupeIndices);return backend2.makeTensorInfo(shape,outBuf.dtype,outBuf.values)}var scatterNdConfig={kernelName:ScatterNd,backendName:"cpu",kernelFunc:scatterNd};function select(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs;assertNotComplex([condition,t,e],"select");let conditionRank=condition.shape.length,values=backend2.data.get(condition.dataId).values,tValues=backend2.data.get(t.dataId).values,eValues=backend2.data.get(e.dataId).values,resultDtype=upcastType(t.dtype,e.dtype),newValues=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape),resultDtype),index=0,offset=conditionRank===0||conditionRank>1||t.shape.length===1?1:util_exports.sizeFromShape(t.shape.slice(1));for(let i=0;i<values.length;i++)for(let j=0;j<offset;j++)values[i]===1?newValues[index++]=tValues[i]:newValues[index++]=eValues[i];return backend2.makeTensorInfo(t.shape,resultDtype,newValues)}var selectConfig={kernelName:Select,backendName:"cpu",kernelFunc:select};var scaleAlpha=backend_util_exports.SELU_SCALEALPHA,scale=backend_util_exports.SELU_SCALE,selu2=unaryKernelFunc(Selu,xi=>xi>=0?scale*xi:scaleAlpha*(Math.exp(xi)-1)),seluConfig={kernelName:Selu,backendName:"cpu",kernelFunc:selu2};var sigmoid2=unaryKernelFunc(Sigmoid,xi=>1/(1+Math.exp(-xi))),sigmoidConfig={kernelName:Sigmoid,backendName:"cpu",kernelFunc:sigmoid2};var sign2=unaryKernelFunc(Sign,xi=>xi<0?-1:xi>0?1:0),signConfig={kernelName:Sign,backendName:"cpu",kernelFunc:sign2};var sin2=unaryKernelFunc(Sin,xi=>Math.sin(xi)),sinConfig={kernelName:Sin,backendName:"cpu",kernelFunc:sin2};var sinh2=unaryKernelFunc(Sinh,xi=>Math.sinh(xi)),sinhConfig={kernelName:Sinh,backendName:"cpu",kernelFunc:sinh2};var epsilon2=11920928955078125e-23,threshold=Math.log(epsilon2)+2,softplus2=unaryKernelFunc(Softplus,xi=>{let tooLarge=xi>-threshold,tooSmall=xi<threshold,expX=Math.exp(xi),result;return tooSmall?result=expX:tooLarge?result=xi:result=Math.log(1+expX),result}),softplusConfig={kernelName:Softplus,backendName:"cpu",kernelFunc:softplus2};function spaceToBatchND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;assertNotComplex([x],"spaceToBatchND");let prod5=util_exports.sizeFromShape(blockShape),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=padV2Config.kernelFunc({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),paddedXReshaped=reshape3({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose2({inputs:{x:paddedXReshaped},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape3({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return backend2.disposeIntermediateTensorInfo(paddedX),backend2.disposeIntermediateTensorInfo(paddedXReshaped),backend2.disposeIntermediateTensorInfo(paddedXT),result}var spaceToBatchNDConfig={kernelName:SpaceToBatchND,backendName:"cpu",kernelFunc:spaceToBatchND2};function sparseToDense2(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,indicesBuf=backend2.bufferSync(sparseIndices),updatesBuf=backend2.bufferSync(sparseValues),$defaultValue=backend2.data.get(defaultValue.dataId).values[0],outBuf=scatterImpl(indicesBuf,updatesBuf,outputShape,outputSize,sliceSize,numUpdates,sliceRank,strides,$defaultValue,sumDupeIndices);return backend2.makeTensorInfo(outputShape,outBuf.dtype,outBuf.values)}var sparseToDenseConfig={kernelName:SparseToDense,backendName:"cpu",kernelFunc:sparseToDense2};function splitV(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[$axis]=s;let sliceT=slice2({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[$axis]+=s,sliceT})}var splitVConfig={kernelName:SplitV,backendName:"cpu",kernelFunc:splitV};var sqrt2=unaryKernelFunc(Sqrt,xi=>Math.sqrt(xi)),sqrtConfig={kernelName:Sqrt,backendName:"cpu",kernelFunc:sqrt2};var squareConfig={kernelName:Square,backendName:"cpu",kernelFunc:({inputs,backend:backend2})=>{let{x}=inputs,cpuBackend=backend2;assertNotComplex(x,"square");let values=cpuBackend.data.get(x.dataId).values,newValues=new Float32Array(values.length);for(let i=0;i<values.length;++i){let value=values[i];newValues[i]=value*value}return{dataId:cpuBackend.write(newValues,x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}};var step2=unaryKernelFunc(Step,(xi,attrs)=>{let stepAttrs=attrs;return isNaN(xi)?NaN:xi>0?1:stepAttrs.alpha}),stepConfig={kernelName:Step,backendName:"cpu",kernelFunc:step2};function stridedSlice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs;assertNotComplex(x,"stridedSlice");let{nonStrided,$begin,$strides,size,newShape,outShape}=slice_util_exports.sliceInfo(x.shape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask),$x=reshape3({inputs:{x},backend:backend2,attrs:{shape:newShape}}),result;if(nonStrided){let sliced=slice2({inputs:{x:$x},backend:backend2,attrs:{begin:$begin,size}});result=reshape3({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(sliced)}else if(outShape.some(axis=>axis===0))result=backend2.makeTensorInfo(outShape,x.dtype,[]);else{let xBuf=backend2.bufferSync($x),outBuf=stridedSliceImpl(outShape,xBuf,$strides,$begin);result=backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:outShape}});return backend2.disposeIntermediateTensorInfo($x),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var stridedSliceConfig={kernelName:StridedSlice,backendName:"cpu",kernelFunc:stridedSlice2};var tan2=unaryKernelFunc(Tan,xi=>Math.tan(xi)),tanConfig={kernelName:Tan,backendName:"cpu",kernelFunc:tan2};var tanh3=unaryKernelFunc(Tanh,xi=>Math.tanh(xi)),tanhConfig={kernelName:Tanh,backendName:"cpu",kernelFunc:tanh3};function tile3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reps}=attrs;assertNotComplex(x,"tile");let outBuf=tileImpl(backend2.bufferSync(x),reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var tileConfig={kernelName:Tile,backendName:"cpu",kernelFunc:tile3};function topK(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs;assertNotComplex(x,"topk");let xVals=backend2.data.get(x.dataId).values,[allTopKVals,allTopKIndices]=topKImpl(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig={kernelName:TopK,backendName:"cpu",kernelFunc:topK};function unique3(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex(x,"unique");let values=backend2.data.get(x.dataId).values,{outputValues,outputShape,indices}=uniqueImpl(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig={kernelName:Unique,backendName:"cpu",kernelFunc:unique3};function unpack(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let valueRank=value.shape.length,num=value.shape[axis],outShape=new Array(valueRank-1),outIndex=0;for(let i=0;i<valueRank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let begin=new Array(valueRank).fill(0),size=value.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let tempRes=slice2({inputs:{x:value},backend:backend2,attrs:{begin,size}});res[i]=reshape3({inputs:{x:tempRes},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(tempRes)}return res}var unpackConfig={kernelName:Unpack,backendName:"cpu",kernelFunc:unpack};function unsortedSegmentSum2(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs;assertNotComplex(x,"unsortedSegmentSum");let xRank=x.shape.length,segmentIdsRank=segmentIds.shape.length,res=[],intermediates=[],numIters=xRank-segmentIdsRank,$segmentIds=segmentIds;for(let i=0;i<numIters;++i){let expanded=expandDims3({inputs:{input:$segmentIds},backend:backend2,attrs:{dim:i+1}});$segmentIds=expanded,intermediates.push(expanded)}for(let i=0;i<numSegments;++i){let scalarValue=util_exports.createScalarValue(i,"int32"),segmentId=backend2.makeTensorInfo([],"int32",scalarValue),mask=equal2({inputs:{a:segmentId,b:$segmentIds},backend:backend2}),maskCasted=cast3({inputs:{x:mask},backend:backend2,attrs:{dtype:"float32"}}),mul2=multiply2({inputs:{a:maskCasted,b:x},backend:backend2}),sumTensorInfo=sum3({inputs:{x:mul2},backend:backend2,attrs:{axis:0,keepDims:!1}});res.push(sumTensorInfo),intermediates.push(segmentId),intermediates.push(mask),intermediates.push(maskCasted),intermediates.push(mul2),intermediates.push(sumTensorInfo)}let result=pack({inputs:res,backend:backend2,attrs:{axis:0}});return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig={kernelName:UnsortedSegmentSum,backendName:"cpu",kernelFunc:unsortedSegmentSum2};var kernelConfigs=[_fusedMatMulConfig,absConfig,acosConfig,acoshConfig,addConfig,addNConfig,allConfig,anyConfig,argMaxConfig,argMinConfig,asinConfig,asinhConfig,atanConfig,atan2Config,atanhConfig,avgPoolConfig,avgPool3DConfig,avgPool3DGradConfig2,avgPoolGradConfig2,batchMatMulConfig,batchNormConfig,batchToSpaceNDConfig,bincountConfig,castConfig,ceilConfig,clipConfig,complexConfig,complexAbsConfig,concatConfig,conv2DBackpropFilterConfig,conv2DBackpropInputConfig,conv2DConfig,conv3DBackpropFilterV2Config,conv3DBackpropInputV2Config,conv3DConfig,cosConfig,coshConfig,cropAndResizeConfig,cumsumConfig,denseBincountConfig,depthToSpaceConfig,depthwiseConv2dNativeConfig,depthwiseConv2dNativeBackpropFilterConfig,depthwiseConv2dNativeBackpropInputConfig,diagConfig,dilation2dConfig,dilation2dBackpropInputConfig,dilation2dBackpropFilterConfig,realDivConfig,eluConfig,eluGradConfig2,equalConfig,erfConfig,expConfig,expandDimsConfig,expm1Config,fftConfig,fillConfig,flipLeftRightConfig,floorConfig,floorDivConfig,fusedConv2DConfig,fusedDepthwiseConv2DConfig,gatherNdConfig,gatherV2Config,greaterConfig,greaterEqualConfig,identityConfig,ifftConfig,imagConfig,isFiniteConfig,isInfConfig,isNaNConfig,leakyReluConfig,lessConfig,lessEqualConfig,linSpaceConfig,logConfig,log1pConfig,logicalAndConfig,logicalNotConfig,logicalOrConfig,lRNConfig,lRNGradConfig,maximumConfig,maxPoolConfig,maxPool3DConfig,maxPool3DGradConfig2,maxPoolGradConfig2,maxPoolWithArgmaxConfig,maxConfig,meanConfig,minConfig,minimumConfig,mirrorPadConfig,modConfig,multinomialConfig,multiplyConfig,negConfig,nonMaxSuppressionV3Config,nonMaxSuppressionV4Config,nonMaxSuppressionV5Config,notEqualConfig,oneHotConfig,onesLikeConfig,packConfig,padV2Config,powConfig,preluConfig,prodConfig,rangeConfig,realConfig,reciprocalConfig,reluConfig,relu6Config,reshapeConfig,resizeBilinearConfig,resizeBilinearGradConfig2,resizeNearestNeighborConfig,resizeNearestNeighborGradConfig2,reverseConfig,rotateWithOffsetConfig,roundConfig,rsqrtConfig,scatterNdConfig,selectConfig,seluConfig,sigmoidConfig,signConfig,sinConfig,sinhConfig,sliceConfig,softmaxConfig,softplusConfig,spaceToBatchNDConfig,sparseToDenseConfig,splitVConfig,sqrtConfig,squareConfig,squaredDifferenceConfig,stepConfig,stridedSliceConfig,subConfig,sumConfig,tanConfig,tanhConfig,tileConfig,topKConfig,transposeConfig,uniqueConfig,unpackConfig,unsortedSegmentSumConfig,zerosLikeConfig];for(let kernelConfig of kernelConfigs)registerKernel(kernelConfig);var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(webGLVersion,gl){contexts[webGLVersion]=gl}function getWebGLContext(webGLVersion){if(!(webGLVersion in contexts)){let newCtx=getWebGLRenderingContext(webGLVersion);if(newCtx!==null)contexts[webGLVersion]=newCtx;else return console.log("Could not get context for WebGL version",webGLVersion),null}let gl=contexts[webGLVersion];return gl.isContextLost()?(delete contexts[webGLVersion],getWebGLContext(webGLVersion)):(gl.disable(gl.DEPTH_TEST),gl.disable(gl.STENCIL_TEST),gl.disable(gl.BLEND),gl.disable(gl.DITHER),gl.disable(gl.POLYGON_OFFSET_FILL),gl.disable(gl.SAMPLE_COVERAGE),gl.enable(gl.SCISSOR_TEST),gl.enable(gl.CULL_FACE),gl.cullFace(gl.BACK),contexts[webGLVersion])}function createCanvas(webGLVersion){if(typeof OffscreenCanvas!="undefined"&&webGLVersion===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function getWebGLRenderingContext(webGLVersion){if(webGLVersion!==1&&webGLVersion!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let canvas=createCanvas(webGLVersion);return canvas.addEventListener("webglcontextlost",ev=>{ev.preventDefault(),delete contexts[webGLVersion]},!1),webGLVersion===1?canvas.getContext("webgl",WEBGL_ATTRIBUTES)||canvas.getContext("experimental-webgl",WEBGL_ATTRIBUTES):canvas.getContext("webgl2",WEBGL_ATTRIBUTES)}var PackingScheme;(function(PackingScheme2){PackingScheme2[PackingScheme2.DENSE=0]="DENSE",PackingScheme2[PackingScheme2.SHARED_BATCH=1]="SHARED_BATCH"})(PackingScheme||(PackingScheme={}));var TextureUsage;(function(TextureUsage2){TextureUsage2[TextureUsage2.RENDER=0]="RENDER",TextureUsage2[TextureUsage2.UPLOAD=1]="UPLOAD",TextureUsage2[TextureUsage2.PIXELS=2]="PIXELS",TextureUsage2[TextureUsage2.DOWNLOAD=3]="DOWNLOAD"})(TextureUsage||(TextureUsage={}));var PhysicalTextureType;(function(PhysicalTextureType2){PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(PhysicalTextureType||(PhysicalTextureType={}));function getUnpackedMatrixTextureShapeWidthHeight(rows,columns){return[columns,rows]}function getUnpackedArraySizeFromMatrixSize(matrixSize,channelsPerTexture){return matrixSize*channelsPerTexture}function getDenseTexShape(shape){let size=util_exports.sizeFromShape(shape),texelsNeeded=Math.ceil(size/4);return util_exports.sizeToSquarishShape(texelsNeeded)}function getPackedMatrixTextureShapeWidthHeight(rows,columns){return[Math.max(1,Math.ceil(columns/2)),Math.max(1,Math.ceil(rows/2))]}function getPackedRGBAArraySizeFromMatrixShape(rows,columns){let[w,h]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return w*h*4}function getTextureConfig(gl,textureHalfFloatExtension){let glany=gl,internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat;return env().getNumber("WEBGL_VERSION")===2?(internalFormatFloat=glany.R32F,internalFormatHalfFloat=glany.R16F,internalFormatPackedHalfFloat=glany.RGBA16F,internalFormatPackedFloat=glany.RGBA32F,textureFormatFloat=glany.RED,downloadUnpackNumChannels=4,defaultNumChannels=1,textureTypeHalfFloat=glany.HALF_FLOAT,textureTypeFloat=glany.FLOAT):(internalFormatFloat=gl.RGBA,internalFormatHalfFloat=gl.RGBA,internalFormatPackedHalfFloat=gl.RGBA,internalFormatPackedFloat=glany.RGBA,textureFormatFloat=gl.RGBA,downloadUnpackNumChannels=4,defaultNumChannels=4,textureTypeHalfFloat=textureHalfFloatExtension!=null?textureHalfFloatExtension.HALF_FLOAT_OES:null,textureTypeFloat=gl.FLOAT),downloadTextureFormat=gl.RGBA,{internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat}}function callAndCheck(gl,func2){let returnValue=func2();return env().getBool("DEBUG")&&checkWebGLError(gl),returnValue}function checkWebGLError(gl){let error=gl.getError();if(error!==gl.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(gl,error))}var MIN_FLOAT16=596e-10,MAX_FLOAT16=65504;function canBeRepresented(num){return!!(env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||num===0||MIN_FLOAT16<Math.abs(num)&&Math.abs(num)<MAX_FLOAT16)}function getWebGLErrorMessage(gl,status){switch(status){case gl.NO_ERROR:return"NO_ERROR";case gl.INVALID_ENUM:return"INVALID_ENUM";case gl.INVALID_VALUE:return"INVALID_VALUE";case gl.INVALID_OPERATION:return"INVALID_OPERATION";case gl.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case gl.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case gl.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${status}`}}function getExtensionOrThrow(gl,extensionName){return throwIfNull(gl,()=>gl.getExtension(extensionName),'Extension "'+extensionName+'" not supported on this browser.')}function createVertexShader(gl,vertexShaderSource){let vertexShader=throwIfNull(gl,()=>gl.createShader(gl.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(vertexShader,vertexShaderSource)),callAndCheck(gl,()=>gl.compileShader(vertexShader)),gl.getShaderParameter(vertexShader,gl.COMPILE_STATUS)===!1)throw console.log(gl.getShaderInfoLog(vertexShader)),new Error("Failed to compile vertex shader.");return vertexShader}function createFragmentShader(gl,fragmentShaderSource){let fragmentShader=throwIfNull(gl,()=>gl.createShader(gl.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(fragmentShader,fragmentShaderSource)),callAndCheck(gl,()=>gl.compileShader(fragmentShader)),gl.getShaderParameter(fragmentShader,gl.COMPILE_STATUS)===!1)throw logShaderSourceAndInfoLog(fragmentShaderSource,gl.getShaderInfoLog(fragmentShader)),new Error("Failed to compile fragment shader.");return fragmentShader}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(shaderSource,shaderInfoLog){let lineNumberRegexResult=lineNumberRegex.exec(shaderInfoLog);if(lineNumberRegexResult==null){console.log(`Couldn't parse line number in error: ${shaderInfoLog}`),console.log(shaderSource);return}let lineNumber=+lineNumberRegexResult[1],shaderLines=shaderSource.split(`
+============================`));let dataId={};return this.data.set(dataId,{values,dtype,refCount:1}),dataId}makeTensorInfo(shape,dtype,values){let outId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));outId=this.write(encodedValues,shape,dtype)}else outId=this.write(values,shape,dtype);return{dataId:outId,shape,dtype}}incRef(dataId){let tensorData=this.data.get(dataId);tensorData.refCount++}decRef(dataId){if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--}}move(dataId,values,shape,dtype){this.data.set(dataId,{values,dtype,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{dtype,complexTensorInfos}=this.data.get(dataId);if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);return backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}return this.data.get(dataId).values}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}makeOutput(values,shape,dtype){let dataId=this.write(values,shape,dtype);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}disposeData(dataId){if(this.data.has(dataId)){let{complexTensorInfos}=this.data.get(dataId);complexTensorInfos!=null&&(this.disposeData(complexTensorInfos.real.dataId),this.disposeData(complexTensorInfos.imag.dataId)),this.data.delete(dataId)}}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--,tensorData.refCount<1&&this.disposeData(dataId)}}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(condition){assertNotComplex([condition],"where");let condVals=this.readSync(condition.dataId);return whereImpl2(condition.shape,condVals)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};var shared_exports={};__export(shared_exports,{addImpl:()=>addImpl,bincountImpl:()=>bincountImpl,bincountReduceImpl:()=>bincountReduceImpl,ceilImpl:()=>ceilImpl,concatImpl:()=>concatImpl,expImpl:()=>expImpl,expm1Impl:()=>expm1Impl,floorImpl:()=>floorImpl,gatherV2Impl:()=>gatherV2Impl,greaterImpl:()=>greaterImpl,lessImpl:()=>lessImpl,linSpaceImpl:()=>linSpaceImpl,logImpl:()=>logImpl,maxImpl:()=>maxImpl,maximumImpl:()=>maximumImpl,minimumImpl:()=>minimumImpl,multiplyImpl:()=>multiplyImpl,negImpl:()=>negImpl,notEqualImpl:()=>notEqualImpl,prodImpl:()=>prodImpl,rangeImpl:()=>rangeImpl,rsqrtImpl:()=>rsqrtImpl,simpleAbsImpl:()=>simpleAbsImpl,sliceImpl:()=>sliceImpl,squaredDifferenceImpl:()=>squaredDifferenceImpl,stridedSliceImpl:()=>stridedSliceImpl,subImpl:()=>subImpl,tileImpl:()=>tileImpl,topKImpl:()=>topKImpl,transposeImpl:()=>transposeImpl,uniqueImpl:()=>uniqueImpl});function simpleAbsImpl(vals){let resultValues=new Float32Array(vals.length);for(let i=0;i<vals.length;++i)resultValues[i]=Math.abs(vals[i]);return resultValues}var abs2=args=>{let{x}=args.inputs,cpuBackend=args.backend;assertNotComplex(x,"abs");let resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),values=cpuBackend.data.get(x.dataId).values;return resultValues=simpleAbsImpl(values),cpuBackend.makeOutput(resultValues,x.shape,"float32")},absConfig={kernelName:Abs,backendName:"cpu",kernelFunc:abs2};function createSimpleBinaryKernelImpl(op2){return(aShape,bShape,aVals,bVals,dtype)=>{let newShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultRank=newShape.length,resultStrides=util_exports.computeStrides(newShape),resultSize=util_exports.sizeFromShape(newShape),result=util_exports.getTypedArrayFromDType(dtype,resultSize),aRank=aShape.length,bRank=bShape.length,aStrides=util_exports.computeStrides(aShape),bStrides=util_exports.computeStrides(bShape),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,newShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<result.length;++i)result[i]=op2(aVals[i%aVals.length],bVals[i%bVals.length]);else for(let i=0;i<result.length;++i){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides);result[i]=op2(aVals[aIndex],bVals[bIndex])}return[result,newShape]}}function complex2(args){let{inputs,backend:backend2}=args,{real:real4,imag:imag4}=inputs,realVals=backend2.data.get(real4.dataId).values,imagVals=backend2.data.get(imag4.dataId).values,complexInfo=backend2.makeTensorInfo(real4.shape,"complex64"),complex4=backend2.data.get(complexInfo.dataId);return complex4.complexTensorInfos={real:backend2.makeTensorInfo(real4.shape,"float32",realVals),imag:backend2.makeTensorInfo(imag4.shape,"float32",imagVals)},complexInfo}var complexConfig={kernelName:Complex,backendName:"cpu",kernelFunc:complex2};function zeros3(backend2,shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros3(backend2,shape,"float32"),imag4=zeros3(backend2,shape,"float32");return complex2({inputs:{real:real4,imag:imag4},backend:backend2})}let values=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape),dtype);return backend2.makeTensorInfo(shape,dtype,values)}function identity2(args){let{inputs,backend:backend2}=args,{x}=inputs;return backend2.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig={kernelName:Identity,backendName:"cpu",kernelFunc:identity2};function real2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,real4=backend2.data.get(input2.dataId).complexTensorInfos.real,realVal=backend2.data.get(real4.dataId).values;return backend2.makeTensorInfo(real4.shape,real4.dtype,realVal)}var realConfig={kernelName:Real,backendName:"cpu",kernelFunc:real2};function cast3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity2({inputs:{x},backend:backend2});let zerosTensorInfo=zeros3(backend2,x.shape,x.dtype),floatX=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex2({inputs:{real:floatX,imag:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),result=cast3({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity2({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32"){let values=backend2.data.get(x.dataId).values,resultValues=Int32Array.from(values);return backend2.makeTensorInfo(x.shape,"int32",resultValues)}if(dtype==="bool"){let xVals=backend2.data.get(x.dataId).values,zero=util_exports.toTypedArray([0],x.dtype),[resultData,resultShape]=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0)(x.shape,[],xVals,zero,"bool");return backend2.makeTensorInfo(resultShape,"bool",resultData)}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig={kernelName:Cast,backendName:"cpu",kernelFunc:cast3};function binaryKernelFunc(name,simpleImpl,complexImpl,dtype){return complexImpl==null?({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;assertNotComplex([a,b],name);let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}:({inputs,backend:backend2})=>{let{a,b}=inputs,cpuBackend=backend2;if(a.dtype==="complex64"||b.dtype==="complex64"){let $aComplex=cast3({inputs:{x:a},backend:cpuBackend,attrs:{dtype:"complex64"}}),$aComplexVals=cpuBackend.data.get($aComplex.dataId),aReal=$aComplexVals.complexTensorInfos.real,aImag=$aComplexVals.complexTensorInfos.imag,aRealVals=cpuBackend.data.get(aReal.dataId).values,aImagVals=cpuBackend.data.get(aImag.dataId).values,$bComplex=cast3({inputs:{x:b},backend:cpuBackend,attrs:{dtype:"complex64"}}),$bComplexVals=cpuBackend.data.get($bComplex.dataId),bReal=$bComplexVals.complexTensorInfos.real,bImag=$bComplexVals.complexTensorInfos.imag,bRealVals=cpuBackend.data.get(bReal.dataId).values,bImagVals=cpuBackend.data.get(bImag.dataId).values,[resultRealData,resultImagData,resultShape]=complexImpl(a.shape,b.shape,aRealVals,aImagVals,bRealVals,bImagVals),resultReal=cpuBackend.makeTensorInfo(resultShape,"float32",resultRealData),resultImag=cpuBackend.makeTensorInfo(resultShape,"float32",resultImagData),result=complex2({inputs:{real:resultReal,imag:resultImag},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($aComplex),cpuBackend.disposeIntermediateTensorInfo($bComplex),cpuBackend.disposeIntermediateTensorInfo(resultReal),cpuBackend.disposeIntermediateTensorInfo(resultImag),result}else{let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}}}function createComplexBinaryKernelImpl(op2){return(aShape,bShape,aRealVals,aImagVals,bRealVals,bImagVals)=>{let resultShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultSize=util_exports.sizeFromShape(resultShape),resultRank=resultShape.length,resultStrides=util_exports.computeStrides(resultShape),resultRealVals=util_exports.getTypedArrayFromDType("float32",resultSize),resultImagVals=util_exports.getTypedArrayFromDType("float32",resultSize),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,resultShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,resultShape),aVals=backend_util_exports.mergeRealAndImagArrays(aRealVals,aImagVals),bVals=backend_util_exports.mergeRealAndImagArrays(bRealVals,bImagVals),aRank=aShape.length,aStrides=util_exports.computeStrides(aShape),bRank=bShape.length,bStrides=util_exports.computeStrides(bShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resultRealVals.length;i++){let aIdx=i%aVals.length,bIdx=i%bVals.length,result=op2(aVals[aIdx*2],aVals[aIdx*2+1],bVals[bIdx*2],bVals[bIdx*2+1]);resultRealVals[i]=result.real,resultImagVals[i]=result.imag}else for(let i=0;i<resultRealVals.length;i++){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides),opResult=op2(aVals[aIndex*2],aVals[aIndex*2+1],bVals[bIndex*2],bVals[bIndex*2+1]);resultRealVals[i]=opResult.real,resultImagVals[i]=opResult.imag}return[resultRealVals,resultImagVals,resultShape]}}var addImpl=createSimpleBinaryKernelImpl((a,b)=>a+b),addComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal+bReal,imag:aImag+bImag})),add4=binaryKernelFunc(Add,addImpl,addComplexImpl),addConfig={kernelName:Add,backendName:"cpu",kernelFunc:add4};function bincountImpl(xVals,weightsVals,weightsDtype,weightsShape,size){let weightsSize=util_exports.sizeFromShape(weightsShape),outVals=util_exports.makeZerosTypedArray(size,weightsDtype);for(let i=0;i<xVals.length;i++){let value=xVals[i];if(value<0)throw new Error("Input x must be non-negative!");value>=size||(weightsSize>0?outVals[value]+=weightsVals[i]:outVals[value]+=1)}return outVals}function bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput=!1){let numRows=xBuf.shape[0],numCols=xBuf.shape[1],outBuf=buffer([numRows,size],weightsBuf.dtype);for(let i=0;i<numRows;i++)for(let j=0;j<numCols;j++){let value=xBuf.get(i,j);if(value<0)throw new Error("Input x must be non-negative!");value>=size||(binaryOutput?outBuf.set(1,i,value):weightsBuf.size>0?outBuf.set(outBuf.get(i,value)+weightsBuf.get(i,j),i,value):outBuf.set(outBuf.get(i,value)+1,i,value))}return outBuf}function createSimpleUnaryImpl(op2){return(values,dtype,attrs)=>{let newValues=util_exports.getTypedArrayFromDType(dtype,values.length);for(let i=0;i<values.length;++i)newValues[i]=op2(values[i],attrs);return newValues}}function unaryKernelFunc(name,op2,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),$dtype=dtype||x.dtype,newValues=util_exports.getArrayFromDType($dtype,xSize);for(let i=0;i<xSize;++i)newValues[i]=op2(values[i],attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}function unaryKernelFuncFromImpl(name,unaryImpl,dtype){return({inputs,attrs,backend:backend2})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend2,values=cpuBackend.data.get(x.dataId).values,$dtype=dtype||x.dtype,newValues=unaryImpl(values,$dtype,attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}var ceilImpl=createSimpleUnaryImpl(xi=>Math.ceil(xi)),ceil2=unaryKernelFuncFromImpl(Ceil,ceilImpl),ceilConfig={kernelName:Ceil,backendName:"cpu",kernelFunc:ceil2};function concatImpl(inputs,outShape,dtype,simplyConcat){let outVals=util_exports.getArrayFromDType(dtype,util_exports.sizeFromShape(outShape));if(simplyConcat&&dtype!=="string"){let offset=0;inputs.forEach(input2=>{let size=util_exports.sizeFromShape(input2.shape);outVals.set(input2.vals,offset),offset+=size})}else{let colOffset=0;inputs.forEach(input2=>{let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(input2.vals):input2.vals,tIdx=0;for(let row=0;row<input2.shape[0];++row){let resIdx=row*outShape[1]+colOffset;for(let col=0;col<input2.shape[1];++col)outVals[resIdx+col]=decodedData[tIdx++]}colOffset+=input2.shape[1]})}return outVals}var expImpl=createSimpleUnaryImpl(xi=>Math.exp(xi)),exp2=unaryKernelFuncFromImpl(Exp,expImpl),expConfig={kernelName:Exp,backendName:"cpu",kernelFunc:exp2};var expm1Impl=createSimpleUnaryImpl(xi=>Math.expm1(xi)),expm12=unaryKernelFuncFromImpl(Expm1,expm1Impl),expm1Config={kernelName:Expm1,backendName:"cpu",kernelFunc:expm12};var floorImpl=createSimpleUnaryImpl(xi=>Math.floor(xi)),floor2=unaryKernelFuncFromImpl(Floor,floorImpl),floorConfig={kernelName:Floor,backendName:"cpu",kernelFunc:floor2};function gatherV2Impl(xBuf,indicesBuf,flattenOutputShape){let outBuf=buffer(flattenOutputShape,xBuf.dtype);for(let i=0;i<outBuf.size;++i){let originalLoc=outBuf.indexToLoc(i).slice(),batchIdx=originalLoc[0],indicesIdx=originalLoc[2],indicesIndex=indicesBuf.locToIndex([batchIdx,indicesIdx]);originalLoc[2]=indicesBuf.values[indicesIndex];let originalIndex=xBuf.locToIndex(originalLoc);outBuf.values[i]=xBuf.values[originalIndex]}return outBuf}var greaterImpl=createSimpleBinaryKernelImpl((a,b)=>a>b?1:0),greater3=binaryKernelFunc(Greater,greaterImpl,null,"bool"),greaterConfig={kernelName:Greater,backendName:"cpu",kernelFunc:greater3};var lessImpl=createSimpleBinaryKernelImpl((a,b)=>a<b?1:0),less3=binaryKernelFunc(Less,lessImpl,null,"bool"),lessConfig={kernelName:Less,backendName:"cpu",kernelFunc:less3};function linSpaceImpl(start,stop,num){let step4=(stop-start)/(num-1),values=util_exports.makeZerosTypedArray(num,"float32");values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var logImpl=createSimpleUnaryImpl(xi=>Math.log(xi)),log3=unaryKernelFuncFromImpl(Log,logImpl),logConfig={kernelName:Log,backendName:"cpu",kernelFunc:log3};function maxImpl(aVals,reduceSize,outShape,dtype){let vals=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(outShape));for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value)}vals[i]=max6}return vals}var maximumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.max(aValue,bValue)),maximum3=binaryKernelFunc(Maximum,maximumImpl),maximumConfig={kernelName:Maximum,backendName:"cpu",kernelFunc:maximum3};var minimumImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.min(aValue,bValue)),minimum3=binaryKernelFunc(Minimum,minimumImpl),minimumConfig={kernelName:Minimum,backendName:"cpu",kernelFunc:minimum3};var multiplyImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue*bValue),multiplyComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal*bReal-aImag*bImag,imag:aReal*bImag+aImag*bReal})),multiply2=binaryKernelFunc(Multiply,multiplyImpl,multiplyComplexImpl),multiplyConfig={kernelName:Multiply,backendName:"cpu",kernelFunc:multiply2};function negImpl(xVals,xShape,xDtype){let minusOne=util_exports.createScalarValue(-1,xDtype);return multiplyImpl([],xShape,minusOne,xVals,xDtype)}function neg2(args){let{inputs,backend:backend2}=args,{x}=inputs;assertNotComplex(x,"neg");let xVals=backend2.data.get(x.dataId).values,[res,newShape]=negImpl(xVals,x.shape,x.dtype);return backend2.makeTensorInfo(newShape,x.dtype,res)}var negConfig={kernelName:Neg,backendName:"cpu",kernelFunc:neg2};var notEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0),notEqual2=binaryKernelFunc(NotEqual,notEqualImpl,null,"bool"),notEqualConfig={kernelName:NotEqual,backendName:"cpu",kernelFunc:notEqual2};function transposeImpl(xVals,xShape,dtype,perm,newShape){let xRank=xShape.length,xSize=util_exports.sizeFromShape(xShape),xStrides=util_exports.computeStrides(xShape),newStrides=util_exports.computeStrides(newShape),result=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(newShape));for(let i=0;i<xSize;++i){let loc=util_exports.indexToLoc(i,xRank,xStrides),newLoc=new Array(loc.length);for(let i2=0;i2<newLoc.length;i2++)newLoc[i2]=loc[perm[i2]];let newIndex=util_exports.locToIndex(newLoc,xRank,newStrides);result[newIndex]=xVals[i]}return result}function transpose2(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{perm}=attrs;assertNotComplex(x,"transpose");let xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let values=backend2.data.get(x.dataId).values,result=transposeImpl(values,x.shape,x.dtype,perm,newShape);return{dataId:backend2.write(result,newShape,x.dtype),shape:newShape,dtype:x.dtype}}var transposeConfig={kernelName:Transpose,backendName:"cpu",kernelFunc:transpose2};function prodImpl(xShape,xDtype,xVals,reductionAxes){let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,reductionAxes),outDtype=upcastType(xDtype,"int32"),outVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),outDtype),reduceSize=util_exports.sizeFromShape(reduceShape);for(let i=0;i<outVals.length;++i){let offset=i*reduceSize,prod5=1;for(let j=0;j<reduceSize;++j)prod5*=xVals[offset+j];outVals[i]=prod5}return{outVals,outShape,outDtype}}function prod2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"prod");let xRank=x.shape.length,axes=util_exports.parseAxisParam(axis,x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=x,intermediateTensorInfos=[];permutation!=null&&(permutedX=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}),intermediateTensorInfos.push(permutedX),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank));let xVals=backend2.data.get(permutedX.dataId).values,{outVals,outShape,outDtype}=prodImpl(permutedX.shape,permutedX.dtype,xVals,reductionAxes),resultShape=outShape;return keepDims&&(resultShape=backend_util_exports.expandShapeToKeepDim(outShape,axes)),intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(resultShape,outDtype,outVals)}var prodConfig={kernelName:Prod,backendName:"cpu",kernelFunc:prod2};function rangeImpl(start,stop,step4,dtype){let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step4<0,decreasingRangePositiveStep=stop<start&&step4>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return util_exports.makeZerosTypedArray(0,dtype);let numElements=Math.abs(Math.ceil((stop-start)/step4)),values=util_exports.makeZerosTypedArray(numElements,dtype);stop<start&&step4===1&&(step4=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step4;return values}var rsqrtImpl=createSimpleUnaryImpl(xi=>1/Math.sqrt(xi)),rsqrt2=unaryKernelFuncFromImpl(Rsqrt,rsqrtImpl),rsqrtConfig={kernelName:Rsqrt,backendName:"cpu",kernelFunc:rsqrt2};function sliceImpl(vals,begin,size,shape,dtype){let isContinous=slice_util_exports.isSliceContinous(shape,begin,size),length=util_exports.sizeFromShape(size),xStrides=util_exports.computeStrides(shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin,xStrides);return dtype==="string"?vals.slice(flatOffset,flatOffset+length):vals.subarray(flatOffset,flatOffset+length)}let decodedData=dtype==="string"?backend_util_exports.fromUint8ToStringArray(vals):vals,inBuf=buffer(shape,dtype,decodedData),outBuf=buffer(size,dtype);for(let i=0;i<outBuf.size;++i){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.map((idx,j)=>idx+begin[j]);outBuf.set(inBuf.get(...inLoc),...outLoc)}return dtype==="string"?backend_util_exports.fromStringArrayToUint8(outBuf.values):outBuf.values}function slice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs;assertNotComplex(x,"slice");let[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);slice_util_exports.assertParamsValid(x,$begin,$size);let vals=backend2.data.get(x.dataId).values,outVals=sliceImpl(vals,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outVals)}var sliceConfig={kernelName:Slice,backendName:"cpu",kernelFunc:slice2};var squaredDifferenceImpl=createSimpleBinaryKernelImpl((a,b)=>{let diff=a-b;return diff*diff}),squaredDifference2=binaryKernelFunc(SquaredDifference,squaredDifferenceImpl),squaredDifferenceConfig={kernelName:SquaredDifference,backendName:"cpu",kernelFunc:squaredDifference2};function stridedSliceImpl(outShape,xBuf,strides,begin){let outBuf=buffer(outShape,xBuf.dtype);for(let i=0;i<outBuf.size;i++){let loc=outBuf.indexToLoc(i),newLoc=new Array(loc.length);for(let j=0;j<newLoc.length;j++)newLoc[j]=loc[j]*strides[j]+begin[j];outBuf.set(xBuf.get(...newLoc),...loc)}return outBuf}var subImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue-bValue),subComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal-bReal,imag:aImag-bImag})),sub2=binaryKernelFunc(Sub,subImpl,subComplexImpl),subConfig={kernelName:Sub,backendName:"cpu",kernelFunc:sub2};function tileImpl(xBuf,reps){let newShape=new Array(xBuf.rank);for(let i=0;i<newShape.length;i++)newShape[i]=xBuf.shape[i]*reps[i];let result=buffer(newShape,xBuf.dtype);for(let i=0;i<result.values.length;++i){let newLoc=result.indexToLoc(i),originalLoc=new Array(xBuf.rank);for(let j=0;j<originalLoc.length;j++)originalLoc[j]=newLoc[j]%xBuf.shape[j];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result}function topKImpl(x,xShape,xDtype,k,sorted){let lastDim=xShape[xShape.length-1],[batch,size]=[x.length/lastDim,lastDim],allTopKVals=util_exports.getTypedArrayFromDType(xDtype,batch*k),allTopKIndices=util_exports.getTypedArrayFromDType("int32",batch*k);for(let b=0;b<batch;b++){let offset=b*size,vals=x.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value);let outOffset=b*k,topKVals=allTopKVals.subarray(outOffset,outOffset+k),topKIndices=allTopKIndices.subarray(outOffset,outOffset+k);for(let i=0;i<k;i++)topKVals[i]=valAndInd[i].value,topKIndices[i]=valAndInd[i].index}let outputShape=xShape.slice();return outputShape[outputShape.length-1]=k,[buffer(outputShape,xDtype,allTopKVals),buffer(outputShape,"int32",allTopKIndices)]}function uniqueImpl(values,axis,shape,dtype){let $axis=util_exports.parseAxisParam(axis,shape)[0],newShape=[1,shape[0],1];for(let i=0;i<$axis;i++)newShape[0]*=shape[i];newShape[1]=shape[$axis];for(let i=$axis+1;i<shape.length;i++)newShape[2]*=shape[i];let uniqueElements={},indices=new Int32Array(shape[$axis]),inputBuffer=new TensorBuffer(newShape,dtype,values),uniqueIndices=[],is1DTensor=newShape[0]===1&&newShape[2]===1;for(let i=0;i<shape[$axis];i++){let element;if(is1DTensor)element=values[i].toString();else{let axisValues=[];for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)axisValues.push(inputBuffer.get(m,i,n));element=axisValues.join(",")}if(uniqueElements[element]!==void 0)indices[i]=uniqueElements[element];else{let uniqueIndex=Object.keys(uniqueElements).length;uniqueElements[element]=uniqueIndex,indices[i]=uniqueIndex,uniqueIndices.push(i)}}let outputTmpShape=newShape.slice();outputTmpShape[1]=Object.keys(uniqueElements).length;let outputBuffer=new TensorBuffer(outputTmpShape,dtype);uniqueIndices.forEach((uniqueElementIndex,i)=>{for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)outputBuffer.set(inputBuffer.get(m,uniqueElementIndex,n),m,i,n)});let outputShape=shape.slice();return outputShape[$axis]=outputTmpShape[1],{outputValues:outputBuffer.values,outputShape,indices}}var version5="2.8.3";registerBackend("cpu",()=>new MathBackendCPU,1);var elu4=unaryKernelFunc(Elu,xi=>xi>=0?xi:Math.exp(xi)-1),eluConfig={kernelName:Elu,backendName:"cpu",kernelFunc:elu4};function leakyRelu2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{alpha}=attrs;assertNotComplex([x],"leakyRelu");let xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outVals=util_exports.getTypedArrayFromDType("float32",xSize);for(let i=0;i<xVals.length;i++)outVals[i]=xVals[i]<0?alpha*xVals[i]:xVals[i];return backend2.makeTensorInfo(x.shape,"float32",outVals)}var leakyReluConfig={kernelName:LeakyRelu,backendName:"cpu",kernelFunc:leakyRelu2};var preluImpl=createSimpleBinaryKernelImpl((xValue,aValue)=>xValue<0?aValue*xValue:xValue);function prelu3(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs;assertNotComplex([x,alpha],"prelu");let aVals=backend2.data.get(x.dataId).values,bVals=backend2.data.get(alpha.dataId).values,[resultData,resultShape]=preluImpl(x.shape,alpha.shape,aVals,bVals,x.dtype);return backend2.makeTensorInfo(resultShape,x.dtype,resultData)}var preluConfig={kernelName:Prelu,backendName:"cpu",kernelFunc:prelu3};var relu2=unaryKernelFunc(Relu,xi=>Math.max(0,xi)),reluConfig={kernelName:Relu,backendName:"cpu",kernelFunc:relu2};var relu62=unaryKernelFunc(Relu6,xi=>Math.min(Math.max(0,xi),6)),relu6Config={kernelName:Relu6,backendName:"cpu",kernelFunc:relu62};function applyActivation2(backend2,x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return identity2({inputs:{x},backend:backend2});if(activation2==="relu")return relu2({inputs:{x},backend:backend2});if(activation2==="elu")return elu4({inputs:{x},backend:backend2});if(activation2==="relu6")return relu62({inputs:{x},backend:backend2});if(activation2==="prelu")return prelu3({inputs:{x,alpha:preluActivationWeights},backend:backend2});if(activation2==="leakyrelu")return leakyRelu2({inputs:{x},backend:backend2,attrs:{alpha:leakyreluAlpha}});throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`)}function reshape3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`),backend2.incRef(x.dataId);let xData=backend2.data.get(x.dataId);if(xData.complexTensorInfos!=null){let real4=xData.complexTensorInfos.real,imag4=xData.complexTensorInfos.imag;real4.shape=$shape,imag4.shape=$shape}return{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig={kernelName:Reshape,backendName:"cpu",kernelFunc:reshape3};function batchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;assertNotComplex([a,b],"matMul");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape3({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape3({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),sharedDim=transposeA?a3d.shape[1]:a3d.shape[2],leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),a3dValues=backend2.data.get(a3d.dataId).values,b3dValues=backend2.data.get(b3d.dataId).values,a3dStrides=util_exports.computeStrides(a3d.shape),b3dStrides=util_exports.computeStrides(b3d.shape),[aBatch,aOuterStep,aInnerStep]=transposeA?[a3dStrides[0],1,a3dStrides[1]]:[a3dStrides[0],a3dStrides[1],1],[bInnerStep,bOuterStep,bBatch]=transposeB?[1,b3dStrides[1],b3dStrides[0]]:[b3dStrides[1],1,b3dStrides[0]],size=leftDim*rightDim,result=buffer([batchDim,leftDim,rightDim],a3d.dtype),resVals=result.values,blockSize=backend2.blockSize;for(let bi=0;bi<batchDim;bi++)for(let i0=0;i0<leftDim;i0+=blockSize)for(let j0=0;j0<rightDim;j0+=blockSize)for(let k0=0;k0<sharedDim;k0+=blockSize){let iBlock=Math.min(i0+blockSize,leftDim),jBlock=Math.min(j0+blockSize,rightDim),kBlock=Math.min(k0+blockSize,sharedDim);for(let i=i0;i<iBlock;i++)for(let j=j0;j<jBlock;j++){let sum6=0;for(let k=k0;k<kBlock;k++){let batchOffsetA=Math.min(bi,batchDimA-1)*aBatch,batchOffsetB=Math.min(bi,batchDimB-1)*bBatch,aVal=a3dValues[batchOffsetA+i*aOuterStep+k*aInnerStep],bVal=b3dValues[k*bInnerStep+j*bOuterStep+batchOffsetB];sum6+=aVal*bVal}resVals[bi*size+(i*rightDim+j)]+=sum6}}return backend2.disposeIntermediateTensorInfo(a3d),backend2.disposeIntermediateTensorInfo(b3d),backend2.makeTensorInfo(outShape,result.dtype,result.values)}var batchMatMulConfig={kernelName:BatchMatMul,backendName:"cpu",kernelFunc:batchMatMul};function _fusedMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs,{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,current,addRes,activationRes,intermediates=[];current=batchMatMul({inputs:{a,b},attrs:{transposeA,transposeB},backend:backend2}),bias&&(addRes=add4({inputs:{a:current,b:bias},backend:backend2}),intermediates.push(current),current=addRes),activation2&&(activationRes=applyActivation2(backend2,current,activation2,preluActivationWeights,leakyreluAlpha),intermediates.push(current),current=activationRes);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return current}var _fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"cpu",kernelFunc:_fusedMatMul};var acos2=unaryKernelFunc(Acos,xi=>Math.acos(xi)),acosConfig={kernelName:Acos,backendName:"cpu",kernelFunc:acos2};var acosh2=unaryKernelFunc(Acosh,xi=>Math.acosh(xi)),acoshConfig={kernelName:Acosh,backendName:"cpu",kernelFunc:acosh2};function addN2(args){let{inputs,backend:backend2}=args,tensors=inputs;assertNotComplex(inputs,"addN");let vals=tensors.map(t=>backend2.data.get(t.dataId).values),outBuf=buffer(tensors[0].shape,tensors[0].dtype),outVals=outBuf.values;for(let i=0;i<tensors.length;i++){let currVals=vals[i];for(let j=0;j<outVals.length;j++)outVals[j]+=currVals[j]}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var addNConfig={kernelName:AddN,backendName:"cpu",kernelFunc:addN2};function all2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"all");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("all",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,all4=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];all4=all4&&value}vals[i]=all4}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var allConfig={kernelName:All,backendName:"cpu",kernelFunc:all2};function any2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"any");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("any",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,anyVal=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];anyVal=anyVal||value}vals[i]=anyVal}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var anyConfig={kernelName:Any,backendName:"cpu",kernelFunc:any2};function argMax2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMax");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMax",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max6=aVals[offset],maxIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max6&&(max6=value,maxIndex=j)}vals[i]=maxIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMaxConfig={kernelName:ArgMax,backendName:"cpu",kernelFunc:argMax2};function argMin2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis}=attrs;assertNotComplex(x,"argMin");let axes=util_exports.parseAxisParam(axis,x.shape),permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x,intermediateTensorInfos=[];permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),intermediateTensorInfos.push($x),axes=backend_util_exports.getInnerMostAxes(axes.length,$x.shape.length)),axes=[axes[0]],backend_util_exports.assertAxesAreInnerMostDims("argMin",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),outSize=util_exports.sizeFromShape(outShape),vals=util_exports.makeZerosTypedArray(outSize,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset],minIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value,minIndex=j)}vals[i]=minIndex}return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),backend2.makeTensorInfo(outShape,"int32",vals)}var argMinConfig={kernelName:ArgMin,backendName:"cpu",kernelFunc:argMin2};var asin2=unaryKernelFunc(Asin,xi=>Math.asin(xi)),asinConfig={kernelName:Asin,backendName:"cpu",kernelFunc:asin2};var asinh2=unaryKernelFunc(Asinh,xi=>Math.asinh(xi)),asinhConfig={kernelName:Asinh,backendName:"cpu",kernelFunc:asinh2};var atan3=unaryKernelFunc(Atan,xi=>Math.atan(xi)),atanConfig={kernelName:Atan,backendName:"cpu",kernelFunc:atan3};var atan2Impl=createSimpleBinaryKernelImpl((aValue,bValue)=>Math.atan2(aValue,bValue)),atan22=binaryKernelFunc(Atan2,atan2Impl),atan2Config={kernelName:Atan2,backendName:"cpu",kernelFunc:atan22};var atanh2=unaryKernelFunc(Atanh,xi=>Math.atanh(xi)),atanhConfig={kernelName:Atanh,backendName:"cpu",kernelFunc:atanh2};function pool2(xValues,xShape,dtype,strides,convInfo,poolType){let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3],outputRowStrides=convInfo.outShape[2]*convInfo.outShape[3],outputColStrides=convInfo.outShape[3];for(let b=0;b<convInfo.batchSize;++b){let outputBatchOffset=b*outputBatchStrides,inputBatchOffset=b*strides[0];for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=Math.max(0,xRCorner),xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner),outputRowOffset=outputBatchOffset+yR*outputRowStrides;for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=Math.max(0,xCCorner),xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),minMaxValue=initialValue,avgValue=0,count2=0;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let xROffset=inputBatchOffset+xR*strides[1];for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let xCOffset=xROffset+xC*strides[2],pixel=xValues[xCOffset+d];poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++)}if(isNaN(minMaxValue))break}let outputOffset=outputRowOffset+yC*outputColStrides+d;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}return output}function maxPoolPositions(xValues,xShape,dtype,convInfo,flattenPositions=!1,includeBatchInIndex=!1){let maxPositions=buffer(convInfo.outShape,"int32"),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=buffer(xShape,dtype,xValues);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=xRCorner;for(;xRMin<0;)xRMin+=dilationHeight;let xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner);for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=xCCorner;for(;xCMin<0;)xCMin+=dilationWidth;let xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let wR=xR-xRCorner;for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let wC=xC-xCCorner,pixel=xBuf.get(b,xR,xC,d);pixel>maxValue&&(maxValue=pixel,flattenPositions?maxPosition=includeBatchInIndex?((b*convInfo.inHeight+xR)*convInfo.inWidth+xC)*convInfo.inChannels+d:(xR*convInfo.inWidth+xC)*convInfo.inChannels+d:maxPosition=wR*effectiveFilterWidth+wC)}}maxPositions.set(maxPosition,b,yR,yC,d)}}return maxPositions}function pool3d2(xValues,xShape,dtype,strides,convInfo,poolType){let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputDepthStrides=convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputRowStrides=convInfo.outShape[3]*convInfo.outShape[4],outputColStrides=convInfo.outShape[4];for(let batch=0;batch<convInfo.batchSize;++batch){let outputBatchOffset=batch*outputBatchStrides,inputBatchOffset=batch*strides[0];for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner),outputDepthOffset=outputBatchOffset+yDepth*outputDepthStrides;for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner),outputRowOffset=outputDepthOffset+yRow*outputRowStrides;for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),outputColOffset=outputRowOffset+yCol*outputColStrides,minMaxValue=initialValue,avgValue=0,count2=0;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let xDepthOffset=inputBatchOffset+xDepth*strides[1];for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let xRowOffset=xDepthOffset+xRow*strides[2];for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let xColOffset=xRowOffset+xCol*strides[3],pixel=xValues[xColOffset+channel];if(poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++),isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}let outputOffset=outputColOffset+channel;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}}return output}function maxPool3dPositions(xBuf,convInfo){let maxPositions=buffer(convInfo.outShape,"int32"),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner);for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner);for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let wDepth=xDepth-xDepthCorner;for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let wRow=xRow-xRowCorner;for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let wCol=xCol-xColCorner,pixel=xBuf.get(batch,xDepth,xRow,xCol,channel);pixel>=maxValue&&(maxValue=pixel,maxPosition=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterHeight+wCol)}}}maxPositions.set(maxPosition,batch,yDepth,yRow,yCol,channel)}}}return maxPositions}function avgPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"avgPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"avg");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var avgPoolConfig={kernelName:AvgPool,backendName:"cpu",kernelFunc:avgPool2};function avgPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"avgPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"avg");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var avgPool3DConfig={kernelName:AvgPool3D,backendName:"cpu",kernelFunc:avgPool3D};function avgPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"avgPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),avgMultiplier=1/(filterDepth*filterHeight*filterWidth),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel)}}}dx.set(dotProd*avgMultiplier,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPool3DGradConfig2={kernelName:AvgPool3DGrad,backendName:"cpu",kernelFunc:avgPool3DGrad};function avgPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex([dy,input2],"avgPoolGrad");let{filterSize,strides,pad:pad3}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterHeight*filterWidth),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;dotProd+=dyBuf.get(b,dyR,dyC,d)}}dx.set(dotProd*avgMultiplier,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPoolGradConfig2={kernelName:AvgPoolGrad,backendName:"cpu",kernelFunc:avgPoolGrad2};function batchNorm2(args){let{inputs,backend:backend2,attrs}=args,{x,scale:scale2,offset,mean:mean4,variance}=inputs;util_exports.assert(mean4.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean4.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale2==null||mean4.shape.length===scale2.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),assertNotComplex([x,mean4,variance,scale2,offset],"batchNorm");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let xVals=backend2.data.get(x.dataId).values,mVals=backend2.data.get(mean4.dataId).values,varVals=backend2.data.get(variance.dataId).values,sVals=scale2?backend2.data.get(scale2.dataId).values:new Float32Array([1]),offVals=offset?backend2.data.get(offset.dataId).values:new Float32Array([0]),outVals=new Float32Array(xVals.length),offValsLength=offVals.length,sValsLength=sVals.length,varValsLength=varVals.length,mValsLength=mVals.length,offi=0,mi=0,si=0,vi=0;for(let i=0;i<xVals.length;++i)outVals[i]=offVals[offi++]+(xVals[i]-mVals[mi++])*sVals[si++]/Math.sqrt(varVals[vi++]+varianceEpsilon),offi>=offValsLength&&(offi=0),mi>=mValsLength&&(mi=0),si>=sValsLength&&(si=0),vi>=varValsLength&&(vi=0);return backend2.makeTensorInfo(x.shape,x.dtype,outVals)}var batchNormConfig={kernelName:FusedBatchNorm,backendName:"cpu",kernelFunc:batchNorm2};function batchToSpaceND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;assertNotComplex([x],"batchToSpaceND");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),xReshaped=reshape3({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),xTransposed=transpose2({inputs:{x:xReshaped},backend:backend2,attrs:{perm:permuted}}),xTransposedReshaped=reshape3({inputs:{x:xTransposed},backend:backend2,attrs:{shape:reshapedPermuted}}),result=slice2({inputs:{x:xTransposedReshaped},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return backend2.disposeIntermediateTensorInfo(xReshaped),backend2.disposeIntermediateTensorInfo(xTransposed),backend2.disposeIntermediateTensorInfo(xTransposedReshaped),result}var batchToSpaceNDConfig={kernelName:BatchToSpaceND,backendName:"cpu",kernelFunc:batchToSpaceND2};function bincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig={kernelName:Bincount,backendName:"cpu",kernelFunc:bincount2};var clip=unaryKernelFunc(ClipByValue,(xi,attrs)=>{let clipAttrs=attrs;return xi>clipAttrs.clipValueMax?clipAttrs.clipValueMax:xi<clipAttrs.clipValueMin?clipAttrs.clipValueMin:xi}),clipConfig={kernelName:ClipByValue,backendName:"cpu",kernelFunc:clip};var complexAbs=args=>{let{x}=args.inputs,cpuBackend=args.backend,resultValues=new Float32Array(util_exports.sizeFromShape(x.shape)),complexVals=cpuBackend.data.get(x.dataId),real4=complexVals.complexTensorInfos.real,imag4=complexVals.complexTensorInfos.imag,realVals=cpuBackend.data.get(real4.dataId).values,imagVals=cpuBackend.data.get(imag4.dataId).values;for(let i=0;i<realVals.length;i++){let real5=realVals[i],imag5=imagVals[i];resultValues[i]=Math.hypot(real5,imag5)}return cpuBackend.makeOutput(resultValues,x.shape,"float32")},complexAbsConfig={kernelName:ComplexAbs,backendName:"cpu",kernelFunc:complexAbs};function imag2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,imag4=backend2.data.get(input2.dataId).complexTensorInfos.imag,imagVal=backend2.data.get(imag4.dataId).values;return backend2.makeTensorInfo(imag4.shape,imag4.dtype,imagVal)}var imagConfig={kernelName:Imag,backendName:"cpu",kernelFunc:imag2};function concat2(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity2({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,$axis),$inputs[0].dtype==="complex64"){let reals=$inputs.map(t=>real2({inputs:{input:t},backend:backend2})),imags=$inputs.map(t=>imag2({inputs:{input:t},backend:backend2})),realConcated=concat2({inputs:reals,backend:backend2,attrs:{axis:$axis}}),imagConcated=concat2({inputs:imags,backend:backend2,attrs:{axis:$axis}}),result=complex2({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result}let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice($axis));return reshape3({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.data.get(t.dataId).values,shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),$axis),outInfo=backend2.makeTensorInfo(finalOutShape,inputs[0].dtype,outVals);return inputs2D.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}var concatConfig={kernelName:Concat,backendName:"cpu",kernelFunc:concat2};function conv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"conv2d");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,padLeft=convInfo.padInfo.left,padTop=convInfo.padInfo.top,isChannelsLast=convInfo.dataFormat==="channelsLast",y=new TensorBuffer(convInfo.outShape,x.dtype),xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),xBatchStride=xStrides[0],xRowStride=isChannelsLast?xStrides[1]:xStrides[2],xColStride=isChannelsLast?xStrides[2]:1,xChannelStride=isChannelsLast?1:xStrides[1],yBatchStride=y.strides[0],yRowStride=isChannelsLast?y.strides[1]:y.strides[2],yColStride=isChannelsLast?y.strides[2]:1,yChannelStride=isChannelsLast?1:y.strides[1],xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xBatchStride,yOffset1=b*yBatchStride;for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*yRowStride,xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xRowStride;for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*yColStride,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*xColStride,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1*xChannelStride];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset3+d2*yChannelStride]+=xVal*wVals[wOffset3+d2];wOffset3+=convInfo.outChannels}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,yVals)}var conv2DConfig={kernelName:Conv2D,backendName:"cpu",kernelFunc:conv2D};function conv2DBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"conv2dBackpropFilter");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,isChannelsLast=convInfo.dataFormat==="channelsLast",dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,xVals=backend2.data.get(x.dataId).values,dyVals=backend2.data.get(dy.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d1=0;d1<convInfo.inChannels;++d1)for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;isChannelsLast?dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2):dotProd+=xBuf.get(b,d1,xR,xC)*dyBuf.get(b,d2,yR,yC)}}dW.set(dotProd,wR,wC,d1,d2)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var conv2DBackpropFilterConfig={kernelName:Conv2DBackpropFilter,backendName:"cpu",kernelFunc:conv2DBackpropFilter2};function conv2DBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{inputShape,strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;assertNotComplex([dy,filter],"conv2dBackpropInput");let filterStrides=util_exports.computeStrides(filter.shape),dyStrides=util_exports.computeStrides(dy.shape),$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,1,pad3,dimRoundingMode,!1,$dataFormat),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,dyValues=backend2.data.get(dy.dataId).values,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo;$dataFormat=convInfo.dataFormat;let topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=$dataFormat==="channelsLast",xBatchStride=dx.strides[0],xRowStride=isChannelsLast?dx.strides[1]:dx.strides[2],xColStride=isChannelsLast?dx.strides[2]:1,xChannelStride=isChannelsLast?1:dx.strides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1];for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=yBatchStride*b+yRowStride*yR+yColStride*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+yChannelStride*d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}let dxOffset=xBatchStride*b+xRowStride*xR+xColStride*xC+xChannelStride*d1;dxValues[dxOffset]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv2DBackpropInputConfig={kernelName:Conv2DBackpropInput,backendName:"cpu",kernelFunc:conv2DBackpropInput2};function conv3D(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs;assertNotComplex([x,filter],"conv3d");let convInfo=backend_util_exports.computeConv3DInfo(x.shape,filter.shape,strides,dilations,pad3),{filterDepth,filterHeight,filterWidth,dilationDepth,dilationHeight,dilationWidth,padInfo}=convInfo,padFront=padInfo.front,padLeft=padInfo.left,padTop=padInfo.top,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values,xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yF=0;yF<convInfo.outDepth;++yF){let yOffset2=yOffset1+yF*y.strides[1],xFCorner=yF*convInfo.strideDepth-padFront;for(let wF=0;wF<filterDepth;++wF){let xF=xFCorner+wF*dilationDepth;if(xF<0||xF>=convInfo.inDepth)continue;let wOffset1=wF*filterStrides[0],xOffset2=xOffset1+xF*xStrides[1];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset3=yOffset2+yR*y.strides[2],xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset2=wOffset1+wR*filterStrides[1],xOffset3=xOffset2+xR*xStrides[2];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset4=yOffset3+yC*convInfo.outChannels,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset3=wOffset2+wC*filterStrides[2],xOffset4=xOffset3+xC*convInfo.inChannels,wOffset4=wOffset3;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset4+d1];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset4+d2]+=xVal*wVals[wOffset4+d2];wOffset4+=convInfo.outChannels}}}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var conv3DConfig={kernelName:Conv3D,backendName:"cpu",kernelFunc:conv3D};function conv3DBackpropFilterV2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,pad:pad3,filterShape}=attrs;assertNotComplex([x,dy],"conv3dBackpropFilterV2");let xStrides=util_exports.computeStrides(x.shape),dyStrides=util_exports.computeStrides(dy.shape),convInfo=backend_util_exports.computeConv3DInfo(x.shape,filterShape,strides,1,pad3),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dw=new TensorBuffer(convInfo.filterShape,"float32"),dwValues=dw.values,[dwS0,dwS1,dwS2,dwS3]=dw.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,xValues=backend2.data.get(x.dataId).values,[xS0,xS1,xS2,xS3]=xStrides,frontPad=convInfo.padInfo.front,leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top;for(let wF=0;wF<filterDepth;++wF){let yFMin=Math.max(0,Math.ceil((frontPad-wF)/strideDepth)),yFMax=Math.min(convInfo.outDepth,(convInfo.inDepth+frontPad-wF)/strideDepth),wOffset1=wF*dwS0;for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight),wOffset2=wR*dwS1+wOffset1;for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth),wOffset3=wC*dwS2+wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let wOffset4=d1*dwS3+wOffset3;for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xS0,yOffset1=b*dyS0;for(let yF=yFMin;yF<yFMax;++yF){let xOffset2=(wF+yF*strideDepth-frontPad)*xS1+xOffset1,yOffset2=yF*dyS1+yOffset1;for(let yR=yRMin;yR<yRMax;++yR){let xOffset3=(wR+yR*strideHeight-topPad)*xS2+xOffset2,yOffset3=yR*dyS2+yOffset2;for(let yC=yCMin;yC<yCMax;++yC){let xOffset4=(wC+yC*strideWidth-leftPad)*xS3+xOffset3,yOffset4=yC*dyS3+yOffset3;dotProd+=xValues[xOffset4+d1]*dyValues[yOffset4+d2]}}}}dwValues[wOffset4+d2]=dotProd}}}}}return backend2.makeTensorInfo(dw.shape,dw.dtype,dw.values)}var conv3DBackpropFilterV2Config={kernelName:Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:conv3DBackpropFilterV2};function conv3DBackpropInputV2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{pad:pad3,strides,inputShape}=attrs;assertNotComplex([dy],"conv3dBackpropInputV2");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv3DInfo(inputShape,filter.shape,strides,1,pad3),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2,dxS3]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2,fltS3]=filterStrides,{batchSize,filterDepth,filterHeight,filterWidth,inChannels,inDepth,inHeight,inWidth,outChannels,outDepth,outHeight,outWidth,strideDepth,strideHeight,strideWidth}=convInfo,frontPad=filterDepth-1-convInfo.padInfo.front,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xF=0;xF<inDepth;++xF){let xFCorner=xF-frontPad,xFMin=Math.max(0,Math.ceil(xFCorner/strideDepth)),yFMax=Math.min(outDepth,(filterDepth+xFCorner)/strideDepth);for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yF=xFMin;yF<yFMax;++yF){let wF=yF*strideDepth-xFCorner;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yF+dyS2*yR+dyS3*yC,fltOffset=fltS0*(filterDepth-1-wF)+fltS1*(filterHeight-1-wR)+fltS2*(filterWidth-1-wC)+fltS3*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}}dxValues[dxS0*b+dxS1*xF+dxS2*xR+dxS3*xC+d1]=dotProd}}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv3DBackpropInputV2Config={kernelName:Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:conv3DBackpropInputV2};var cos2=unaryKernelFunc(Cos,xi=>Math.cos(xi)),cosConfig={kernelName:Cos,backendName:"cpu",kernelFunc:cos2};var cosh2=unaryKernelFunc(Cosh,xi=>Math.cosh(xi)),coshConfig={kernelName:Cosh,backendName:"cpu",kernelFunc:cosh2};function cropAndResize2(args){let{inputs,backend:backend2,attrs}=args,{image:image3,boxes,boxInd}=inputs,{cropSize,method,extrapolationValue}=attrs,[batch,imageHeight,imageWidth,numChannels]=image3.shape,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,output=buffer([numBoxes,cropHeight,cropWidth,numChannels],"float32"),boxVals=backend2.data.get(boxes.dataId).values,boxIndVals=backend2.data.get(boxInd.dataId).values,imageVals=backend2.data.get(image3.dataId).values,inStride=util_exports.computeStrides(image3.shape),outStride=util_exports.computeStrides(output.shape);for(let b=0;b<numBoxes;b++){let startInd=b*4,y1=boxVals[startInd],x1=boxVals[startInd+1],y2=boxVals[startInd+2],x2=boxVals[startInd+3],bInd=boxIndVals[b];if(bInd>=batch)continue;let heightScale=cropHeight>1?(y2-y1)*(imageHeight-1)/(cropHeight-1):0,widthScale=cropWidth>1?(x2-x1)*(imageWidth-1)/(cropWidth-1):0;for(let y=0;y<cropHeight;y++){let yInd=cropHeight>1?y1*(imageHeight-1)+y*heightScale:.5*(y1+y2)*(imageHeight-1);if(yInd<0||yInd>imageHeight-1){for(let x=0;x<cropWidth;x++)for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}if(method==="bilinear"){let topInd=Math.floor(yInd),bottomInd=Math.ceil(yInd),yLerp=yInd-topInd;for(let x=0;x<cropWidth;x++){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let leftInd=Math.floor(xInd),rightInd=Math.ceil(xInd),xLerp=xInd-leftInd;for(let c=0;c<numChannels;c++){let ind=c+leftInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0],topLeft=imageVals[ind];ind=c+rightInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];let topRight=imageVals[ind];ind=c+leftInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomLeft=imageVals[ind];ind=c+rightInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomRight=imageVals[ind],top=topLeft+(topRight-topLeft)*xLerp,bottom=bottomLeft+(bottomRight-bottomLeft)*xLerp;ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0],output.values[ind]=top+(bottom-top)*yLerp}}}else for(let x=0;x<cropWidth;++x){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let closestX=Math.round(xInd),closestY=Math.round(yInd);for(let c=0;c<numChannels;c++){let inInd=c+closestX*inStride[2]+closestY*inStride[1]+bInd*inStride[0],outInd=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[outInd]=imageVals[inInd]}}}}return backend2.makeTensorInfo(output.shape,output.dtype,output.values)}var cropAndResizeConfig={kernelName:CropAndResize,backendName:"cpu",kernelFunc:cropAndResize2};function cumsum2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs;assertNotComplex(x,"cumsum");let permutation=backend_util_exports.getAxesPermutation([axis],x.shape.length),$x=x;permutation!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,x.shape.length)[0];if(permutedAxis!==$x.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length-1} but got axis=${permutedAxis}`);let resultDtype=upcastType($x.dtype,"int32"),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape),resultDtype),aVals=backend2.data.get($x.dataId).values,finalDim=$x.shape[$x.shape.length-1],indexAdjuster=reverse5?(i,j)=>i+finalDim-j-1:(i,j)=>i+j;for(let i=0;i<aVals.length;i+=finalDim)for(let j=0;j<finalDim;j++){let idx=indexAdjuster(i,j);if(j===0)vals[idx]=exclusive?0:aVals[idx];else{let prevIdx=indexAdjuster(i,j-1);vals[idx]=exclusive?aVals[prevIdx]+vals[prevIdx]:aVals[idx]+vals[prevIdx]}}let result=backend2.makeTensorInfo($x.shape,resultDtype,vals);if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose2({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo($x),reverseTransposedResult}return result}var cumsumConfig={kernelName:Cumsum,backendName:"cpu",kernelFunc:cumsum2};function denseBincount2(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.data.get(x.dataId).values,weightsVals=backend2.data.get(weights.dataId).values,outVals=bincountImpl(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImpl(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig={kernelName:DenseBincount,backendName:"cpu",kernelFunc:denseBincount2};function depthToSpace2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(dataFormat==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`),util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=x.shape[1],inputWidth=x.shape[2],inputDepth=x.shape[3],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),xValues=backend2.data.get(x.dataId).values,result=new Float32Array(batchSize*outputHeight*outputWidth*outputDepth),outputIdx=0;for(let b=0;b<batchSize;++b)for(let h=0;h<outputHeight;++h){let inH=Math.floor(h/blockSize),offsetH=h%blockSize;for(let w=0;w<outputWidth;++w){let inW=Math.floor(w/blockSize),offsetW=w%blockSize,offsetD=(offsetH*blockSize+offsetW)*outputDepth;for(let d=0;d<outputDepth;++d){let inputIdx=d+offsetD+inputDepth*(inW+inputWidth*(inH+inputHeight*b));result[outputIdx++]=xValues[inputIdx]}}}return backend2.makeTensorInfo([batchSize,outputHeight,outputWidth,outputDepth],x.dtype,result)}var depthToSpaceConfig={kernelName:DepthToSpace,backendName:"cpu",kernelFunc:depthToSpace2};function depthwiseConv2dNative(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"depthwiseConv2DNative");let xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),{filterHeight,filterWidth,dilationHeight,dilationWidth,padInfo}=convInfo,padLeft=padInfo.left,padTop=padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend2.data.get(x.dataId).values,wVals=backend2.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*y.strides[1],xRCorner=yR*convInfo.strideHeight-padLeft;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xStrides[1];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*y.strides[2],xCCorner=yC*convInfo.strideWidth-padTop;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*convInfo.inChannels,yOffset4=yOffset3,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1];for(let q=0;q<chMul;++q)yVals[yOffset4+q]+=xVal*wVals[wOffset3+q];yOffset4+=chMul,wOffset3+=chMul}}}}}}return backend2.makeTensorInfo(y.shape,y.dtype,y.values)}var depthwiseConv2dNativeConfig={kernelName:DepthwiseConv2dNative,backendName:"cpu",kernelFunc:depthwiseConv2dNative};function depthwiseConv2dNativeBackpropFilter2(args){let{inputs,backend:backend2,attrs}=args,{x,dy}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"depthwiseConv2dNativeBackpropFilter");let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,dilations,pad3,dimRoundingMode,!0),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,xVals=backend2.data.get(x.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyVals=backend2.data.get(dy.dataId).values,dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d2=0;d2<convInfo.outChannels;++d2){let d1=Math.trunc(d2/chMul),dm=d2%chMul,dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2)}}dW.set(dotProd,wR,wC,d1,dm)}}}return backend2.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var depthwiseConv2dNativeBackpropFilterConfig={kernelName:DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropFilter2};function depthwiseConv2dNativeBackpropInput2(args){let{inputs,backend:backend2,attrs}=args,{dy,filter}=inputs,{strides,dilations,pad:pad3,dimRoundingMode,inputShape}=attrs;assertNotComplex([dy,filter],"depthwiseConv2DNativeBackpropInput");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2]=dx.strides,dyValues=backend2.data.get(dy.dataId).values,[dyS0,dyS1,dyS2]=dyStrides,fltValues=backend2.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,chMul=outChannels/inChannels;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yR+dyS2*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let dm=0;dm<chMul;++dm){let d2=d1*chMul+dm,pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+dm];dotProd+=pixel*weight}}}dxValues[dxS0*b+dxS1*xR+dxS2*xC+d1]=dotProd}}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var depthwiseConv2dNativeBackpropInputConfig={kernelName:DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropInput2};function diag2(args){let{inputs,backend:backend2}=args,{x}=inputs,xSize=util_exports.sizeFromShape(x.shape),xVals=backend2.data.get(x.dataId).values,outBuf=buffer([xSize,xSize],x.dtype),vals=outBuf.values;for(let i=0;i<xVals.length;i++)vals[i*xSize+i]=xVals[i];let outShape=[...x.shape,...x.shape];return backend2.makeTensorInfo(outShape,outBuf.dtype,outBuf.values)}var diagConfig={kernelName:Diag,backendName:"cpu",kernelFunc:diag2};var dilation2dConfig={kernelName:Dilation2D,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,xVals=cpuBackend.data.get(x.dataId).values,xRank=x.shape.length,filterVals=cpuBackend.data.get(filter.dataId).values,filterRank=filter.shape.length,{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations),outSize=util_exports.sizeFromShape(outShape),outRank=outShape.length,outputVals=util_exports.getArrayFromDType(x.dtype,outSize);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let xIndex=util_exports.locToIndex([b,hIn,wIn,d],xRank,util_exports.computeStrides(x.shape)),filterIndex=util_exports.locToIndex([h,w,d],filterRank,util_exports.computeStrides(filter.shape)),val=xVals[xIndex]+filterVals[filterIndex];val>curVal&&(curVal=val)}}}let outputIndex=util_exports.locToIndex([b,hOut,wOut,d],outRank,util_exports.computeStrides(outShape));outputVals[outputIndex]=curVal}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(outputVals,x.dtype),outShape,x.dtype),shape:outShape,dtype:x.dtype}}};var dilation2dBackpropFilterConfig={kernelName:Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(filter.shape,filter.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hMax=0,wMax=0;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hMax=h,wMax=w)}}}gradients[hMax][wMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),filter.shape,filter.dtype),shape:filter.shape,dtype:filter.dtype}}};var dilation2dBackpropInputConfig={kernelName:Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs,backend:backend2,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad3,dilations}=attrs,cpuBackend=backend2,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad3,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients=util_exports.makeZerosNestedTypedArray(x.shape,x.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hInMax=hBeg<0?0:hBeg,wInMax=wBeg<0?0:wBeg;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hInMax=hIn,wInMax=wIn)}}}gradients[b][hInMax][wInMax][d]+=$dy[b][hOut][wOut][d]}}}return{dataId:cpuBackend.write(util_exports.toTypedArray(gradients,x.dtype),x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}};function eluGrad(args){let{inputs,backend:backend2}=args,{dy,y}=inputs;assertNotComplex([dy,y],"eluGrad");let resultValues=new Float32Array(util_exports.sizeFromShape(y.shape)),values=backend2.data.get(y.dataId).values,dyValues=backend2.data.get(dy.dataId).values;for(let i=0;i<values.length;++i){let v=values[i];v>=1?resultValues[i]=dyValues[i]:resultValues[i]=dyValues[i]*(v+1)}return backend2.makeTensorInfo(y.shape,"float32",resultValues)}var eluGradConfig2={kernelName:EluGrad,backendName:"cpu",kernelFunc:eluGrad};var equalImpl=createSimpleBinaryKernelImpl((a,b)=>a===b?1:0),equal2=binaryKernelFunc(Equal,equalImpl,null,"bool"),equalConfig={kernelName:Equal,backendName:"cpu",kernelFunc:equal2};var p=backend_util_exports.ERF_P,a1=backend_util_exports.ERF_A1,a2=backend_util_exports.ERF_A2,a3=backend_util_exports.ERF_A3,a4=backend_util_exports.ERF_A4,a5=backend_util_exports.ERF_A5,erf2=unaryKernelFunc(Erf,xi=>{let sign4=Math.sign(xi),v=Math.abs(xi),t=1/(1+p*v);return sign4*(1-((((a5*t+a4)*t+a3)*t+a2)*t+a1)*t*Math.exp(-v*v))}),erfConfig={kernelName:Erf,backendName:"cpu",kernelFunc:erf2};function expandDims3(args){let{inputs,backend:backend2,attrs}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig={kernelName:ExpandDims,backendName:"cpu",kernelFunc:expandDims3};var realDivImpl=createSimpleBinaryKernelImpl((a,b)=>a/b),div2=binaryKernelFunc(RealDiv,realDivImpl),realDivConfig={kernelName:RealDiv,backendName:"cpu",kernelFunc:div2};function fftBatch(input2,inverse,cpuBackend){let inputShape=input2.shape,batch=inputShape[0],innerDim=inputShape[1],inputVals=cpuBackend.data.get(input2.dataId),real2D=inputVals.complexTensorInfos.real,imag2D=inputVals.complexTensorInfos.imag,resultShape=[batch,innerDim],resultSize=util_exports.sizeFromShape(resultShape),resultReal=util_exports.getTypedArrayFromDType("float32",resultSize),resultImag=util_exports.getTypedArrayFromDType("float32",resultSize);for(let b=0;b<batch;b++){let r=slice2({inputs:{x:real2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),i=slice2({inputs:{x:imag2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),input3=complex2({inputs:{real:r,imag:i},backend:cpuBackend}),{real:real4,imag:imag4}=fftImpl(input3,inverse,cpuBackend),res=backend_util_exports.mergeRealAndImagArrays(real4,imag4);for(let d=0;d<innerDim;d++){let c=backend_util_exports.getComplexWithIndex(res,d);resultReal[b*innerDim+d]=c.real,resultImag[b*innerDim+d]=c.imag}cpuBackend.disposeIntermediateTensorInfo(r),cpuBackend.disposeIntermediateTensorInfo(i),cpuBackend.disposeIntermediateTensorInfo(input3)}let $realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultReal),$imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultImag),result=complex2({inputs:{real:$realInfo,imag:$imagInfo},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($realInfo),cpuBackend.disposeIntermediateTensorInfo($imagInfo),result}function fftImpl(input2,inverse,cpuBackend){let inputSize=util_exports.sizeFromShape(input2.shape),inputVals=cpuBackend.data.get(input2.dataId),realVals=cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values,imagVals=cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;if(isExponentOf2(inputSize)){let result=fftRadix2(realVals,imagVals,inputSize,inverse,cpuBackend),resultShape=[input2.shape[0],input2.shape[1]];if(inverse){let realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.real),imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.imag),sizeInfo=cpuBackend.makeTensorInfo([],"float32",util_exports.createScalarValue(inputSize,"float32")),sizeInfoCopy=identity2({inputs:{x:sizeInfo},backend:cpuBackend}),divRealInfo=realDivConfig.kernelFunc({inputs:{a:realInfo,b:sizeInfo},backend:cpuBackend}),divImagInfo=realDivConfig.kernelFunc({inputs:{a:imagInfo,b:sizeInfoCopy},backend:cpuBackend}),divRealVals=cpuBackend.data.get(divRealInfo.dataId).values,divImagVals=cpuBackend.data.get(divImagInfo.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(realInfo),cpuBackend.disposeIntermediateTensorInfo(imagInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy),cpuBackend.disposeIntermediateTensorInfo(divRealInfo),cpuBackend.disposeIntermediateTensorInfo(divImagInfo),{real:divRealVals,imag:divImagVals}}return result}else{let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),rawOutput=fourierTransformByMatmul(data,inputSize,inverse);return backend_util_exports.splitRealAndImagArrays(rawOutput)}}function isExponentOf2(size){return(size&size-1)==0}function fftRadix2(realVals,imagVals,size,inverse,cpuBackend){if(size===1)return{real:realVals,imag:imagVals};let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),half=size/2,evenComplex=backend_util_exports.complexWithEvenIndex(data),evenRealVals=evenComplex.real,evenImagVals=evenComplex.imag,evenShape=[evenRealVals.length],evenRealInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenRealVals),evenImagInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenImagVals),evenTensorInfo=complex2({inputs:{real:evenRealInfo,imag:evenImagInfo},backend:cpuBackend}),oddComplex=backend_util_exports.complexWithOddIndex(data),oddRealVals=oddComplex.real,oddImagVals=oddComplex.imag,oddShape=[oddRealVals.length],oddRealInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddRealVals),oddImagInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddImagVals),oddTensorInfo=complex2({inputs:{real:oddRealInfo,imag:oddImagInfo},backend:cpuBackend}),$evenComplex=fftRadix2(evenRealVals,evenImagVals,half,inverse,cpuBackend),$evenRealVals=$evenComplex.real,$evenImagVals=$evenComplex.imag,$evenShape=[$evenRealVals.length],$evenRealInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenRealVals),$evenImagInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenImagVals),$evenTensorInfo=complex2({inputs:{real:$evenRealInfo,imag:$evenImagInfo},backend:cpuBackend}),$oddComplex=fftRadix2(oddRealVals,oddImagVals,half,inverse,cpuBackend),$oddRealVals=$oddComplex.real,$oddImagVals=$oddComplex.imag,$oddShape=[$oddRealVals.length],$oddRealInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddRealVals),$oddImagInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddImagVals),$oddTensorInfo=complex2({inputs:{real:$oddRealInfo,imag:$oddImagInfo},backend:cpuBackend}),e=backend_util_exports.exponents(size,inverse),eShape=[e.real.length],eRealInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.real),eImagInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.imag),complexInfo=complex2({inputs:{real:eRealInfo,imag:eImagInfo},backend:cpuBackend}),exponentInfo=multiply2({inputs:{a:complexInfo,b:$oddTensorInfo},backend:cpuBackend}),addPart=add4({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),subPart=sub2({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),addPartReal=real2({inputs:{input:addPart},backend:cpuBackend}),subPartReal=real2({inputs:{input:subPart},backend:cpuBackend}),addPartImag=imag2({inputs:{input:addPart},backend:cpuBackend}),subPartImag=imag2({inputs:{input:subPart},backend:cpuBackend}),$real=concat2({inputs:[addPartReal,subPartReal],backend:cpuBackend,attrs:{axis:0}}),$imag=concat2({inputs:[addPartImag,subPartImag],backend:cpuBackend,attrs:{axis:0}}),$realVals=cpuBackend.data.get($real.dataId).values,$imagVals=cpuBackend.data.get($imag.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(evenRealInfo),cpuBackend.disposeIntermediateTensorInfo(evenImagInfo),cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo(oddRealInfo),cpuBackend.disposeIntermediateTensorInfo(oddImagInfo),cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo($evenRealInfo),cpuBackend.disposeIntermediateTensorInfo($evenImagInfo),cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo($oddRealInfo),cpuBackend.disposeIntermediateTensorInfo($oddImagInfo),cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo(eRealInfo),cpuBackend.disposeIntermediateTensorInfo(eImagInfo),cpuBackend.disposeIntermediateTensorInfo(complexInfo),cpuBackend.disposeIntermediateTensorInfo(exponentInfo),cpuBackend.disposeIntermediateTensorInfo(addPart),cpuBackend.disposeIntermediateTensorInfo(subPart),cpuBackend.disposeIntermediateTensorInfo(addPartReal),cpuBackend.disposeIntermediateTensorInfo(addPartImag),cpuBackend.disposeIntermediateTensorInfo(subPartReal),cpuBackend.disposeIntermediateTensorInfo(subPartImag),cpuBackend.disposeIntermediateTensorInfo($real),cpuBackend.disposeIntermediateTensorInfo($imag),{real:$realVals,imag:$imagVals}}function fourierTransformByMatmul(data,size,inverse){let ret=new Float32Array(size*2);for(let r=0;r<size;r++){let real4=0,imag4=0;for(let c=0;c<size;c++){let e=backend_util_exports.exponent(r*c,size,inverse),term=backend_util_exports.getComplexWithIndex(data,c);real4+=term.real*e.real-term.imag*e.imag,imag4+=term.real*e.imag+term.imag*e.real}inverse&&(real4/=size,imag4/=size),backend_util_exports.assignToTypedArray(ret,real4,imag4,r)}return ret}function fft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!1,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var fftConfig={kernelName:FFT,backendName:"cpu",kernelFunc:fft2};function fill2(args){let{backend:backend2,attrs}=args,{shape,value,dtype}=attrs,$dtype=dtype||util_exports.inferDtype(value),values=util_exports.getArrayFromDType($dtype,util_exports.sizeFromShape(shape));return fillValues(values,value,$dtype),backend2.makeTensorInfo(shape,$dtype,values)}var fillConfig={kernelName:Fill,backendName:"cpu",kernelFunc:fill2};function fillValues(values,value,dtype){values.fill(value)}var flipLeftRightConfig={kernelName:FlipLeftRight,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let x=[batch,row,col,channel][2],coordX=Math.round(imageWidth-x),outIdx=batchOffset+rowOffset+colOffset+channel,outputValue=imageVals[outIdx];if(coordX>=0&&coordX<imageWidth){let rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}};var floorDivImpl=createSimpleBinaryKernelImpl((a,b)=>Math.floor(a/b)),floorDiv2=binaryKernelFunc(FloorDiv,floorDivImpl,null,"int32"),floorDivConfig={kernelName:FloorDiv,backendName:"cpu",kernelFunc:floorDiv2};function fusedConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=conv2D({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let resultOld=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(resultOld)}if(activation2){let resultOld=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(resultOld)}return result}var fusedConv2DConfig={kernelName:FusedConv2D,backendName:"cpu",kernelFunc:fusedConv2D};function fusedDepthwiseConv2D(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,result=depthwiseConv2dNative({inputs:{x,filter},backend:backend2,attrs:{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}});if(bias){let oldResult=result;result=add4({inputs:{a:result,b:bias},backend:backend2}),backend2.disposeIntermediateTensorInfo(oldResult)}if(activation2){let oldResult=result;result=applyActivation2(backend2,result,activation2,preluActivationWeights,leakyreluAlpha),backend2.disposeIntermediateTensorInfo(oldResult)}return result}var fusedDepthwiseConv2DConfig={kernelName:FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:fusedDepthwiseConv2D};function gatherNd(args){let{inputs,backend:backend2}=args,{params,indices}=inputs,paramsSize=util_exports.sizeFromShape(params.shape),indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(params,indices);if(numSlices===0)return backend2.makeTensorInfo(resultShape,params.dtype,[]);let outBuf=buffer([numSlices,sliceSize],params.dtype),indicesData=backend2.data.get(indices.dataId).values,paramsData=backend2.data.get(params.dataId).values;for(let i=0;i<numSlices;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];flattenIndex+=dim*strides[j],index.push(dim)}if(flattenIndex<0||flattenIndex>=paramsSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${params.shape}`);for(let k=0;k<sliceSize;k++)outBuf.values[i*sliceSize+k]=paramsData[flattenIndex*sliceSize+k]}return backend2.makeTensorInfo(resultShape,outBuf.dtype,outBuf.values)}var gatherNdConfig={kernelName:GatherNd,backendName:"cpu",kernelFunc:gatherNd};function gatherV2(args){let{inputs,backend:backend2,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs;assertNotComplex([x,indices],"gatherV2");let $batchDims=batchDims;batchDims==null&&($batchDims=0);let indicesSize=util_exports.sizeFromShape(indices.shape),parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,$batchDims),flattenX=reshape3({inputs:{x},backend:backend2,attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]}}),flattenIndex=reshape3({inputs:{x:indices},backend:backend2,attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]}}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],indicesBuf=backend2.bufferSync(flattenIndex),xBuf=backend2.bufferSync(flattenX),outBuf=gatherV2Impl(xBuf,indicesBuf,flattenOutputShape);return backend2.disposeIntermediateTensorInfo(flattenX),backend2.disposeIntermediateTensorInfo(flattenIndex),backend2.makeTensorInfo(shapeInfo.outputShape,outBuf.dtype,outBuf.values)}var gatherV2Config={kernelName:GatherV2,backendName:"cpu",kernelFunc:gatherV2};var greaterEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a>=b?1:0),greaterEqual2=binaryKernelFunc(GreaterEqual,greaterEqualImpl,null,"bool"),greaterEqualConfig={kernelName:GreaterEqual,backendName:"cpu",kernelFunc:greaterEqual2};function ifft2(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape3({inputs:{x:input2},backend:backend2,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!0,backend2),resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:input2.shape}});return backend2.disposeIntermediateTensorInfo(input2D),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var ifftConfig={kernelName:IFFT,backendName:"cpu",kernelFunc:ifft2};var isFinite3=unaryKernelFunc(IsFinite,xi=>Number.isFinite(xi)?1:0,"bool"),isFiniteConfig={kernelName:IsFinite,backendName:"cpu",kernelFunc:isFinite3};var isInf2=unaryKernelFunc(IsInf,xi=>Math.abs(xi)===Infinity?1:0,"bool"),isInfConfig={kernelName:IsInf,backendName:"cpu",kernelFunc:isInf2};var isNaN3=unaryKernelFunc(IsNan,xi=>Number.isNaN(xi)?1:0,"bool"),isNaNConfig={kernelName:IsNan,backendName:"cpu",kernelFunc:isNaN3};var lessEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a<=b?1:0),lessEqual2=binaryKernelFunc(LessEqual,lessEqualImpl,null,"bool"),lessEqualConfig={kernelName:LessEqual,backendName:"cpu",kernelFunc:lessEqual2};function linSpace(args){let{backend:backend2,attrs}=args,{start,stop,num}=attrs,outVals=linSpaceImpl(start,stop,num);return backend2.makeTensorInfo([outVals.length],"float32",outVals)}var linSpaceConfig={kernelName:LinSpace,backendName:"cpu",kernelFunc:linSpace};var log1p2=unaryKernelFunc(Log1p,xi=>Math.log1p(xi)),log1pConfig={kernelName:Log1p,backendName:"cpu",kernelFunc:log1p2};var logicalAndImpl=createSimpleBinaryKernelImpl((a,b)=>a&&b),logicalAnd2=binaryKernelFunc(LogicalAnd,logicalAndImpl,null,"bool"),logicalAndConfig={kernelName:LogicalAnd,backendName:"cpu",kernelFunc:logicalAnd2};var logicalNot2=unaryKernelFunc(LogicalNot,xi=>xi?0:1,"bool"),logicalNotConfig={kernelName:LogicalNot,backendName:"cpu",kernelFunc:logicalNot2};var logicalOrImpl=createSimpleBinaryKernelImpl((a,b)=>a||b),logicalOr2=binaryKernelFunc(LogicalOr,logicalOrImpl,null,"bool"),logicalOrConfig={kernelName:LogicalOr,backendName:"cpu",kernelFunc:logicalOr2};function lRN(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(x,"LRN");let channels=x.shape[3],maxD=channels-1,xValues=backend2.data.get(x.dataId).values,size=util_exports.sizeFromShape(x.shape),result=new Float32Array(size);function sumAcrossChannels(offset){let currentChannel=offset%channels,beginSumOffset=offset-currentChannel+Math.max(0,currentChannel-depthRadius),endSumOffset=offset-currentChannel+Math.min(currentChannel+depthRadius,maxD),sum6=0;for(;beginSumOffset<=endSumOffset;beginSumOffset++){let z=xValues[beginSumOffset];sum6+=z*z}return sum6}for(let offset=0;offset<size;offset++){let sum6=sumAcrossChannels(offset),val=xValues[offset]*Math.pow(bias+alpha*sum6,-beta);result[offset]=val}return backend2.makeTensorInfo(x.shape,x.dtype,result)}var lRNConfig={kernelName:LRN,backendName:"cpu",kernelFunc:lRN};function lRNGrad(args){let{inputs,backend:backend2,attrs}=args,{x,y,dy}=inputs,{depthRadius,bias,alpha,beta}=attrs;assertNotComplex(dy,"LRNGrad");let dySize=util_exports.sizeFromShape(dy.shape),channels=dy.shape[3],dyValues=backend2.data.get(dy.dataId).values,xValues=backend2.data.get(x.dataId).values,yValues=backend2.data.get(y.dataId).values,result=new Float32Array(dySize),size=dySize;for(let offset=0;offset<size;offset++){let currentChannel=offset%channels,depthBegin=offset-currentChannel+Math.max(0,currentChannel-depthRadius),depthEnd=offset-currentChannel+Math.min(channels,currentChannel+depthRadius+1),norm2=0;for(let k=depthBegin;k<depthEnd;k++)norm2+=Math.pow(xValues[k],2);norm2=alpha*norm2+bias;for(let k=depthBegin;k<depthEnd;k++){let dyi=-2*alpha*beta*xValues[k]*yValues[offset]/norm2;offset===k&&(dyi+=Math.pow(norm2,-beta)),dyi*=dyValues[offset],result[k]+=dyi}}return backend2.makeTensorInfo(dy.shape,x.dtype,result)}var lRNGradConfig={kernelName:LRNGrad,backendName:"cpu",kernelFunc:lRNGrad};function max3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reductionIndices,keepDims}=attrs,cpuBackend=backend2,xShape=x.shape,xRank=xShape.length,origAxes=util_exports.parseAxisParam(reductionIndices,xShape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xVals=cpuBackend.data.get(x.dataId).values;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];xVals=transposeImpl(xVals,xShape,x.dtype,permutedAxes,newShape),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xShape=newShape}assertNotComplex(x,"max"),backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),result=maxImpl(xVals,reduceSize,maxOutShape,x.dtype),dataId=cpuBackend.write(result,maxOutShape,x.dtype),outShape=maxOutShape;return keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes)),{dataId,shape:outShape,dtype:x.dtype}}var maxConfig={kernelName:Max,backendName:"cpu",kernelFunc:max3};function maxPool2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs;assertNotComplex(x,"maxPool");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad3,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend2});else{let xValues=backend2.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer2=pool2(xValues,x.shape,x.dtype,strides2,convInfo,"max");res=backend2.makeTensorInfo(convInfo.outShape,x.dtype,buffer2.values)}return res}var maxPoolConfig={kernelName:MaxPool,backendName:"cpu",kernelFunc:maxPool2};function maxPool3D(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations}=attrs;assertNotComplex(x,"maxPool3d");let $dilations=dilations;$dilations==null&&($dilations=[1,1,1]);let convInfo=backend_util_exports.computePool3DInfo(x.shape,filterSize,strides,$dilations,pad3,dimRoundingMode,dataFormat),xValues=backend2.data.get(x.dataId).values,outBuf=pool3d2(xValues,x.shape,x.dtype,util_exports.computeStrides(x.shape),convInfo,"max");return backend2.makeTensorInfo(outBuf.shape,"float32",outBuf.values)}var maxPool3DConfig={kernelName:MaxPool3D,backendName:"cpu",kernelFunc:maxPool3D};function maxPool3DGrad(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2}=inputs,{filterSize,strides,pad:pad3,dilations,dimRoundingMode}=attrs;assertNotComplex([dy,input2],"maxPool3DGrad");let convInfo=backend_util_exports.computePool3DInfo(input2.shape,filterSize,strides,dilations,pad3,dimRoundingMode),inputBuf=backend2.bufferSync(input2),maxPosBuf=maxPool3dPositions(inputBuf,convInfo),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(input2.shape,"float32"),dyBuf=backend2.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(!(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth))for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(!(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow))for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;let maxPos=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(batch,dyDepth,dyRow,dyCol,channel),curPos=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterWidth+wCol,mask=maxPos===curPos?1:0;if(mask===0)continue;dotProd+=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel)*mask}}}dx.set(dotProd,batch,dxDepth,dxRow,dxCol,channel)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPool3DGradConfig2={kernelName:MaxPool3DGrad,backendName:"cpu",kernelFunc:maxPool3DGrad};function maxPoolGrad2(args){let{inputs,backend:backend2,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex([input2,output],"maxPoolGrad");let{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),xValues=backend2.data.get(x.dataId).values,maxPosBuf=buffer(convInfo.outShape,x.dtype,maxPoolPositions(xValues,x.shape,x.dtype,convInfo).values),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),dyData=backend2.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(!(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR))for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;let maxPos=effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(b,dyR,dyC,d),curPos=wR*effectiveFilterWidth+wC,mask=maxPos===curPos?1:0;if(mask===0)continue;dotProd+=dyBuf.get(b,dyR,dyC,d)*mask}}dx.set(dotProd,b,dxR,dxC,d)}return backend2.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPoolGradConfig2={kernelName:MaxPoolGrad,backendName:"cpu",kernelFunc:maxPoolGrad2};function maxPoolWithArgmaxImpl(xValues,xShape,dtype,includeBatchInIndex,convInfo){let strides=util_exports.computeStrides(xShape),maxPools=pool2(xValues,xShape,dtype,strides,convInfo,"max"),maxPositions=maxPoolPositions(xValues,xShape,dtype,convInfo,!0,includeBatchInIndex);return[maxPools.values,maxPositions.values]}var maxPoolWithArgmaxConfig={kernelName:MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{x}=inputs,{filterSize,strides,pad:pad3,includeBatchInIndex}=attrs,cpuBackend=backend2;assertNotComplex(x,"MaxPoolWithArgmax");let values=cpuBackend.data.get(x.dataId).values,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,[1,1],pad3),[pooled,indexes]=maxPoolWithArgmaxImpl(values,x.shape,x.dtype,includeBatchInIndex,convInfo),pooledDataId=cpuBackend.write(pooled,convInfo.outShape,x.dtype),indexesDataId=cpuBackend.write(indexes,convInfo.outShape,x.dtype);return[{dataId:pooledDataId,shape:convInfo.outShape,dtype:x.dtype},{dataId:indexesDataId,shape:convInfo.outShape,dtype:"int32"}]}};function sum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"sum");let $x;x.dtype==="bool"?$x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"int32"}}):$x=identity2({inputs:{x},backend:backend2});let xRank=$x.shape.length,axes=util_exports.parseAxisParam(axis,$x.shape),permutation=backend_util_exports.getAxesPermutation(axes,xRank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose2({inputs:{x:$x},backend:backend2,attrs:{perm:permutation}}),reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,xRank)),backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,permutedX.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(permutedX.shape,reductionAxes),resultDtype=backend_util_exports.upcastType(permutedX.dtype,"int32"),result=zeros3(backend2,outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=backend2.data.get(result.dataId).values,aVals=backend2.data.get(permutedX.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,sum6=0;for(let j=0;j<reduceSize;++j)sum6+=aVals[offset+j];vals[i]=sum6}if(keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(result.shape,axes),oldResult=result;result=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:newShape}}),backend2.disposeIntermediateTensorInfo(oldResult)}return backend2.disposeIntermediateTensorInfo($x),permutation!=null&&backend2.disposeIntermediateTensorInfo(permutedX),result}var sumConfig={kernelName:Sum,backendName:"cpu",kernelFunc:sum3};function mean2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs,axes=util_exports.parseAxisParam(axis,x.shape),reduceShape=backend_util_exports.computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=util_exports.sizeFromShape(reduceShape),toDispose=[],reduceSizeScalar=backend2.makeTensorInfo([],"float32",new Float32Array([reduceSize]));toDispose.push(reduceSizeScalar);let $x=cast3({inputs:{x},backend:backend2,attrs:{dtype:"float32"}});toDispose.push($x);let res=div2({inputs:{a:$x,b:reduceSizeScalar},backend:backend2});toDispose.push(res);let result=sum3({inputs:{x:res},backend:backend2,attrs:{axis,keepDims}});return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var meanConfig={kernelName:Mean,backendName:"cpu",kernelFunc:mean2};function min3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,keepDims}=attrs;assertNotComplex(x,"min");let origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,x.shape.length),$x=x;permutedAxes!=null&&($x=transpose2({inputs:{x},backend:backend2,attrs:{perm:permutedAxes}}),axes=backend_util_exports.getInnerMostAxes(axes.length,x.shape.length)),backend_util_exports.assertAxesAreInnerMostDims("min",axes,$x.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes($x.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),vals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape),$x.dtype),aVals=backend2.data.get($x.dataId).values;for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min6=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min6&&(min6=value)}vals[i]=min6}permutedAxes!=null&&backend2.disposeIntermediateTensorInfo($x);let result=backend2.makeTensorInfo(outShape,$x.dtype,vals);if(keepDims){let expandedShape=backend_util_exports.expandShapeToKeepDim(outShape,origAxes),reshapedResult=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:expandedShape}});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}return result}var minConfig={kernelName:Min,backendName:"cpu",kernelFunc:min3};function mirrorPad2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,mode}=attrs;assertNotComplex(x,"mirrorPad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),end=paddings.map((p2,i)=>p2[0]+x.shape[i]),offset=mode==="reflect"?0:1,xVals=backend2.data.get(x.dataId).values,xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);for(let i=0;i<resultSize;i++){let coords2=util_exports.indexToLoc(i,resultRank,resultStrides);for(let i2=0;i2<resultRank;i2++)coords2[i2]<start[i2]?coords2[i2]=start[i2]*2-coords2[i2]-offset:coords2[i2]>=end[i2]&&(coords2[i2]=(end[i2]-1)*2-coords2[i2]+offset);coords2=coords2.map((c,i2)=>c-start[i2]);let inIndex=util_exports.locToIndex(coords2,xRank,xStrides);resVals[i]=xVals[inIndex]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var mirrorPadConfig={kernelName:MirrorPad,backendName:"cpu",kernelFunc:mirrorPad2};var modImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>{let rem=aValue%bValue;return aValue<0&&bValue<0||aValue>=0&&bValue>=0?rem:(rem+bValue)%bValue}),mod2=binaryKernelFunc(Mod,modImpl),modConfig={kernelName:Mod,backendName:"cpu",kernelFunc:mod2};var seedrandom4=__toModule(require_seedrandom2());function softmax3(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{dim}=attrs,logitsRank=logits.shape.length,$dim=dim;if($dim===-1&&($dim=logitsRank-1),$dim!==logitsRank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);let axes=util_exports.parseAxisParam([$dim],logits.shape),maxLogit=max3({inputs:{x:logits},backend:backend2,attrs:{reductionIndices:axes,keepDims:!1}}),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),maxLogitReshaped=reshape3({inputs:{x:maxLogit},backend:backend2,attrs:{shape:expandedShape}}),a=sub2({inputs:{a:logits,b:maxLogitReshaped},backend:backend2}),b=exp2({inputs:{x:a},backend:backend2}),sumExp=sum3({inputs:{x:b},backend:backend2,attrs:{axis:axes,keepDims:!1}}),sumReshaped=reshape3({inputs:{x:sumExp},backend:backend2,attrs:{shape:expandedShape}}),result=div2({inputs:{a:b,b:sumReshaped},backend:backend2});return backend2.disposeIntermediateTensorInfo(maxLogit),backend2.disposeIntermediateTensorInfo(maxLogitReshaped),backend2.disposeIntermediateTensorInfo(a),backend2.disposeIntermediateTensorInfo(b),backend2.disposeIntermediateTensorInfo(sumExp),backend2.disposeIntermediateTensorInfo(sumReshaped),result}var softmaxConfig={kernelName:Softmax,backendName:"cpu",kernelFunc:softmax3};function multinomial2(args){let{inputs,backend:backend2,attrs}=args,{logits}=inputs,{numSamples,seed,normalized}=attrs;assertNotComplex(logits,"multinomial");let probabilities=normalized?logits:softmax3({inputs:{logits},backend:backend2,attrs:{dim:-1}}),batchSize=probabilities.shape[0],numEvents=probabilities.shape[1],probVals=backend2.data.get(probabilities.dataId).values,resShape=[batchSize,numSamples],resVals=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape),"int32");for(let b=0;b<batchSize;++b){let offset=b*numEvents,cdf=new Float32Array(numEvents-1);cdf[0]=probVals[offset];for(let event=1;event<cdf.length;++event)cdf[event]=cdf[event-1]+probVals[offset+event];let random=seedrandom4.alea(seed.toString()),outOffset=b*numSamples;for(let sampleId=0;sampleId<numSamples;++sampleId){let r=random();resVals[outOffset+sampleId]=cdf.length;for(let event=0;event<cdf.length;event++)if(r<cdf[event]){resVals[outOffset+sampleId]=event;break}}}return normalized||backend2.disposeIntermediateTensorInfo(probabilities),backend2.makeTensorInfo(resShape,"int32",resVals)}var multinomialConfig={kernelName:Multinomial,backendName:"cpu",kernelFunc:multinomial2};var nonMaxSuppressionV3Impl2=kernel_impls_exports.nonMaxSuppressionV3Impl;function nonMaxSuppressionV3(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold}=attrs;assertNotComplex(boxes,"NonMaxSuppression");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices}=nonMaxSuppressionV3Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices))}var nonMaxSuppressionV3Config={kernelName:NonMaxSuppressionV3,backendName:"cpu",kernelFunc:nonMaxSuppressionV3};var nonMaxSuppressionV4Impl2=kernel_impls_exports.nonMaxSuppressionV4Impl;function nonMaxSuppressionV4(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs;assertNotComplex(boxes,"NonMaxSuppressionPadded");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([],"int32",new Int32Array([validOutputs]))]}var nonMaxSuppressionV4Config={kernelName:NonMaxSuppressionV4,backendName:"cpu",kernelFunc:nonMaxSuppressionV4};var nonMaxSuppressionV5Impl2=kernel_impls_exports.nonMaxSuppressionV5Impl;function nonMaxSuppressionV5(args){let{inputs,backend:backend2,attrs}=args,{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs;assertNotComplex(boxes,"NonMaxSuppressionWithScore");let boxesVals=backend2.data.get(boxes.dataId).values,scoresVals=backend2.data.get(scores.dataId).values,maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl2(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[backend2.makeTensorInfo([selectedIndices.length],"int32",new Int32Array(selectedIndices)),backend2.makeTensorInfo([selectedScores.length],"float32",new Float32Array(selectedScores))]}var nonMaxSuppressionV5Config={kernelName:NonMaxSuppressionV5,backendName:"cpu",kernelFunc:nonMaxSuppressionV5};function oneHot2(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs;assertNotComplex(indices,"oneHot");let indicesSize=util_exports.sizeFromShape(indices.shape),res=new Float32Array(indicesSize*depth);res.fill(offValue);let indicesVal=backend2.data.get(indices.dataId).values;for(let event=0;event<indicesSize;++event)indicesVal[event]>=0&&indicesVal[event]<depth&&(res[event*depth+indicesVal[event]]=onValue);return backend2.makeTensorInfo([...indices.shape,depth],"int32",res)}var oneHotConfig={kernelName:OneHot,backendName:"cpu",kernelFunc:oneHot2};function zerosLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=zerosLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:0,dtype:x.dtype}})}var zerosLikeConfig={kernelName:ZerosLike,backendName:"cpu",kernelFunc:zerosLike2};function onesLike2(args){let{inputs,backend:backend2}=args,{x}=inputs;if(x.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(x.dtype==="complex64"){let realPart=real2({inputs:{input:x},backend:backend2}),r=onesLike2({inputs:{x:realPart},backend:backend2}),imagPart=imag2({inputs:{input:x},backend:backend2}),i=zerosLike2({inputs:{x:imagPart},backend:backend2}),result=complex2({inputs:{real:r,imag:i},backend:backend2});return backend2.disposeIntermediateTensorInfo(realPart),backend2.disposeIntermediateTensorInfo(r),backend2.disposeIntermediateTensorInfo(imagPart),backend2.disposeIntermediateTensorInfo(i),result}else return fill2({backend:backend2,attrs:{shape:x.shape,value:1,dtype:x.dtype}})}var onesLikeConfig={kernelName:OnesLike,backendName:"cpu",kernelFunc:onesLike2};function pack(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims3({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let intermediateTensorInfos=[],expandedTensors=inputs.map(t=>{let expandedT=expandDims3({inputs:{input:t},backend:backend2,attrs:{dim:axis}});return intermediateTensorInfos.push(expandedT),expandedT}),result=concat2({inputs:expandedTensors,backend:backend2,attrs:{axis}});return intermediateTensorInfos.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var packConfig={kernelName:Pack,backendName:"cpu",kernelFunc:pack};function padV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{paddings,constantValue}=attrs;assertNotComplex(x,"pad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),xVals=backend2.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);constantValue!==0&&resVals.fill(constantValue);for(let i=0;i<xSize;i++){let outCoords=util_exports.indexToLoc(i,xRank,xStrides).map((c,i2)=>c+start[i2]),outIndex=util_exports.locToIndex(outCoords,resultRank,resultStrides);resVals[outIndex]=xVals[i]}return{dataId:backend2.write(resVals,outShape,x.dtype),shape:outShape,dtype:x.dtype}}var padV2Config={kernelName:PadV2,backendName:"cpu",kernelFunc:padV2};var powImpl=createSimpleBinaryKernelImpl((a,b)=>Math.pow(a,b)),pow2=binaryKernelFunc(Pow,powImpl),powConfig={kernelName:Pow,backendName:"cpu",kernelFunc:pow2};function range3(args){let{backend:backend2,attrs}=args,{start,stop,dtype,step:step4}=attrs,values=rangeImpl(start,stop,step4,dtype);return backend2.makeTensorInfo([values.length],dtype,values)}var rangeConfig={kernelName:Range,backendName:"cpu",kernelFunc:range3};var reciprocal2=unaryKernelFunc(Reciprocal,xi=>1/xi),reciprocalConfig={kernelName:Reciprocal,backendName:"cpu",kernelFunc:reciprocal2};function resizeBilinear2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeBilinear");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,result=new Float32Array(util_exports.sizeFromShape([batch,newHeight,newWidth,numChannels])),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],outputIdx=0,effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];for(let b=0;b<batch;b++)for(let r=0;r<newHeight;r++){let sourceFracRow;halfPixelCenters?sourceFracRow=effectiveRowSizeRatio*(r+.5)-.5:sourceFracRow=effectiveRowSizeRatio*r;let sourceRowFloor=Math.max(0,Math.floor(sourceFracRow)),rowFrac=sourceFracRow-sourceRowFloor,sourceRowCeil=Math.min(oldHeight-1,Math.ceil(sourceFracRow)),topRowOffset=b*imagesStrides[0]+sourceRowFloor*imagesStrides[1],botRowOffset=b*imagesStrides[0]+sourceRowCeil*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol;halfPixelCenters?sourceFracCol=effectiveColSizeRatio*(c+.5)-.5:sourceFracCol=effectiveColSizeRatio*c;let sourceColFloor=Math.max(0,Math.floor(sourceFracCol)),colFrac=sourceFracCol-sourceColFloor,sourceColCeil=Math.min(oldWidth-1,Math.ceil(sourceFracCol)),topLeftOffest=topRowOffset+sourceColFloor*imagesStrides[2],botLeftOffset=botRowOffset+sourceColFloor*imagesStrides[2],topRightOffset=topRowOffset+sourceColCeil*imagesStrides[2],botRightOffest=botRowOffset+sourceColCeil*imagesStrides[2];for(let d=0;d<numChannels;d++){let topLeft=xValues[topLeftOffest+d],bottomLeft=xValues[botLeftOffset+d],topRight=xValues[topRightOffset+d],bottomRight=xValues[botRightOffest+d],top=topLeft+(topRight-topLeft)*colFrac,bottom=bottomLeft+(bottomRight-bottomLeft)*colFrac,newValue=top+(bottom-top)*rowFrac;result[outputIdx++]=newValue}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],"float32",result)}var resizeBilinearConfig={kernelName:ResizeBilinear,backendName:"cpu",kernelFunc:resizeBilinear2};function resizeBilinearGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeBilinearGrad");let imagesStrides=util_exports.computeStrides(images.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],dyValues=backend2.data.get(dy.dataId).values,offset=0;for(let b=0;b<batch;b++){let bOffset=b*imagesStrides[0];for(let r=0;r<yHeight;r++){let dxR=r*heightScale,topDxRIndex=Math.floor(dxR),bottomDxRIndex=Math.min(Math.ceil(dxR),xHeight-1),topDxROffset=bOffset+topDxRIndex*imagesStrides[1],bottomDxROffset=bOffset+bottomDxRIndex*imagesStrides[1],dxRLerp=dxR-topDxRIndex,inverseDxRLerp=1-dxRLerp;for(let c=0;c<yWidth;c++){let dxC=c*widthScale,leftDxCIndex=Math.floor(dxC),rightDxCIndex=Math.min(Math.ceil(dxC),xWidth-1),dxCLerp=dxC-leftDxCIndex,inverseDxCLerp=1-dxCLerp,topLeftRCOffset=topDxROffset+leftDxCIndex*imagesStrides[2],topRightRCOffset=topDxROffset+rightDxCIndex*imagesStrides[2],bottomLeftRCOffset=bottomDxROffset+leftDxCIndex*imagesStrides[2],bottomRightRCOffset=bottomDxROffset+rightDxCIndex*imagesStrides[2],inverseDxRLerpTimesInverseDxCLerp=inverseDxRLerp*inverseDxCLerp,inverseDxRLerpTimesDxCLerp=inverseDxRLerp*dxCLerp,dxRLerpTimesInverseDxCLerp=dxRLerp*inverseDxCLerp,dxRLerpTimesDxCLerp=dxRLerp*dxCLerp;for(let d=0;d<depth;d++){let dyVal=dyValues[offset++];output[topLeftRCOffset+d]+=dyVal*inverseDxRLerpTimesInverseDxCLerp,output[topRightRCOffset+d]+=dyVal*inverseDxRLerpTimesDxCLerp,output[bottomLeftRCOffset+d]+=dyVal*dxRLerpTimesInverseDxCLerp,output[bottomRightRCOffset+d]+=dyVal*dxRLerpTimesDxCLerp}}}}return backend2.makeTensorInfo([batch,xWidth,xHeight,depth],"float32",output)}var resizeBilinearGradConfig2={kernelName:ResizeBilinearGrad,backendName:"cpu",kernelFunc:resizeBilinearGrad};function resizeNearestNeighbor2(args){let{inputs,backend:backend2,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs;assertNotComplex(images,"resizeNearestNeighbor");let imagesStrides=util_exports.computeStrides(images.shape),[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,xValues=backend2.data.get(images.dataId).values,output=new Float32Array(batch*newHeight*newWidth*numChannels),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1],outputOffset=0;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<newHeight;r++){let sourceFracRow=halfPixelCenters?effectiveRowSizeRatio*(r+.5):effectiveRowSizeRatio*r,sourceNearestRow=Math.min(oldHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));halfPixelCenters&&(sourceNearestRow=Math.max(0,sourceNearestRow));let rowOffset=batchOffset+sourceNearestRow*imagesStrides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=halfPixelCenters?effectiveColSizeRatio*(c+.5):effectiveColSizeRatio*c,sourceNearestCol=Math.min(oldWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));halfPixelCenters&&(sourceNearestCol=Math.max(0,sourceNearestCol));let colOffset=rowOffset+sourceNearestCol*imagesStrides[2];for(let d=0;d<numChannels;d++){let newVal=xValues[colOffset+d];output[outputOffset++]=newVal}}}}return backend2.makeTensorInfo([batch,newHeight,newWidth,numChannels],images.dtype,output)}var resizeNearestNeighborConfig={kernelName:ResizeNearestNeighbor,backendName:"cpu",kernelFunc:resizeNearestNeighbor2};function resizeNearestNeighborGrad(args){let{inputs,backend:backend2,attrs}=args,{images,dy}=inputs,{alignCorners}=attrs;assertNotComplex([dy,images],"resizeNearestNeighborGrad");let imagesStrides=util_exports.computeStrides(images.shape),dyStrides=util_exports.computeStrides(dy.shape),[batch,xHeight,xWidth,depth]=images.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),dyValues=backend2.data.get(dy.dataId).values,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;for(let b=0;b<batch;b++){let batchOffset=b*imagesStrides[0];for(let r=0;r<xHeight;r++){let rowOffset=batchOffset+r*imagesStrides[1],startRLerp=Math.floor(r*invHeightScale),startDyR=Math.floor(startRLerp-winHeight/2);for(let c=0;c<xWidth;c++){let colOffset=rowOffset+c*imagesStrides[2],startCLerp=Math.floor(c*invWidthScale),startDyC=Math.floor(startCLerp-winWidth/2);for(let d=0;d<depth;d++){let accum=0;for(let dyRIndex=0;dyRIndex<winHeight;dyRIndex++){let dyR=dyRIndex+startDyR;if(dyR<0||dyR>=yHeight)continue;let dyROffset=batchOffset+dyR*dyStrides[1],sourceFracRow=dyR*heightScale,sourceNearestRow=Math.min(xHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));if(r===sourceNearestRow)for(let dyCIndex=0;dyCIndex<winWidth;dyCIndex++){let dyC=dyCIndex+startDyC;if(dyC<0||dyC>=yWidth)continue;let dyCOffset=dyROffset+dyC*dyStrides[2],sourceFracCol=dyC*widthScale,sourceNearestCol=Math.min(xWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));c===sourceNearestCol&&(accum+=dyValues[dyCOffset+d])}}output[colOffset+d]=accum}}}}return backend2.makeTensorInfo(images.shape,images.dtype,output)}var resizeNearestNeighborGradConfig2={kernelName:ResizeNearestNeighborGrad,backendName:"cpu",kernelFunc:resizeNearestNeighborGrad};function reverse2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs;assertNotComplex(x,"reverse");let xRank=x.shape.length,$dims=util_exports.parseAxisParam(dims,x.shape);if(xRank===0)return identity2({inputs:{x},backend:backend2});let outBuf=new TensorBuffer(x.shape,x.dtype),xBuf=backend2.bufferSync(x);for(let i=0;i<outBuf.size;i++){let outLoc=outBuf.indexToLoc(i),inLoc=outLoc.slice();$dims.forEach(d=>inLoc[d]=x.shape[d]-1-inLoc[d]),outBuf.set(xBuf.get(...inLoc),...outLoc)}return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var reverseConfig={kernelName:Reverse,backendName:"cpu",kernelFunc:reverse2};var rotateWithOffsetConfig={kernelName:RotateWithOffset,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend2})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,cpuBackend=backend2,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fullOpacityValue=255,sinFactor=Math.sin(radians),cosFactor=Math.cos(radians),imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],y=coords2[1],coordX=(x-centerX)*cosFactor-(y-centerY)*sinFactor,coordY=(x-centerX)*sinFactor+(y-centerY)*cosFactor;coordX=Math.round(coordX+centerX),coordY=Math.round(coordY+centerY);let outputValue=fillValue;if(typeof fillValue!="number"&&(channel===3?outputValue=fullOpacityValue:outputValue=fillValue[channel]),coordX>=0&&coordX<imageWidth&&coordY>=0&&coordY<imageHeight){let rotatedRowOffset=coordY*(imageWidth*numChannels),rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rotatedRowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}let outIdx=batchOffset+rowOffset+colOffset+channel;output[outIdx]=outputValue}}}}return{dataId:cpuBackend.write(output,image3.shape,image3.dtype),shape:image3.shape,dtype:image3.dtype}}};var round3=unaryKernelFunc(Round,xi=>{let base2=Math.floor(xi);return xi-base2<.5?Math.floor(xi):xi-base2>.5?Math.ceil(xi):base2%2==0?base2:base2+1}),roundConfig={kernelName:Round,backendName:"cpu",kernelFunc:round3};function scatterImpl(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices){let flattenShape=[outputSize/sliceSize,sliceSize],indicesData=indices.values,updatesData=updates.values;if(outputSize===0)return buffer(shape,updates.dtype);let outBuf=buffer(flattenShape,updates.dtype);outBuf.values.fill(defaultValue);for(let i=0;i<numUpdates;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];index.push(dim),flattenIndex+=dim*strides[j]}if(flattenIndex<0||flattenIndex>=outputSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${shape}`);for(let k=0;k<sliceSize;k++)sumDupeIndices?outBuf.values[flattenIndex*sliceSize+k]+=updatesData[i*sliceSize+k]:outBuf.values[flattenIndex*sliceSize+k]=updates.rank===0?updatesData[0]:updatesData[i*sliceSize+k]}return outBuf}function scatterNd(args){let{inputs,backend:backend2,attrs}=args,{indices,updates}=inputs,{shape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),sumDupeIndices=!0,indicesBuf=backend2.bufferSync(indices),updatesBuf=backend2.bufferSync(updates),outBuf=scatterImpl(indicesBuf,updatesBuf,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,0,sumDupeIndices);return backend2.makeTensorInfo(shape,outBuf.dtype,outBuf.values)}var scatterNdConfig={kernelName:ScatterNd,backendName:"cpu",kernelFunc:scatterNd};function select(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs;assertNotComplex([condition,t,e],"select");let conditionRank=condition.shape.length,values=backend2.data.get(condition.dataId).values,tValues=backend2.data.get(t.dataId).values,eValues=backend2.data.get(e.dataId).values,resultDtype=upcastType(t.dtype,e.dtype),newValues=util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape),resultDtype),index=0,offset=conditionRank===0||conditionRank>1||t.shape.length===1?1:util_exports.sizeFromShape(t.shape.slice(1));for(let i=0;i<values.length;i++)for(let j=0;j<offset;j++)values[i]===1?newValues[index++]=tValues[i]:newValues[index++]=eValues[i];return backend2.makeTensorInfo(t.shape,resultDtype,newValues)}var selectConfig={kernelName:Select,backendName:"cpu",kernelFunc:select};var scaleAlpha=backend_util_exports.SELU_SCALEALPHA,scale=backend_util_exports.SELU_SCALE,selu2=unaryKernelFunc(Selu,xi=>xi>=0?scale*xi:scaleAlpha*(Math.exp(xi)-1)),seluConfig={kernelName:Selu,backendName:"cpu",kernelFunc:selu2};var sigmoid2=unaryKernelFunc(Sigmoid,xi=>1/(1+Math.exp(-xi))),sigmoidConfig={kernelName:Sigmoid,backendName:"cpu",kernelFunc:sigmoid2};var sign2=unaryKernelFunc(Sign,xi=>xi<0?-1:xi>0?1:0),signConfig={kernelName:Sign,backendName:"cpu",kernelFunc:sign2};var sin2=unaryKernelFunc(Sin,xi=>Math.sin(xi)),sinConfig={kernelName:Sin,backendName:"cpu",kernelFunc:sin2};var sinh2=unaryKernelFunc(Sinh,xi=>Math.sinh(xi)),sinhConfig={kernelName:Sinh,backendName:"cpu",kernelFunc:sinh2};var epsilon2=11920928955078125e-23,threshold=Math.log(epsilon2)+2,softplus2=unaryKernelFunc(Softplus,xi=>{let tooLarge=xi>-threshold,tooSmall=xi<threshold,expX=Math.exp(xi),result;return tooSmall?result=expX:tooLarge?result=xi:result=Math.log(1+expX),result}),softplusConfig={kernelName:Softplus,backendName:"cpu",kernelFunc:softplus2};function spaceToBatchND2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;assertNotComplex([x],"spaceToBatchND");let prod5=util_exports.sizeFromShape(blockShape),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=padV2Config.kernelFunc({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),paddedXReshaped=reshape3({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose2({inputs:{x:paddedXReshaped},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape3({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return backend2.disposeIntermediateTensorInfo(paddedX),backend2.disposeIntermediateTensorInfo(paddedXReshaped),backend2.disposeIntermediateTensorInfo(paddedXT),result}var spaceToBatchNDConfig={kernelName:SpaceToBatchND,backendName:"cpu",kernelFunc:spaceToBatchND2};function sparseToDense2(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,indicesBuf=backend2.bufferSync(sparseIndices),updatesBuf=backend2.bufferSync(sparseValues),$defaultValue=backend2.data.get(defaultValue.dataId).values[0],outBuf=scatterImpl(indicesBuf,updatesBuf,outputShape,outputSize,sliceSize,numUpdates,sliceRank,strides,$defaultValue,sumDupeIndices);return backend2.makeTensorInfo(outputShape,outBuf.dtype,outBuf.values)}var sparseToDenseConfig={kernelName:SparseToDense,backendName:"cpu",kernelFunc:sparseToDense2};function splitV(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[$axis]=s;let sliceT=slice2({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[$axis]+=s,sliceT})}var splitVConfig={kernelName:SplitV,backendName:"cpu",kernelFunc:splitV};var sqrt2=unaryKernelFunc(Sqrt,xi=>Math.sqrt(xi)),sqrtConfig={kernelName:Sqrt,backendName:"cpu",kernelFunc:sqrt2};var squareConfig={kernelName:Square,backendName:"cpu",kernelFunc:({inputs,backend:backend2})=>{let{x}=inputs,cpuBackend=backend2;assertNotComplex(x,"square");let values=cpuBackend.data.get(x.dataId).values,newValues=new Float32Array(values.length);for(let i=0;i<values.length;++i){let value=values[i];newValues[i]=value*value}return{dataId:cpuBackend.write(newValues,x.shape,x.dtype),shape:x.shape,dtype:x.dtype}}};var step2=unaryKernelFunc(Step,(xi,attrs)=>{let stepAttrs=attrs;return isNaN(xi)?NaN:xi>0?1:stepAttrs.alpha}),stepConfig={kernelName:Step,backendName:"cpu",kernelFunc:step2};function stridedSlice2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs;assertNotComplex(x,"stridedSlice");let{nonStrided,$begin,$strides,size,newShape,outShape}=slice_util_exports.sliceInfo(x.shape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask),$x=reshape3({inputs:{x},backend:backend2,attrs:{shape:newShape}}),result;if(nonStrided){let sliced=slice2({inputs:{x:$x},backend:backend2,attrs:{begin:$begin,size}});result=reshape3({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(sliced)}else if(outShape.some(axis=>axis===0))result=backend2.makeTensorInfo(outShape,x.dtype,[]);else{let xBuf=backend2.bufferSync($x),outBuf=stridedSliceImpl(outShape,xBuf,$strides,$begin);result=backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let resultReshaped=reshape3({inputs:{x:result},backend:backend2,attrs:{shape:outShape}});return backend2.disposeIntermediateTensorInfo($x),backend2.disposeIntermediateTensorInfo(result),resultReshaped}var stridedSliceConfig={kernelName:StridedSlice,backendName:"cpu",kernelFunc:stridedSlice2};var tan2=unaryKernelFunc(Tan,xi=>Math.tan(xi)),tanConfig={kernelName:Tan,backendName:"cpu",kernelFunc:tan2};var tanh3=unaryKernelFunc(Tanh,xi=>Math.tanh(xi)),tanhConfig={kernelName:Tanh,backendName:"cpu",kernelFunc:tanh3};function tile3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{reps}=attrs;assertNotComplex(x,"tile");let outBuf=tileImpl(backend2.bufferSync(x),reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}var tileConfig={kernelName:Tile,backendName:"cpu",kernelFunc:tile3};function topK(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs;assertNotComplex(x,"topk");let xVals=backend2.data.get(x.dataId).values,[allTopKVals,allTopKIndices]=topKImpl(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig={kernelName:TopK,backendName:"cpu",kernelFunc:topK};function unique3(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex(x,"unique");let values=backend2.data.get(x.dataId).values,{outputValues,outputShape,indices}=uniqueImpl(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig={kernelName:Unique,backendName:"cpu",kernelFunc:unique3};function unpack(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let valueRank=value.shape.length,num=value.shape[axis],outShape=new Array(valueRank-1),outIndex=0;for(let i=0;i<valueRank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let begin=new Array(valueRank).fill(0),size=value.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let tempRes=slice2({inputs:{x:value},backend:backend2,attrs:{begin,size}});res[i]=reshape3({inputs:{x:tempRes},backend:backend2,attrs:{shape:outShape}}),backend2.disposeIntermediateTensorInfo(tempRes)}return res}var unpackConfig={kernelName:Unpack,backendName:"cpu",kernelFunc:unpack};function unsortedSegmentSum2(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs;assertNotComplex(x,"unsortedSegmentSum");let xRank=x.shape.length,segmentIdsRank=segmentIds.shape.length,res=[],intermediates=[],numIters=xRank-segmentIdsRank,$segmentIds=segmentIds;for(let i=0;i<numIters;++i){let expanded=expandDims3({inputs:{input:$segmentIds},backend:backend2,attrs:{dim:i+1}});$segmentIds=expanded,intermediates.push(expanded)}for(let i=0;i<numSegments;++i){let scalarValue=util_exports.createScalarValue(i,"int32"),segmentId=backend2.makeTensorInfo([],"int32",scalarValue),mask=equal2({inputs:{a:segmentId,b:$segmentIds},backend:backend2}),maskCasted=cast3({inputs:{x:mask},backend:backend2,attrs:{dtype:"float32"}}),mul2=multiply2({inputs:{a:maskCasted,b:x},backend:backend2}),sumTensorInfo=sum3({inputs:{x:mul2},backend:backend2,attrs:{axis:0,keepDims:!1}});res.push(sumTensorInfo),intermediates.push(segmentId),intermediates.push(mask),intermediates.push(maskCasted),intermediates.push(mul2),intermediates.push(sumTensorInfo)}let result=pack({inputs:res,backend:backend2,attrs:{axis:0}});return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig={kernelName:UnsortedSegmentSum,backendName:"cpu",kernelFunc:unsortedSegmentSum2};var kernelConfigs=[_fusedMatMulConfig,absConfig,acosConfig,acoshConfig,addConfig,addNConfig,allConfig,anyConfig,argMaxConfig,argMinConfig,asinConfig,asinhConfig,atanConfig,atan2Config,atanhConfig,avgPoolConfig,avgPool3DConfig,avgPool3DGradConfig2,avgPoolGradConfig2,batchMatMulConfig,batchNormConfig,batchToSpaceNDConfig,bincountConfig,castConfig,ceilConfig,clipConfig,complexConfig,complexAbsConfig,concatConfig,conv2DBackpropFilterConfig,conv2DBackpropInputConfig,conv2DConfig,conv3DBackpropFilterV2Config,conv3DBackpropInputV2Config,conv3DConfig,cosConfig,coshConfig,cropAndResizeConfig,cumsumConfig,denseBincountConfig,depthToSpaceConfig,depthwiseConv2dNativeConfig,depthwiseConv2dNativeBackpropFilterConfig,depthwiseConv2dNativeBackpropInputConfig,diagConfig,dilation2dConfig,dilation2dBackpropInputConfig,dilation2dBackpropFilterConfig,realDivConfig,eluConfig,eluGradConfig2,equalConfig,erfConfig,expConfig,expandDimsConfig,expm1Config,fftConfig,fillConfig,flipLeftRightConfig,floorConfig,floorDivConfig,fusedConv2DConfig,fusedDepthwiseConv2DConfig,gatherNdConfig,gatherV2Config,greaterConfig,greaterEqualConfig,identityConfig,ifftConfig,imagConfig,isFiniteConfig,isInfConfig,isNaNConfig,leakyReluConfig,lessConfig,lessEqualConfig,linSpaceConfig,logConfig,log1pConfig,logicalAndConfig,logicalNotConfig,logicalOrConfig,lRNConfig,lRNGradConfig,maximumConfig,maxPoolConfig,maxPool3DConfig,maxPool3DGradConfig2,maxPoolGradConfig2,maxPoolWithArgmaxConfig,maxConfig,meanConfig,minConfig,minimumConfig,mirrorPadConfig,modConfig,multinomialConfig,multiplyConfig,negConfig,nonMaxSuppressionV3Config,nonMaxSuppressionV4Config,nonMaxSuppressionV5Config,notEqualConfig,oneHotConfig,onesLikeConfig,packConfig,padV2Config,powConfig,preluConfig,prodConfig,rangeConfig,realConfig,reciprocalConfig,reluConfig,relu6Config,reshapeConfig,resizeBilinearConfig,resizeBilinearGradConfig2,resizeNearestNeighborConfig,resizeNearestNeighborGradConfig2,reverseConfig,rotateWithOffsetConfig,roundConfig,rsqrtConfig,scatterNdConfig,selectConfig,seluConfig,sigmoidConfig,signConfig,sinConfig,sinhConfig,sliceConfig,softmaxConfig,softplusConfig,spaceToBatchNDConfig,sparseToDenseConfig,splitVConfig,sqrtConfig,squareConfig,squaredDifferenceConfig,stepConfig,stridedSliceConfig,subConfig,sumConfig,tanConfig,tanhConfig,tileConfig,topKConfig,transposeConfig,uniqueConfig,unpackConfig,unsortedSegmentSumConfig,zerosLikeConfig];for(let kernelConfig of kernelConfigs)registerKernel(kernelConfig);var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(webGLVersion,gl){contexts[webGLVersion]=gl}function getWebGLContext(webGLVersion){if(!(webGLVersion in contexts)){let newCtx=getWebGLRenderingContext(webGLVersion);if(newCtx!==null)contexts[webGLVersion]=newCtx;else return console.log("Could not get context for WebGL version",webGLVersion),null}let gl=contexts[webGLVersion];return gl.isContextLost()?(delete contexts[webGLVersion],getWebGLContext(webGLVersion)):(gl.disable(gl.DEPTH_TEST),gl.disable(gl.STENCIL_TEST),gl.disable(gl.BLEND),gl.disable(gl.DITHER),gl.disable(gl.POLYGON_OFFSET_FILL),gl.disable(gl.SAMPLE_COVERAGE),gl.enable(gl.SCISSOR_TEST),gl.enable(gl.CULL_FACE),gl.cullFace(gl.BACK),contexts[webGLVersion])}function createCanvas(webGLVersion){if(typeof OffscreenCanvas!="undefined"&&webGLVersion===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function getWebGLRenderingContext(webGLVersion){if(webGLVersion!==1&&webGLVersion!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let canvas=createCanvas(webGLVersion);return canvas.addEventListener("webglcontextlost",ev=>{ev.preventDefault(),delete contexts[webGLVersion]},!1),webGLVersion===1?canvas.getContext("webgl",WEBGL_ATTRIBUTES)||canvas.getContext("experimental-webgl",WEBGL_ATTRIBUTES):canvas.getContext("webgl2",WEBGL_ATTRIBUTES)}var PackingScheme;(function(PackingScheme2){PackingScheme2[PackingScheme2.DENSE=0]="DENSE",PackingScheme2[PackingScheme2.SHARED_BATCH=1]="SHARED_BATCH"})(PackingScheme||(PackingScheme={}));var TextureUsage;(function(TextureUsage2){TextureUsage2[TextureUsage2.RENDER=0]="RENDER",TextureUsage2[TextureUsage2.UPLOAD=1]="UPLOAD",TextureUsage2[TextureUsage2.PIXELS=2]="PIXELS",TextureUsage2[TextureUsage2.DOWNLOAD=3]="DOWNLOAD"})(TextureUsage||(TextureUsage={}));var PhysicalTextureType;(function(PhysicalTextureType2){PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(PhysicalTextureType||(PhysicalTextureType={}));function getUnpackedMatrixTextureShapeWidthHeight(rows,columns){return[columns,rows]}function getUnpackedArraySizeFromMatrixSize(matrixSize,channelsPerTexture){return matrixSize*channelsPerTexture}function getDenseTexShape(shape){let size=util_exports.sizeFromShape(shape),texelsNeeded=Math.ceil(size/4);return util_exports.sizeToSquarishShape(texelsNeeded)}function getPackedMatrixTextureShapeWidthHeight(rows,columns){return[Math.max(1,Math.ceil(columns/2)),Math.max(1,Math.ceil(rows/2))]}function getPackedRGBAArraySizeFromMatrixShape(rows,columns){let[w,h]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return w*h*4}function getTextureConfig(gl,textureHalfFloatExtension){let glany=gl,internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat;return env().getNumber("WEBGL_VERSION")===2?(internalFormatFloat=glany.R32F,internalFormatHalfFloat=glany.R16F,internalFormatPackedHalfFloat=glany.RGBA16F,internalFormatPackedFloat=glany.RGBA32F,textureFormatFloat=glany.RED,downloadUnpackNumChannels=4,defaultNumChannels=1,textureTypeHalfFloat=glany.HALF_FLOAT,textureTypeFloat=glany.FLOAT):(internalFormatFloat=gl.RGBA,internalFormatHalfFloat=gl.RGBA,internalFormatPackedHalfFloat=gl.RGBA,internalFormatPackedFloat=glany.RGBA,textureFormatFloat=gl.RGBA,downloadUnpackNumChannels=4,defaultNumChannels=4,textureTypeHalfFloat=textureHalfFloatExtension!=null?textureHalfFloatExtension.HALF_FLOAT_OES:null,textureTypeFloat=gl.FLOAT),downloadTextureFormat=gl.RGBA,{internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat}}function callAndCheck(gl,func2){let returnValue=func2();return env().getBool("DEBUG")&&checkWebGLError(gl),returnValue}function checkWebGLError(gl){let error=gl.getError();if(error!==gl.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(gl,error))}var MIN_FLOAT16=596e-10,MAX_FLOAT16=65504;function canBeRepresented(num){return!!(env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||num===0||MIN_FLOAT16<Math.abs(num)&&Math.abs(num)<MAX_FLOAT16)}function getWebGLErrorMessage(gl,status){switch(status){case gl.NO_ERROR:return"NO_ERROR";case gl.INVALID_ENUM:return"INVALID_ENUM";case gl.INVALID_VALUE:return"INVALID_VALUE";case gl.INVALID_OPERATION:return"INVALID_OPERATION";case gl.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case gl.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case gl.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${status}`}}function getExtensionOrThrow(gl,extensionName){return throwIfNull(gl,()=>gl.getExtension(extensionName),'Extension "'+extensionName+'" not supported on this browser.')}function createVertexShader(gl,vertexShaderSource){let vertexShader=throwIfNull(gl,()=>gl.createShader(gl.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(vertexShader,vertexShaderSource)),callAndCheck(gl,()=>gl.compileShader(vertexShader)),gl.getShaderParameter(vertexShader,gl.COMPILE_STATUS)===!1)throw console.log(gl.getShaderInfoLog(vertexShader)),new Error("Failed to compile vertex shader.");return vertexShader}function createFragmentShader(gl,fragmentShaderSource){let fragmentShader=throwIfNull(gl,()=>gl.createShader(gl.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(fragmentShader,fragmentShaderSource)),callAndCheck(gl,()=>gl.compileShader(fragmentShader)),gl.getShaderParameter(fragmentShader,gl.COMPILE_STATUS)===!1)throw logShaderSourceAndInfoLog(fragmentShaderSource,gl.getShaderInfoLog(fragmentShader)),new Error("Failed to compile fragment shader.");return fragmentShader}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(shaderSource,shaderInfoLog){let lineNumberRegexResult=lineNumberRegex.exec(shaderInfoLog);if(lineNumberRegexResult==null){console.log(`Couldn't parse line number in error: ${shaderInfoLog}`),console.log(shaderSource);return}let lineNumber=+lineNumberRegexResult[1],shaderLines=shaderSource.split(`
 `),pad3=shaderLines.length.toString().length+2,linesWithLineNumbers=shaderLines.map((line,lineNumber2)=>util_exports.rightPad((lineNumber2+1).toString(),pad3)+line),maxLineLength=0;for(let i=0;i<linesWithLineNumbers.length;i++)maxLineLength=Math.max(linesWithLineNumbers[i].length,maxLineLength);let beforeErrorLines=linesWithLineNumbers.slice(0,lineNumber-1),errorLine=linesWithLineNumbers.slice(lineNumber-1,lineNumber),afterErrorLines=linesWithLineNumbers.slice(lineNumber);console.log(beforeErrorLines.join(`
 `)),console.log(shaderInfoLog.split(`
 `)[0]),console.log(`%c ${util_exports.rightPad(errorLine[0],maxLineLength)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(afterErrorLines.join(`
-`))}function createProgram(gl){return throwIfNull(gl,()=>gl.createProgram(),"Unable to create WebGLProgram.")}function linkProgram(gl,program){if(callAndCheck(gl,()=>gl.linkProgram(program)),gl.getProgramParameter(program,gl.LINK_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(gl,program){if(callAndCheck(gl,()=>gl.validateProgram(program)),gl.getProgramParameter(program,gl.VALIDATE_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createStaticIndexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createTexture(gl){return throwIfNull(gl,()=>gl.createTexture(),"Unable to create WebGLTexture.")}function validateTextureSize(width,height){let maxTextureSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(width<=0||height<=0){let requested=`[${width}x${height}]`;throw new Error("Requested texture size "+requested+" is invalid.")}if(width>maxTextureSize||height>maxTextureSize){let requested=`[${width}x${height}]`,max6=`[${maxTextureSize}x${maxTextureSize}]`;throw new Error("Requested texture size "+requested+" greater than WebGL maximum on this browser / GPU "+max6+".")}}function createFramebuffer(gl){return throwIfNull(gl,()=>gl.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(gl,program,attribute,buffer2,arrayEntriesPerItem,itemStrideInBytes,itemOffsetInBytes){let loc=gl.getAttribLocation(program,attribute);return loc===-1?!1:(callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.vertexAttribPointer(loc,arrayEntriesPerItem,gl.FLOAT,!1,itemStrideInBytes,itemOffsetInBytes)),callAndCheck(gl,()=>gl.enableVertexAttribArray(loc)),!0)}function bindTextureUnit(gl,texture,textureUnit){validateTextureUnit(gl,textureUnit),callAndCheck(gl,()=>gl.activeTexture(gl.TEXTURE0+textureUnit)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture))}function getProgramUniformLocationOrThrow(gl,program,uniformName){return throwIfNull(gl,()=>gl.getUniformLocation(program,uniformName),'uniform "'+uniformName+'" not present in program.')}function getProgramUniformLocation(gl,program,uniformName){return gl.getUniformLocation(program,uniformName)}function bindTextureToProgramUniformSampler(gl,texture,uniformSamplerLocation,textureUnit){callAndCheck(gl,()=>bindTextureUnit(gl,texture,textureUnit)),callAndCheck(gl,()=>gl.uniform1i(uniformSamplerLocation,textureUnit))}function bindColorTextureToFramebuffer(gl,texture,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0))}function unbindColorTextureFromFramebuffer(gl,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,null,0))}function validateFramebuffer(gl){let status=gl.checkFramebufferStatus(gl.FRAMEBUFFER);if(status!==gl.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(gl,status))}function getFramebufferErrorMessage(gl,status){switch(status){case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case gl.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${status}`}}function throwIfNull(gl,returnTOrNull,failureMessage){let tOrNull=callAndCheck(gl,()=>returnTOrNull());if(tOrNull==null)throw new Error(failureMessage);return tOrNull}function validateTextureUnit(gl,textureUnit){let maxTextureUnit=gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,glTextureUnit=textureUnit+gl.TEXTURE0;if(glTextureUnit<gl.TEXTURE0||glTextureUnit>maxTextureUnit){let textureUnitRange=`[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;throw new Error(`textureUnit must be in ${textureUnitRange}.`)}}function getBatchDim(shape,dimsToSkip=2){return util_exports.sizeFromShape(shape.slice(0,shape.length-dimsToSkip))}function getRowsCols(shape){if(shape.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[shape.length>1?shape[shape.length-2]:1,shape[shape.length-1]]}function getShapeAs3D(shape){let shapeAs3D=[1,1,1];return shape.length===0||shape.length===1&&shape[0]===1||(shapeAs3D=[getBatchDim(shape),...getRowsCols(shape)]),shapeAs3D}function getTextureShapeFromLogicalShape(logShape,isPacked=!1){let maxTexSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");isPacked&&(maxTexSize=maxTexSize*2,logShape=logShape.map((d,i)=>i>=logShape.length-2?util_exports.nearestLargerEven(logShape[i]):logShape[i]),logShape.length===1&&(logShape=[2,logShape[0]])),logShape.length!==2&&(logShape=util_exports.squeezeShape(logShape).newShape);let size=util_exports.sizeFromShape(logShape);if(logShape.length<=1&&size<=maxTexSize)return[1,size];if(logShape.length===2&&logShape[0]<=maxTexSize&&logShape[1]<=maxTexSize)return logShape;if(logShape.length===3&&logShape[0]*logShape[1]<=maxTexSize&&logShape[2]<=maxTexSize)return[logShape[0]*logShape[1],logShape[2]];if(logShape.length===3&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]];if(logShape.length===4&&logShape[0]*logShape[1]*logShape[2]<=maxTexSize&&logShape[3]<=maxTexSize)return[logShape[0]*logShape[1]*logShape[2],logShape[3]];if(logShape.length===4&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]*logShape[3]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]*logShape[3]];if(isPacked){let batchDim=getBatchDim(logShape),rows=2,cols=2;return logShape.length&&([rows,cols]=getRowsCols(logShape)),size=batchDim*(rows/2)*(cols/2),util_exports.sizeToSquarishShape(size).map(d=>d*2)}return util_exports.sizeToSquarishShape(size)}function isEven(n){return n%2===0}function isReshapeFree(shape1,shape2){if(shape1=shape1.slice(-2),shape2=shape2.slice(-2),util_exports.arraysEqual(shape1,shape2))return!0;if(!shape1.length||!shape2.length)return!0;if(shape1[0]===0||shape1[1]===0||shape2[0]===0||shape2[1]===0)return!0;if(shape1.length!==shape2.length){let shape1Cols=shape1.slice(-1)[0],shape2Cols=shape2.slice(-1)[0];if(shape1Cols===shape2Cols)return!0;if(isEven(shape1Cols)&&isEven(shape2Cols)&&(shape1[0]===1||shape2[0]===1))return!0}return shape1[1]===shape2[1]&&isEven(shape1[0])&&isEven(shape2[0])}var MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER;function getWebGLMaxTextureSize(webGLVersion){if(MAX_TEXTURE_SIZE==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURE_SIZE=gl.getParameter(gl.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(webGLVersion){if(MAX_TEXTURES_IN_SHADER==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURES_IN_SHADER=gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(webGLVersion){if(webGLVersion===0)return 0;let queryTimerVersion,gl=getWebGLContext(webGLVersion);return hasExtension(gl,"EXT_disjoint_timer_query_webgl2")&&webGLVersion===2?queryTimerVersion=2:hasExtension(gl,"EXT_disjoint_timer_query")?queryTimerVersion=1:queryTimerVersion=0,queryTimerVersion}function hasExtension(gl,extensionName){return gl.getExtension(extensionName)!=null}function isWebGLVersionEnabled(webGLVersion){try{if(getWebGLContext(webGLVersion)!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function isCapableOfRenderingToFloatTexture(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1}else if(!hasExtension(gl,"EXT_color_buffer_float"))return!1;return createFloatTextureAndBindToFramebuffer(gl)}function isDownloadFloatTextureEnabled(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1;if(!hasExtension(gl,"WEBGL_color_buffer_float"))return!1}else{if(hasExtension(gl,"EXT_color_buffer_float"))return createFloatTextureAndBindToFramebuffer(gl);let COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(hasExtension(gl,COLOR_BUFFER_HALF_FLOAT)){let textureHalfFloatExtension=gl.getExtension(COLOR_BUFFER_HALF_FLOAT);return createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension)}return!1}return createFloatTextureAndBindToFramebuffer(gl)}function createFloatTextureAndBindToFramebuffer(gl){let texConfig=getTextureConfig(gl),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension){let texConfig=getTextureConfig(gl,textureHalfFloatExtension),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatHalfFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeHalfFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function isWebGLFenceEnabled(webGLVersion){return webGLVersion!==2?!1:getWebGLContext(webGLVersion).fenceSync!=null}function assertNotComplex2(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the WebGL backend.`)})}var ENV3=env();ENV3.registerFlag("HAS_WEBGL",()=>ENV3.getNumber("WEBGL_VERSION")>0);ENV3.registerFlag("WEBGL_VERSION",()=>isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0);ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ENV3.get("WEBGL_VERSION")===2);ENV3.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ENV3.registerFlag("WEBGL_PACK",()=>ENV3.getBool("HAS_WEBGL"));ENV3.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_CLIP",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_REDUCE",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_LAZILY_UNPACK",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_CONV_IM2COL",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let webGLVersion=ENV3.getNumber("WEBGL_VERSION");return webGLVersion===0?0:getWebGLDisjointQueryTimerVersion(webGLVersion)});ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!device_util_exports.isMobile());ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ENV3.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_FENCE_API_ENABLED",()=>isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,threshold2=>{if(threshold2<0&&threshold2!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`)});function getGlslDifferences(){let version10,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound;return env().getNumber("WEBGL_VERSION")===2?(version10="#version 300 es",attribute="in",varyingVs="out",varyingFs="in",texture2D="texture",output="outputColor",defineOutput="out vec4 outputColor;",defineSpecialNaN=`
+`))}function createProgram(gl){return throwIfNull(gl,()=>gl.createProgram(),"Unable to create WebGLProgram.")}function linkProgram(gl,program){if(callAndCheck(gl,()=>gl.linkProgram(program)),gl.getProgramParameter(program,gl.LINK_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(gl,program){if(callAndCheck(gl,()=>gl.validateProgram(program)),gl.getProgramParameter(program,gl.VALIDATE_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createStaticIndexBuffer(gl,data){let buffer2=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer2}function createTexture(gl){return throwIfNull(gl,()=>gl.createTexture(),"Unable to create WebGLTexture.")}function validateTextureSize(width,height){let maxTextureSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(width<=0||height<=0){let requested=`[${width}x${height}]`;throw new Error("Requested texture size "+requested+" is invalid.")}if(width>maxTextureSize||height>maxTextureSize){let requested=`[${width}x${height}]`,max6=`[${maxTextureSize}x${maxTextureSize}]`;throw new Error("Requested texture size "+requested+" greater than WebGL maximum on this browser / GPU "+max6+".")}}function createFramebuffer(gl){return throwIfNull(gl,()=>gl.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(gl,program,attribute,buffer2,arrayEntriesPerItem,itemStrideInBytes,itemOffsetInBytes){let loc=gl.getAttribLocation(program,attribute);return loc===-1?!1:(callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer2)),callAndCheck(gl,()=>gl.vertexAttribPointer(loc,arrayEntriesPerItem,gl.FLOAT,!1,itemStrideInBytes,itemOffsetInBytes)),callAndCheck(gl,()=>gl.enableVertexAttribArray(loc)),!0)}function bindTextureUnit(gl,texture,textureUnit){validateTextureUnit(gl,textureUnit),callAndCheck(gl,()=>gl.activeTexture(gl.TEXTURE0+textureUnit)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture))}function getProgramUniformLocationOrThrow(gl,program,uniformName){return throwIfNull(gl,()=>gl.getUniformLocation(program,uniformName),'uniform "'+uniformName+'" not present in program.')}function getProgramUniformLocation(gl,program,uniformName){return gl.getUniformLocation(program,uniformName)}function bindTextureToProgramUniformSampler(gl,texture,uniformSamplerLocation,textureUnit){callAndCheck(gl,()=>bindTextureUnit(gl,texture,textureUnit)),callAndCheck(gl,()=>gl.uniform1i(uniformSamplerLocation,textureUnit))}function bindColorTextureToFramebuffer(gl,texture,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0))}function unbindColorTextureFromFramebuffer(gl,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,null,0))}function validateFramebuffer(gl){let status=gl.checkFramebufferStatus(gl.FRAMEBUFFER);if(status!==gl.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(gl,status))}function getFramebufferErrorMessage(gl,status){switch(status){case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case gl.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${status}`}}function throwIfNull(gl,returnTOrNull,failureMessage){let tOrNull=callAndCheck(gl,()=>returnTOrNull());if(tOrNull==null)throw new Error(failureMessage);return tOrNull}function validateTextureUnit(gl,textureUnit){let maxTextureUnit=gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,glTextureUnit=textureUnit+gl.TEXTURE0;if(glTextureUnit<gl.TEXTURE0||glTextureUnit>maxTextureUnit){let textureUnitRange=`[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;throw new Error(`textureUnit must be in ${textureUnitRange}.`)}}function getBatchDim(shape,dimsToSkip=2){return util_exports.sizeFromShape(shape.slice(0,shape.length-dimsToSkip))}function getRowsCols(shape){if(shape.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[shape.length>1?shape[shape.length-2]:1,shape[shape.length-1]]}function getShapeAs3D(shape){let shapeAs3D=[1,1,1];return shape.length===0||shape.length===1&&shape[0]===1||(shapeAs3D=[getBatchDim(shape),...getRowsCols(shape)]),shapeAs3D}function getTextureShapeFromLogicalShape(logShape,isPacked=!1){let maxTexSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");isPacked&&(maxTexSize=maxTexSize*2,logShape=logShape.map((d,i)=>i>=logShape.length-2?util_exports.nearestLargerEven(logShape[i]):logShape[i]),logShape.length===1&&(logShape=[2,logShape[0]])),logShape.length!==2&&(logShape=util_exports.squeezeShape(logShape).newShape);let size=util_exports.sizeFromShape(logShape);if(logShape.length<=1&&size<=maxTexSize)return[1,size];if(logShape.length===2&&logShape[0]<=maxTexSize&&logShape[1]<=maxTexSize)return logShape;if(logShape.length===3&&logShape[0]*logShape[1]<=maxTexSize&&logShape[2]<=maxTexSize)return[logShape[0]*logShape[1],logShape[2]];if(logShape.length===3&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]];if(logShape.length===4&&logShape[0]*logShape[1]*logShape[2]<=maxTexSize&&logShape[3]<=maxTexSize)return[logShape[0]*logShape[1]*logShape[2],logShape[3]];if(logShape.length===4&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]*logShape[3]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]*logShape[3]];if(isPacked){let batchDim=getBatchDim(logShape),rows=2,cols=2;return logShape.length&&([rows,cols]=getRowsCols(logShape)),size=batchDim*(rows/2)*(cols/2),util_exports.sizeToSquarishShape(size).map(d=>d*2)}return util_exports.sizeToSquarishShape(size)}function isEven(n){return n%2==0}function isReshapeFree(shape1,shape2){if(shape1=shape1.slice(-2),shape2=shape2.slice(-2),util_exports.arraysEqual(shape1,shape2)||!shape1.length||!shape2.length||shape1[0]===0||shape1[1]===0||shape2[0]===0||shape2[1]===0)return!0;if(shape1.length!==shape2.length){let shape1Cols=shape1.slice(-1)[0],shape2Cols=shape2.slice(-1)[0];if(shape1Cols===shape2Cols||isEven(shape1Cols)&&isEven(shape2Cols)&&(shape1[0]===1||shape2[0]===1))return!0}return shape1[1]===shape2[1]&&isEven(shape1[0])&&isEven(shape2[0])}var MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER;function getWebGLMaxTextureSize(webGLVersion){if(MAX_TEXTURE_SIZE==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURE_SIZE=gl.getParameter(gl.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(webGLVersion){if(MAX_TEXTURES_IN_SHADER==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURES_IN_SHADER=gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(webGLVersion){if(webGLVersion===0)return 0;let queryTimerVersion,gl=getWebGLContext(webGLVersion);return hasExtension(gl,"EXT_disjoint_timer_query_webgl2")&&webGLVersion===2?queryTimerVersion=2:hasExtension(gl,"EXT_disjoint_timer_query")?queryTimerVersion=1:queryTimerVersion=0,queryTimerVersion}function hasExtension(gl,extensionName){return gl.getExtension(extensionName)!=null}function isWebGLVersionEnabled(webGLVersion){try{if(getWebGLContext(webGLVersion)!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function isCapableOfRenderingToFloatTexture(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1}else if(!hasExtension(gl,"EXT_color_buffer_float"))return!1;return createFloatTextureAndBindToFramebuffer(gl)}function isDownloadFloatTextureEnabled(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float")||!hasExtension(gl,"WEBGL_color_buffer_float"))return!1}else{if(hasExtension(gl,"EXT_color_buffer_float"))return createFloatTextureAndBindToFramebuffer(gl);let COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(hasExtension(gl,COLOR_BUFFER_HALF_FLOAT)){let textureHalfFloatExtension=gl.getExtension(COLOR_BUFFER_HALF_FLOAT);return createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension)}return!1}return createFloatTextureAndBindToFramebuffer(gl)}function createFloatTextureAndBindToFramebuffer(gl){let texConfig=getTextureConfig(gl),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension){let texConfig=getTextureConfig(gl,textureHalfFloatExtension),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatHalfFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeHalfFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function isWebGLFenceEnabled(webGLVersion){return webGLVersion!==2?!1:getWebGLContext(webGLVersion).fenceSync!=null}function assertNotComplex2(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the WebGL backend.`)})}var ENV3=env();ENV3.registerFlag("HAS_WEBGL",()=>ENV3.getNumber("WEBGL_VERSION")>0);ENV3.registerFlag("WEBGL_VERSION",()=>isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0);ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ENV3.get("WEBGL_VERSION")===2);ENV3.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ENV3.registerFlag("WEBGL_PACK",()=>ENV3.getBool("HAS_WEBGL"));ENV3.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_CLIP",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_REDUCE",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_LAZILY_UNPACK",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_CONV_IM2COL",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let webGLVersion=ENV3.getNumber("WEBGL_VERSION");return webGLVersion===0?0:getWebGLDisjointQueryTimerVersion(webGLVersion)});ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!device_util_exports.isMobile());ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ENV3.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_FENCE_API_ENABLED",()=>isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,threshold2=>{if(threshold2<0&&threshold2!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`)});function getGlslDifferences(){let version10,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound;return env().getNumber("WEBGL_VERSION")===2?(version10="#version 300 es",attribute="in",varyingVs="out",varyingFs="in",texture2D="texture",output="outputColor",defineOutput="out vec4 outputColor;",defineSpecialNaN=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -269,7 +269,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return createVertexShader(gl,vertexShaderSource)}function createVertexBuffer(gl){let vertexArray=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return createStaticVertexBuffer(gl,vertexArray)}function createIndexBuffer(gl){let triangleVertexIndices=new Uint16Array([0,1,2,2,1,3]);return createStaticIndexBuffer(gl,triangleVertexIndices)}function createAndConfigureTexture(gl,width,height,internalFormat,textureFormat,textureType){validateTextureSize(width,height);let texture=createTexture(gl),tex2d=gl.TEXTURE_2D;return callAndCheck(gl,()=>gl.bindTexture(tex2d,texture)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MIN_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MAG_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texImage2D(tex2d,0,internalFormat,width,height,0,textureFormat,textureType,null)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null)),texture}function getInternalFormatForFloat32MatrixTexture(textureConfig){return textureConfig.internalFormatFloat}function createFloat32MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat32MatrixTexture(textureConfig),textureConfig.textureFormatFloat,gl.FLOAT)}function getInternalFormatForFloat16MatrixTexture(textureConfig){return textureConfig.internalFormatHalfFloat}function createFloat16MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16MatrixTexture(textureConfig),textureConfig.textureFormatFloat,textureConfig.textureTypeHalfFloat)}function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig){return textureConfig.downloadTextureFormat}function createUnsignedBytesMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForUnsignedBytesMatrixTexture(textureConfig),gl.RGBA,gl.UNSIGNED_BYTE)}function getInternalFormatForPackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedFloat}function createPackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForPackedMatrixTexture(textureConfig),gl.RGBA,gl.FLOAT)}function getInternalFormatForFloat16PackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedHalfFloat}function createFloat16PackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16PackedMatrixTexture(textureConfig),gl.RGBA,textureConfig.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(gl,program,vertexBuffer){let posOffset=0,uvOffset=3*4,stride=3*4+2*4;return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,vertexBuffer)),bindVertexBufferToProgramAttribute(gl,program,"clipSpacePos",vertexBuffer,3,stride,posOffset)&&bindVertexBufferToProgramAttribute(gl,program,"uv",vertexBuffer,2,stride,uvOffset)}function uploadDenseMatrixToTexture(gl,texture,width,height,data,textureConfig){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture));let dataForUpload,texelDataType,internalFormat;data instanceof Uint8Array?(dataForUpload=new Uint8Array(width*height*4),texelDataType=gl.UNSIGNED_BYTE,internalFormat=gl.RGBA):(dataForUpload=new Float32Array(width*height*4),texelDataType=gl.FLOAT,internalFormat=textureConfig.internalFormatPackedFloat),dataForUpload.set(data),callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,internalFormat,width,height,0,gl.RGBA,texelDataType,dataForUpload)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function uploadPixelDataToTexture(gl,texture,pixels){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture)),pixels.data instanceof Uint8Array?callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,pixels.width,pixels.height,0,gl.RGBA,gl.UNSIGNED_BYTE,pixels.data)):callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,pixels)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function createBufferFromOutputTexture(gl2,rows,columns,textureConfig){let buffer2=gl2.createBuffer();callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2));let bufferSizeBytes=4*4*rows*columns;return callAndCheck(gl2,()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER,bufferSizeBytes,gl2.STREAM_READ)),callAndCheck(gl2,()=>gl2.readPixels(0,0,columns,rows,gl2.RGBA,gl2.FLOAT,0)),callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null)),buffer2}function downloadFloat32MatrixFromBuffer(gl,buffer2,size){let gl2=gl,downloadTarget=new Float32Array(size);return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadByteEncodedFloatMatrixFromOutputTexture(gl,rows,columns,textureConfig){let[w,h]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns),numChannels=4,downloadTarget=new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows*columns,numChannels));return callAndCheck(gl,()=>gl.readPixels(0,0,w,h,textureConfig.downloadTextureFormat,gl.UNSIGNED_BYTE,downloadTarget)),new Float32Array(downloadTarget.buffer)}function downloadPackedMatrixFromBuffer(gl,buffer2,batch,rows,cols,physicalRows,physicalCols,textureConfig){let gl2=gl,downloadTarget=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows,physicalCols));return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadMatrixFromPackedOutputTexture(gl,physicalRows,physicalCols){let packedRGBA=new Float32Array(physicalRows*physicalCols*4);return callAndCheck(gl,()=>gl.readPixels(0,0,physicalCols,physicalRows,gl.RGBA,gl.FLOAT,packedRGBA)),packedRGBA}var GPGPUContext=class{constructor(gl){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let glVersion=env().getNumber("WEBGL_VERSION");gl!=null?(this.gl=gl,setWebGLContext(glVersion,gl)):this.gl=getWebGLContext(glVersion);let COLOR_BUFFER_FLOAT="WEBGL_color_buffer_float",COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(env().getNumber("WEBGL_VERSION")===1){let TEXTURE_FLOAT="OES_texture_float",TEXTURE_HALF_FLOAT="OES_texture_half_float";if(this.textureFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_FLOAT),hasExtension(this.gl,TEXTURE_HALF_FLOAT))this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT),hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=getExtensionOrThrow(this.gl,COLOR_BUFFER_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(COLOR_BUFFER_FLOAT="EXT_color_buffer_float",hasExtension(this.gl,COLOR_BUFFER_FLOAT))this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT);else if(hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}get debug(){return env().getBool("DEBUG")}dispose(){if(!this.disposed){this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let gl=this.gl;callAndCheck(gl,()=>gl.finish()),callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,null)),callAndCheck(gl,()=>gl.deleteFramebuffer(this.framebuffer)),callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.deleteBuffer(this.indexBuffer)),this.disposed=!0}}createFloat32MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,rows,columns,this.textureConfig)}createFloat16MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,rows,columns,this.textureConfig)}createUnsignedBytesMatrixTexture(rows,columns){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,rows,columns,this.textureConfig)}uploadPixelDataToTexture(texture,pixels){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,texture,pixels)}uploadDenseMatrixToTexture(texture,width,height,data){this.throwIfDisposed(),uploadDenseMatrixToTexture(this.gl,texture,width,height,data,this.textureConfig)}createFloat16PackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}createPackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}deleteMatrixTexture(texture){this.throwIfDisposed(),this.outputTexture===texture&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,()=>this.gl.deleteTexture(texture))}downloadByteEncodedFloatMatrixFromOutputTexture(texture,rows,columns){return this.downloadMatrixDriver(texture,()=>downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,rows,columns,this.textureConfig))}downloadPackedMatrixFromBuffer(buffer2,batch,rows,columns,physicalRows,physicalCols){return downloadPackedMatrixFromBuffer(this.gl,buffer2,batch,rows,columns,physicalRows,physicalCols,this.textureConfig)}downloadFloat32MatrixFromBuffer(buffer2,size){return downloadFloat32MatrixFromBuffer(this.gl,buffer2,size)}createBufferFromTexture(texture,rows,columns){this.bindTextureToFrameBuffer(texture);let result=createBufferFromOutputTexture(this.gl,rows,columns,this.textureConfig);return this.unbindTextureToFrameBuffer(),result}createAndWaitForFence(){let fenceContext=this.createFence(this.gl);return this.pollFence(fenceContext)}createFence(gl){let query,isFencePassed;if(env().getBool("WEBGL_FENCE_API_ENABLED")){let gl2=gl,sync=gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE,0);gl.flush(),isFencePassed=()=>{let status=gl2.clientWaitSync(sync,0,0);return status===gl2.ALREADY_SIGNALED||status===gl2.CONDITION_SATISFIED},query=sync}else env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(query=this.beginQuery(),this.endQuery(),isFencePassed=()=>this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):isFencePassed=()=>!0;return{query,isFencePassed}}downloadMatrixFromPackedTexture(texture,physicalRows,physicalCols){return this.downloadMatrixDriver(texture,()=>downloadMatrixFromPackedOutputTexture(this.gl,physicalRows,physicalCols))}createProgram(fragmentShaderSource){this.throwIfDisposed();let gl=this.gl,fragmentShader=createFragmentShader(gl,fragmentShaderSource),vertexShader=createVertexShader2(gl),program=createProgram(gl);return callAndCheck(gl,()=>gl.attachShader(program,vertexShader)),callAndCheck(gl,()=>gl.attachShader(program,fragmentShader)),linkProgram(gl,program),this.debug&&validateProgram(gl,program),this.vertexAttrsAreBound||(this.setProgram(program),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(gl,this.program,this.vertexBuffer)),program}deleteProgram(program){this.throwIfDisposed(),program===this.program&&(this.program=null),program!=null&&callAndCheck(this.gl,()=>this.gl.deleteProgram(program))}setProgram(program){this.throwIfDisposed(),this.program=program,this.program!=null&&this.debug&&validateProgram(this.gl,this.program),callAndCheck(this.gl,()=>this.gl.useProgram(program))}getUniformLocation(program,uniformName,shouldThrow=!0){return this.throwIfDisposed(),shouldThrow?getProgramUniformLocationOrThrow(this.gl,program,uniformName):getProgramUniformLocation(this.gl,program,uniformName)}getAttributeLocation(program,attribute){return this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.getAttribLocation(program,attribute))}getUniformLocationNoThrow(program,uniformName){return this.throwIfDisposed(),this.gl.getUniformLocation(program,uniformName)}setInputMatrixTexture(inputMatrixTexture,uniformLocation,textureUnit){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,inputMatrixTexture,uniformLocation,textureUnit)}setOutputMatrixTexture(outputMatrixTexture,rows,columns){this.setOutputMatrixTextureDriver(outputMatrixTexture,columns,rows)}setOutputPackedMatrixTexture(outputPackedMatrixTexture,rows,columns){this.throwIfDisposed();let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);this.setOutputMatrixTextureDriver(outputPackedMatrixTexture,width,height)}setOutputMatrixWriteRegion(startRow,numRows,startColumn,numColumns){this.setOutputMatrixWriteRegionDriver(startColumn,startRow,numColumns,numRows)}setOutputPackedMatrixWriteRegion(startRow,numRows,startColumn,numColumns){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let gl=this.gl;this.debug&&this.debugValidate(),callAndCheck(gl,()=>gl.drawElements(gl.TRIANGLES,6,gl.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2(),query2=gl2.createQuery();return gl2.beginQuery(ext2.TIME_ELAPSED_EXT,query2),query2}let ext=this.getQueryTimerExtensionWebGL1(),query=ext.createQueryEXT();return ext.beginQueryEXT(ext.TIME_ELAPSED_EXT,query),query}endQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2();gl2.endQuery(ext2.TIME_ELAPSED_EXT);return}let ext=this.getQueryTimerExtensionWebGL1();ext.endQueryEXT(ext.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(query){return await util_exports.repeatedTry(()=>this.disposed||this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(query,queryTimerVersion){if(queryTimerVersion===0)return null;if(queryTimerVersion===2){let gl2=this.gl;return gl2.getQueryParameter(query,gl2.QUERY_RESULT)/1e6}else{let ext=this.getQueryTimerExtensionWebGL1();return ext.getQueryObjectEXT(query,ext.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(query,queryTimerVersion){if(queryTimerVersion===0)return!0;if(queryTimerVersion===2){let gl2=this.gl,ext=this.getQueryTimerExtensionWebGL2(),available=gl2.getQueryParameter(query,gl2.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}else{let ext=this.getQueryTimerExtensionWebGL1(),available=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}}pollFence(fenceContext){return new Promise(resolve=>{this.addItemToPoll(()=>fenceContext.isFencePassed(),()=>resolve())})}pollItems(){let index=linearSearchLastTrue(this.itemsToPoll.map(x=>x.isDoneFn));for(let i=0;i<=index;++i){let{resolveFn}=this.itemsToPoll[i];resolveFn()}this.itemsToPoll=this.itemsToPoll.slice(index+1)}addItemToPoll(isDoneFn,resolveFn){(this.itemsToPoll.push({isDoneFn,resolveFn}),!(this.itemsToPoll.length>1))&&util_exports.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(texture){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,texture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(texture,downloadAndDecode){this.bindTextureToFrameBuffer(texture);let result=downloadAndDecode();return this.unbindTextureToFrameBuffer(),result}setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked,width,height){this.throwIfDisposed();let gl=this.gl;bindColorTextureToFramebuffer(gl,outputMatrixTextureMaybePacked,this.framebuffer),this.debug&&validateFramebuffer(gl),this.outputTexture=outputMatrixTextureMaybePacked,callAndCheck(gl,()=>gl.viewport(0,0,width,height)),callAndCheck(gl,()=>gl.scissor(0,0,width,height))}setOutputMatrixWriteRegionDriver(x,y,width,height){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.scissor(x,y,width,height))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function linearSearchLastTrue(arr){let i=0;for(;i<arr.length&&arr[i]();++i);return i-1}var{getBroadcastDims:getBroadcastDims2}=backend_util_exports;function makeShader(inputsInfo,outputShape,userCode,usesPackedTextures){let prefixSnippets=[];inputsInfo.forEach(x=>{let size=util_exports.sizeFromShape(x.shapeInfo.logicalShape);x.shapeInfo.isUniform?prefixSnippets.push(`uniform float ${x.name}${size>1?`[${size}]`:""};`):(prefixSnippets.push(`uniform sampler2D ${x.name};`),prefixSnippets.push(`uniform int offset${x.name};`))});let inputPrefixSnippet=prefixSnippets.join(`
+    }`;return createVertexShader(gl,vertexShaderSource)}function createVertexBuffer(gl){let vertexArray=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return createStaticVertexBuffer(gl,vertexArray)}function createIndexBuffer(gl){let triangleVertexIndices=new Uint16Array([0,1,2,2,1,3]);return createStaticIndexBuffer(gl,triangleVertexIndices)}function createAndConfigureTexture(gl,width,height,internalFormat,textureFormat,textureType){validateTextureSize(width,height);let texture=createTexture(gl),tex2d=gl.TEXTURE_2D;return callAndCheck(gl,()=>gl.bindTexture(tex2d,texture)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MIN_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MAG_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texImage2D(tex2d,0,internalFormat,width,height,0,textureFormat,textureType,null)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null)),texture}function getInternalFormatForFloat32MatrixTexture(textureConfig){return textureConfig.internalFormatFloat}function createFloat32MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat32MatrixTexture(textureConfig),textureConfig.textureFormatFloat,gl.FLOAT)}function getInternalFormatForFloat16MatrixTexture(textureConfig){return textureConfig.internalFormatHalfFloat}function createFloat16MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16MatrixTexture(textureConfig),textureConfig.textureFormatFloat,textureConfig.textureTypeHalfFloat)}function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig){return textureConfig.downloadTextureFormat}function createUnsignedBytesMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForUnsignedBytesMatrixTexture(textureConfig),gl.RGBA,gl.UNSIGNED_BYTE)}function getInternalFormatForPackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedFloat}function createPackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForPackedMatrixTexture(textureConfig),gl.RGBA,gl.FLOAT)}function getInternalFormatForFloat16PackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedHalfFloat}function createFloat16PackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16PackedMatrixTexture(textureConfig),gl.RGBA,textureConfig.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(gl,program,vertexBuffer){let posOffset=0,uvOffset=3*4,stride=3*4+2*4;return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,vertexBuffer)),bindVertexBufferToProgramAttribute(gl,program,"clipSpacePos",vertexBuffer,3,stride,posOffset)&&bindVertexBufferToProgramAttribute(gl,program,"uv",vertexBuffer,2,stride,uvOffset)}function uploadDenseMatrixToTexture(gl,texture,width,height,data,textureConfig){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture));let dataForUpload,texelDataType,internalFormat;data instanceof Uint8Array?(dataForUpload=new Uint8Array(width*height*4),texelDataType=gl.UNSIGNED_BYTE,internalFormat=gl.RGBA):(dataForUpload=new Float32Array(width*height*4),texelDataType=gl.FLOAT,internalFormat=textureConfig.internalFormatPackedFloat),dataForUpload.set(data),callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,internalFormat,width,height,0,gl.RGBA,texelDataType,dataForUpload)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function uploadPixelDataToTexture(gl,texture,pixels){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture)),pixels.data instanceof Uint8Array?callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,pixels.width,pixels.height,0,gl.RGBA,gl.UNSIGNED_BYTE,pixels.data)):callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,pixels)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function createBufferFromOutputTexture(gl2,rows,columns,textureConfig){let buffer2=gl2.createBuffer();callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2));let bufferSizeBytes=4*4*rows*columns;return callAndCheck(gl2,()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER,bufferSizeBytes,gl2.STREAM_READ)),callAndCheck(gl2,()=>gl2.readPixels(0,0,columns,rows,gl2.RGBA,gl2.FLOAT,0)),callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null)),buffer2}function downloadFloat32MatrixFromBuffer(gl,buffer2,size){let gl2=gl,downloadTarget=new Float32Array(size);return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadByteEncodedFloatMatrixFromOutputTexture(gl,rows,columns,textureConfig){let[w,h]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns),numChannels=4,downloadTarget=new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows*columns,numChannels));return callAndCheck(gl,()=>gl.readPixels(0,0,w,h,textureConfig.downloadTextureFormat,gl.UNSIGNED_BYTE,downloadTarget)),new Float32Array(downloadTarget.buffer)}function downloadPackedMatrixFromBuffer(gl,buffer2,batch,rows,cols,physicalRows,physicalCols,textureConfig){let gl2=gl,downloadTarget=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows,physicalCols));return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer2),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadMatrixFromPackedOutputTexture(gl,physicalRows,physicalCols){let packedRGBA=new Float32Array(physicalRows*physicalCols*4);return callAndCheck(gl,()=>gl.readPixels(0,0,physicalCols,physicalRows,gl.RGBA,gl.FLOAT,packedRGBA)),packedRGBA}var GPGPUContext=class{constructor(gl){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let glVersion=env().getNumber("WEBGL_VERSION");gl!=null?(this.gl=gl,setWebGLContext(glVersion,gl)):this.gl=getWebGLContext(glVersion);let COLOR_BUFFER_FLOAT="WEBGL_color_buffer_float",COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(env().getNumber("WEBGL_VERSION")===1){let TEXTURE_FLOAT="OES_texture_float",TEXTURE_HALF_FLOAT="OES_texture_half_float";if(this.textureFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_FLOAT),hasExtension(this.gl,TEXTURE_HALF_FLOAT))this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT),hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=getExtensionOrThrow(this.gl,COLOR_BUFFER_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(COLOR_BUFFER_FLOAT="EXT_color_buffer_float",hasExtension(this.gl,COLOR_BUFFER_FLOAT))this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT);else if(hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}get debug(){return env().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let gl=this.gl;callAndCheck(gl,()=>gl.finish()),callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,null)),callAndCheck(gl,()=>gl.deleteFramebuffer(this.framebuffer)),callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,rows,columns,this.textureConfig)}createFloat16MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,rows,columns,this.textureConfig)}createUnsignedBytesMatrixTexture(rows,columns){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,rows,columns,this.textureConfig)}uploadPixelDataToTexture(texture,pixels){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,texture,pixels)}uploadDenseMatrixToTexture(texture,width,height,data){this.throwIfDisposed(),uploadDenseMatrixToTexture(this.gl,texture,width,height,data,this.textureConfig)}createFloat16PackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}createPackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}deleteMatrixTexture(texture){this.throwIfDisposed(),this.outputTexture===texture&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,()=>this.gl.deleteTexture(texture))}downloadByteEncodedFloatMatrixFromOutputTexture(texture,rows,columns){return this.downloadMatrixDriver(texture,()=>downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,rows,columns,this.textureConfig))}downloadPackedMatrixFromBuffer(buffer2,batch,rows,columns,physicalRows,physicalCols){return downloadPackedMatrixFromBuffer(this.gl,buffer2,batch,rows,columns,physicalRows,physicalCols,this.textureConfig)}downloadFloat32MatrixFromBuffer(buffer2,size){return downloadFloat32MatrixFromBuffer(this.gl,buffer2,size)}createBufferFromTexture(texture,rows,columns){this.bindTextureToFrameBuffer(texture);let result=createBufferFromOutputTexture(this.gl,rows,columns,this.textureConfig);return this.unbindTextureToFrameBuffer(),result}createAndWaitForFence(){let fenceContext=this.createFence(this.gl);return this.pollFence(fenceContext)}createFence(gl){let query,isFencePassed;if(env().getBool("WEBGL_FENCE_API_ENABLED")){let gl2=gl,sync=gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE,0);gl.flush(),isFencePassed=()=>{let status=gl2.clientWaitSync(sync,0,0);return status===gl2.ALREADY_SIGNALED||status===gl2.CONDITION_SATISFIED},query=sync}else env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(query=this.beginQuery(),this.endQuery(),isFencePassed=()=>this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):isFencePassed=()=>!0;return{query,isFencePassed}}downloadMatrixFromPackedTexture(texture,physicalRows,physicalCols){return this.downloadMatrixDriver(texture,()=>downloadMatrixFromPackedOutputTexture(this.gl,physicalRows,physicalCols))}createProgram(fragmentShaderSource){this.throwIfDisposed();let gl=this.gl,fragmentShader=createFragmentShader(gl,fragmentShaderSource),vertexShader=createVertexShader2(gl),program=createProgram(gl);return callAndCheck(gl,()=>gl.attachShader(program,vertexShader)),callAndCheck(gl,()=>gl.attachShader(program,fragmentShader)),linkProgram(gl,program),this.debug&&validateProgram(gl,program),this.vertexAttrsAreBound||(this.setProgram(program),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(gl,this.program,this.vertexBuffer)),program}deleteProgram(program){this.throwIfDisposed(),program===this.program&&(this.program=null),program!=null&&callAndCheck(this.gl,()=>this.gl.deleteProgram(program))}setProgram(program){this.throwIfDisposed(),this.program=program,this.program!=null&&this.debug&&validateProgram(this.gl,this.program),callAndCheck(this.gl,()=>this.gl.useProgram(program))}getUniformLocation(program,uniformName,shouldThrow=!0){return this.throwIfDisposed(),shouldThrow?getProgramUniformLocationOrThrow(this.gl,program,uniformName):getProgramUniformLocation(this.gl,program,uniformName)}getAttributeLocation(program,attribute){return this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.getAttribLocation(program,attribute))}getUniformLocationNoThrow(program,uniformName){return this.throwIfDisposed(),this.gl.getUniformLocation(program,uniformName)}setInputMatrixTexture(inputMatrixTexture,uniformLocation,textureUnit){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,inputMatrixTexture,uniformLocation,textureUnit)}setOutputMatrixTexture(outputMatrixTexture,rows,columns){this.setOutputMatrixTextureDriver(outputMatrixTexture,columns,rows)}setOutputPackedMatrixTexture(outputPackedMatrixTexture,rows,columns){this.throwIfDisposed();let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);this.setOutputMatrixTextureDriver(outputPackedMatrixTexture,width,height)}setOutputMatrixWriteRegion(startRow,numRows,startColumn,numColumns){this.setOutputMatrixWriteRegionDriver(startColumn,startRow,numColumns,numRows)}setOutputPackedMatrixWriteRegion(startRow,numRows,startColumn,numColumns){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let gl=this.gl;this.debug&&this.debugValidate(),callAndCheck(gl,()=>gl.drawElements(gl.TRIANGLES,6,gl.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2(),query2=gl2.createQuery();return gl2.beginQuery(ext2.TIME_ELAPSED_EXT,query2),query2}let ext=this.getQueryTimerExtensionWebGL1(),query=ext.createQueryEXT();return ext.beginQueryEXT(ext.TIME_ELAPSED_EXT,query),query}endQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2();gl2.endQuery(ext2.TIME_ELAPSED_EXT);return}let ext=this.getQueryTimerExtensionWebGL1();ext.endQueryEXT(ext.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(query){return await util_exports.repeatedTry(()=>this.disposed||this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(query,queryTimerVersion){if(queryTimerVersion===0)return null;if(queryTimerVersion===2){let gl2=this.gl;return gl2.getQueryParameter(query,gl2.QUERY_RESULT)/1e6}else{let ext=this.getQueryTimerExtensionWebGL1();return ext.getQueryObjectEXT(query,ext.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(query,queryTimerVersion){if(queryTimerVersion===0)return!0;if(queryTimerVersion===2){let gl2=this.gl,ext=this.getQueryTimerExtensionWebGL2(),available=gl2.getQueryParameter(query,gl2.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}else{let ext=this.getQueryTimerExtensionWebGL1(),available=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}}pollFence(fenceContext){return new Promise(resolve=>{this.addItemToPoll(()=>fenceContext.isFencePassed(),()=>resolve())})}pollItems(){let index=linearSearchLastTrue(this.itemsToPoll.map(x=>x.isDoneFn));for(let i=0;i<=index;++i){let{resolveFn}=this.itemsToPoll[i];resolveFn()}this.itemsToPoll=this.itemsToPoll.slice(index+1)}addItemToPoll(isDoneFn,resolveFn){this.itemsToPoll.push({isDoneFn,resolveFn}),!(this.itemsToPoll.length>1)&&util_exports.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(texture){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,texture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(texture,downloadAndDecode){this.bindTextureToFrameBuffer(texture);let result=downloadAndDecode();return this.unbindTextureToFrameBuffer(),result}setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked,width,height){this.throwIfDisposed();let gl=this.gl;bindColorTextureToFramebuffer(gl,outputMatrixTextureMaybePacked,this.framebuffer),this.debug&&validateFramebuffer(gl),this.outputTexture=outputMatrixTextureMaybePacked,callAndCheck(gl,()=>gl.viewport(0,0,width,height)),callAndCheck(gl,()=>gl.scissor(0,0,width,height))}setOutputMatrixWriteRegionDriver(x,y,width,height){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.scissor(x,y,width,height))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function linearSearchLastTrue(arr){let i=0;for(;i<arr.length&&arr[i]();++i);return i-1}var{getBroadcastDims:getBroadcastDims2}=backend_util_exports;function makeShader(inputsInfo,outputShape,userCode,usesPackedTextures){let prefixSnippets=[];inputsInfo.forEach(x=>{let size=util_exports.sizeFromShape(x.shapeInfo.logicalShape);x.shapeInfo.isUniform?prefixSnippets.push(`uniform float ${x.name}${size>1?`[${size}]`:""};`):(prefixSnippets.push(`uniform sampler2D ${x.name};`),prefixSnippets.push(`uniform int offset${x.name};`))});let inputPrefixSnippet=prefixSnippets.join(`
 `),inputSamplingSnippet=inputsInfo.map(x=>getInputSamplingSnippet(x,outputShape,usesPackedTextures)).join(`
 `),outTexShape=outputShape.texShape,glsl=getGlslDifferences(),floatTextureSampleSnippet=getFloatTextureSampleSnippet(glsl),outputSamplingSnippet,floatTextureSetOutputSnippet,shaderPrefix=getShaderPrefix(glsl);return outputShape.isPacked?(outputSamplingSnippet=getPackedOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRGBASnippet(glsl)):(outputSamplingSnippet=getOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRSnippet(glsl)),usesPackedTextures&&(shaderPrefix+=SHADER_PACKED_PREFIX),[shaderPrefix,floatTextureSampleSnippet,floatTextureSetOutputSnippet,inputPrefixSnippet,outputSamplingSnippet,inputSamplingSnippet,userCode].join(`
 `)}function getSamplerFromInInfo(inInfo){let shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getSamplerScalar(inInfo);case 1:return getSampler1D(inInfo);case 2:return getSampler2D(inInfo);case 3:return getSampler3D(inInfo);case 4:return getSampler4D(inInfo);case 5:return getSampler5D(inInfo);case 6:return getSampler6D(inInfo);default:throw new Error(`${shape.length}-D input sampling is not yet supported`)}}function getPackedSamplerFromInInfo(inInfo){switch(inInfo.shapeInfo.logicalShape.length){case 0:return getPackedSamplerScalar(inInfo);case 1:return getPackedSampler1D(inInfo);case 2:return getPackedSampler2D(inInfo);case 3:return getPackedSampler3D(inInfo);default:return getPackedSamplerND(inInfo)}}function getInputSamplingSnippet(inInfo,outShapeInfo,usesPackedTextures=!1){let res="";usesPackedTextures?res+=getPackedSamplerFromInInfo(inInfo):res+=getSamplerFromInInfo(inInfo);let inShape=inInfo.shapeInfo.logicalShape,outShape=outShapeInfo.logicalShape;return inShape.length<=outShape.length&&(usesPackedTextures?res+=getPackedSamplerAtOutputCoords(inInfo,outShapeInfo):res+=getSamplerAtOutputCoords(inInfo,outShapeInfo)),res}function getPackedOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(outShape,outTexShape);case 2:return getOutputPacked2DCoords(outShape,outTexShape);case 3:return getOutputPacked3DCoords(outShape,outTexShape);default:return getOutputPackedNDCoords(outShape,outTexShape)}}function getOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(outShape,outTexShape);case 2:return getOutput2DCoords(outShape,outTexShape);case 3:return getOutput3DCoords(outShape,outTexShape);case 4:return getOutput4DCoords(outShape,outTexShape);case 5:return getOutput5DCoords(outShape,outTexShape);case 6:return getOutput6DCoords(outShape,outTexShape);default:throw new Error(`${outShape.length}-D output sampling is not yet supported`)}}function getFloatTextureSampleSnippet(glsl){return`
@@ -827,7 +827,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${coordsSnippet}
       return get${texFuncSnippet}(${unpackedCoordsSnippet});
     }
-  `}function getCoordsDataType(rank){if(rank<=1)return"int";if(rank===2)return"ivec2";if(rank===3)return"ivec3";if(rank===4)return"ivec4";if(rank===5)return"ivec5";if(rank===6)return"ivec6";throw Error(`GPU for rank ${rank} is not yet supported`)}function squeezeInputInfo(inInfo,squeezedShape){let newInputInfo=JSON.parse(JSON.stringify(inInfo));return newInputInfo.shapeInfo.logicalShape=squeezedShape,newInputInfo}function getSqueezedParams(params,keptDims){return keptDims.map(d=>params[d]).join(", ")}function compileProgram(gpgpu,program,inputs,output){let userCode=program.userCode,inputInfos=inputs.map((input2,i)=>{let shapeInfo={logicalShape:input2.shape,texShape:input2.isUniform?null:input2.texData.texShape,isUniform:input2.isUniform,isPacked:input2.isUniform?!1:input2.texData.isPacked,flatOffset:null};return input2.texData!=null&&input2.texData.slice!=null&&input2.texData.slice.flatOffset>0&&(shapeInfo.flatOffset=input2.texData.slice.flatOffset),{name:program.variableNames[i],shapeInfo}}),inShapeInfos=inputInfos.map(x=>x.shapeInfo),outShapeInfo={logicalShape:output.shape,texShape:output.texData.texShape,isUniform:!1,isPacked:output.texData.isPacked,flatOffset:null},source=makeShader(inputInfos,outShapeInfo,userCode,program.packedInputs),webGLProgram=gpgpu.createProgram(source),infLoc=null,nanLoc=gpgpu.getUniformLocation(webGLProgram,"NAN",!1);env().getNumber("WEBGL_VERSION")===1&&(infLoc=gpgpu.getUniformLocation(webGLProgram,"INFINITY",!1));let uniformLocations={};for(let i=0;i<program.variableNames.length;i++){let varName=program.variableNames[i],shouldThrow=!1;uniformLocations[varName]=gpgpu.getUniformLocation(webGLProgram,varName,shouldThrow),uniformLocations[`offset${varName}`]=gpgpu.getUniformLocation(webGLProgram,`offset${varName}`,shouldThrow)}return{program,source,webGLProgram,uniformLocations,inShapeInfos,outShapeInfo,infLoc,nanLoc}}function validateBinaryAndProgram(shapeInfos,inputs){if(shapeInfos.length!==inputs.length)throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);shapeInfos.forEach((s,i)=>{let shapeA=s.logicalShape,input2=inputs[i],shapeB=input2.shape;if(!util_exports.arraysEqual(shapeA,shapeB))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);if(!(s.isUniform&&input2.isUniform)){let texShapeA=s.texShape,texShapeB=input2.isUniform?null:input2.texData.texShape;if(!util_exports.arraysEqual(texShapeA,texShapeB))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`)}})}function runProgram(gpgpu,binary,inputs,output,customSetup){validateBinaryAndProgram(binary.inShapeInfos,inputs),validateBinaryAndProgram([binary.outShapeInfo],[output]);let outTex=output.texData.texture,outTexShape=output.texData.texShape;output.texData.isPacked?gpgpu.setOutputPackedMatrixTexture(outTex,outTexShape[0],outTexShape[1]):gpgpu.setOutputMatrixTexture(outTex,outTexShape[0],outTexShape[1]),gpgpu.setProgram(binary.webGLProgram),env().getNumber("WEBGL_VERSION")===1&&(binary.infLoc!==null&&gpgpu.gl.uniform1f(binary.infLoc,Infinity)),binary.nanLoc!==null&&gpgpu.gl.uniform1f(binary.nanLoc,NaN),inputs.forEach((input2,i)=>{let varName=binary.program.variableNames[i],varLoc=binary.uniformLocations[varName],varOffsetLoc=binary.uniformLocations[`offset${varName}`];if(varLoc!=null){if(input2.isUniform){if(util_exports.sizeFromShape(input2.shape)<2)gpgpu.gl.uniform1f(varLoc,input2.uniformValues[0]);else{let vals=input2.uniformValues;vals instanceof Float32Array||(vals=new Float32Array(vals)),gpgpu.gl.uniform1fv(varLoc,vals)}return}input2.texData.slice!=null&&varOffsetLoc!=null&&gpgpu.gl.uniform1i(varOffsetLoc,input2.texData.slice.flatOffset),gpgpu.setInputMatrixTexture(input2.texData.texture,varLoc,i)}}),customSetup!=null&&customSetup(gpgpu,binary.webGLProgram),gpgpu.executeProgram()}function makeShaderKey(program,inputs,output){let keyInputs="";inputs.concat(output).forEach(x=>{let hasOffset=x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0,texShape=x.isUniform?"uniform":x.texData.texShape;keyInputs+=`${x.shape}_${texShape}_${hasOffset}`});let keyUserCode=program.userCode,key=program.constructor.name;return key+="_"+keyInputs+"_"+keyUserCode,key}var{addImpl:addImplCPU,bincountImpl:bincountImplCPU,bincountReduceImpl:bincountReduceImplCPU,ceilImpl:ceilImplCPU,concatImpl:concatImplCPU,expImpl:expImplCPU,expm1Impl:expm1ImplCPU,floorImpl:floorImplCPU,gatherV2Impl:gatherV2ImplCPU,greaterImpl:greaterImplCPU,lessImpl:lessImplCPU,linSpaceImpl:linSpaceImplCPU,logImpl:logImplCPU,maxImpl:maxImplCPU,maximumImpl:maximumImplCPU,minimumImpl:minimumImplCPU,multiplyImpl:multiplyImplCPU,negImpl:negImplCPU,prodImpl:prodImplCPU,rangeImpl:rangeImplCPU,rsqrtImpl:rsqrtImplCPU,simpleAbsImpl:simpleAbsImplCPU,sliceImpl:sliceImplCPU,stridedSliceImpl:stridedSliceImplCPU,subImpl:subImplCPU,tileImpl:tileImplCPU,topKImpl:topKImplCPU,transposeImpl:transposeImplCPU,uniqueImpl:uniqueImplCPU}=shared_exports;function getVecChannels(name,rank){return["x","y","z","w","u","v"].slice(0,rank).map(d=>`${name}.${d}`)}function getChannels(name,rank){return rank===1?[name]:getVecChannels(name,rank)}function getSourceCoords(rank,dims){if(rank===1)return"rc";let coords2="";for(let i=0;i<rank;i++)coords2+=dims[i],i<rank-1&&(coords2+=",");return coords2}var PackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=outputShape;let rank=outputShape.length;if(rank===0)this.userCode=`
+  `}function getCoordsDataType(rank){if(rank<=1)return"int";if(rank===2)return"ivec2";if(rank===3)return"ivec3";if(rank===4)return"ivec4";if(rank===5)return"ivec5";if(rank===6)return"ivec6";throw Error(`GPU for rank ${rank} is not yet supported`)}function squeezeInputInfo(inInfo,squeezedShape){let newInputInfo=JSON.parse(JSON.stringify(inInfo));return newInputInfo.shapeInfo.logicalShape=squeezedShape,newInputInfo}function getSqueezedParams(params,keptDims){return keptDims.map(d=>params[d]).join(", ")}function compileProgram(gpgpu,program,inputs,output){let userCode=program.userCode,inputInfos=inputs.map((input2,i)=>{let shapeInfo={logicalShape:input2.shape,texShape:input2.isUniform?null:input2.texData.texShape,isUniform:input2.isUniform,isPacked:input2.isUniform?!1:input2.texData.isPacked,flatOffset:null};return input2.texData!=null&&input2.texData.slice!=null&&input2.texData.slice.flatOffset>0&&(shapeInfo.flatOffset=input2.texData.slice.flatOffset),{name:program.variableNames[i],shapeInfo}}),inShapeInfos=inputInfos.map(x=>x.shapeInfo),outShapeInfo={logicalShape:output.shape,texShape:output.texData.texShape,isUniform:!1,isPacked:output.texData.isPacked,flatOffset:null},source=makeShader(inputInfos,outShapeInfo,userCode,program.packedInputs),webGLProgram=gpgpu.createProgram(source),infLoc=null,nanLoc=gpgpu.getUniformLocation(webGLProgram,"NAN",!1);env().getNumber("WEBGL_VERSION")===1&&(infLoc=gpgpu.getUniformLocation(webGLProgram,"INFINITY",!1));let uniformLocations={};for(let i=0;i<program.variableNames.length;i++){let varName=program.variableNames[i],shouldThrow=!1;uniformLocations[varName]=gpgpu.getUniformLocation(webGLProgram,varName,shouldThrow),uniformLocations[`offset${varName}`]=gpgpu.getUniformLocation(webGLProgram,`offset${varName}`,shouldThrow)}return{program,source,webGLProgram,uniformLocations,inShapeInfos,outShapeInfo,infLoc,nanLoc}}function validateBinaryAndProgram(shapeInfos,inputs){if(shapeInfos.length!==inputs.length)throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);shapeInfos.forEach((s,i)=>{let shapeA=s.logicalShape,input2=inputs[i],shapeB=input2.shape;if(!util_exports.arraysEqual(shapeA,shapeB))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);if(s.isUniform&&input2.isUniform)return;let texShapeA=s.texShape,texShapeB=input2.isUniform?null:input2.texData.texShape;if(!util_exports.arraysEqual(texShapeA,texShapeB))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`)})}function runProgram(gpgpu,binary,inputs,output,customSetup){validateBinaryAndProgram(binary.inShapeInfos,inputs),validateBinaryAndProgram([binary.outShapeInfo],[output]);let outTex=output.texData.texture,outTexShape=output.texData.texShape;output.texData.isPacked?gpgpu.setOutputPackedMatrixTexture(outTex,outTexShape[0],outTexShape[1]):gpgpu.setOutputMatrixTexture(outTex,outTexShape[0],outTexShape[1]),gpgpu.setProgram(binary.webGLProgram),env().getNumber("WEBGL_VERSION")===1&&binary.infLoc!==null&&gpgpu.gl.uniform1f(binary.infLoc,Infinity),binary.nanLoc!==null&&gpgpu.gl.uniform1f(binary.nanLoc,NaN),inputs.forEach((input2,i)=>{let varName=binary.program.variableNames[i],varLoc=binary.uniformLocations[varName],varOffsetLoc=binary.uniformLocations[`offset${varName}`];if(varLoc!=null){if(input2.isUniform){if(util_exports.sizeFromShape(input2.shape)<2)gpgpu.gl.uniform1f(varLoc,input2.uniformValues[0]);else{let vals=input2.uniformValues;vals instanceof Float32Array||(vals=new Float32Array(vals)),gpgpu.gl.uniform1fv(varLoc,vals)}return}input2.texData.slice!=null&&varOffsetLoc!=null&&gpgpu.gl.uniform1i(varOffsetLoc,input2.texData.slice.flatOffset),gpgpu.setInputMatrixTexture(input2.texData.texture,varLoc,i)}}),customSetup!=null&&customSetup(gpgpu,binary.webGLProgram),gpgpu.executeProgram()}function makeShaderKey(program,inputs,output){let keyInputs="";inputs.concat(output).forEach(x=>{let hasOffset=x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0,texShape=x.isUniform?"uniform":x.texData.texShape;keyInputs+=`${x.shape}_${texShape}_${hasOffset}`});let keyUserCode=program.userCode,key=program.constructor.name;return key+="_"+keyInputs+"_"+keyUserCode,key}var{addImpl:addImplCPU,bincountImpl:bincountImplCPU,bincountReduceImpl:bincountReduceImplCPU,ceilImpl:ceilImplCPU,concatImpl:concatImplCPU,expImpl:expImplCPU,expm1Impl:expm1ImplCPU,floorImpl:floorImplCPU,gatherV2Impl:gatherV2ImplCPU,greaterImpl:greaterImplCPU,lessImpl:lessImplCPU,linSpaceImpl:linSpaceImplCPU,logImpl:logImplCPU,maxImpl:maxImplCPU,maximumImpl:maximumImplCPU,minimumImpl:minimumImplCPU,multiplyImpl:multiplyImplCPU,negImpl:negImplCPU,prodImpl:prodImplCPU,rangeImpl:rangeImplCPU,rsqrtImpl:rsqrtImplCPU,simpleAbsImpl:simpleAbsImplCPU,sliceImpl:sliceImplCPU,stridedSliceImpl:stridedSliceImplCPU,subImpl:subImplCPU,tileImpl:tileImplCPU,topKImpl:topKImplCPU,transposeImpl:transposeImplCPU,uniqueImpl:uniqueImplCPU}=shared_exports;function getVecChannels(name,rank){return["x","y","z","w","u","v"].slice(0,rank).map(d=>`${name}.${d}`)}function getChannels(name,rank){return rank===1?[name]:getVecChannels(name,rank)}function getSourceCoords(rank,dims){if(rank===1)return"rc";let coords2="";for(let i=0;i<rank;i++)coords2+=dims[i],i<rank-1&&(coords2+=",");return coords2}var PackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=outputShape;let rank=outputShape.length;if(rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
@@ -856,7 +856,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             0, 0`:`getA(${sourceCoords[0]}),
           cEdge ? 0. : getA(${sourceCoords[1]}),
           rEdge ? 0. : getA(${sourceCoords[2]}),
-          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`}var ReshapePackedProgram=class{constructor(outputShape,inputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let mainLoop="";for(let i=0;i<4;i++){let thisRC="thisRC = rc;";i%2===1&&(thisRC+="thisRC.z += 1;"),i>1&&(thisRC+="thisRC.y += 1;"),mainLoop+=`
+          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`}var ReshapePackedProgram=class{constructor(outputShape,inputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let mainLoop="";for(let i=0;i<4;i++){let thisRC="thisRC = rc;";i%2==1&&(thisRC+="thisRC.z += 1;"),i>1&&(thisRC+="thisRC.y += 1;"),mainLoop+=`
         ${thisRC}
         ${i>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -889,7 +889,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape)}
       return ivec3(r, c, d);
     }
-  `}var TextureManager=class{constructor(gpgpu){this.gpgpu=gpgpu,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(shapeRC,usage,isPacked){let physicalTexType=getPhysicalFromLogicalTextureType(usage,isPacked),shapeKey=getKeyFromTextureShape(shapeRC,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]),shapeKey in this.usedTextures||(this.usedTextures[shapeKey]=[]);let texBytes=computeBytes(shapeRC,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked);if(this.freeTextures[shapeKey].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=texBytes,this.log();let newTexture2=this.freeTextures[shapeKey].shift();return this.usedTextures[shapeKey].push(newTexture2),newTexture2}let newTexture;return physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT32?newTexture=this.gpgpu.createPackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT16?newTexture=this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT32?newTexture=this.gpgpu.createFloat32MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT16?newTexture=this.gpgpu.createFloat16MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(newTexture=this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0],shapeRC[1])),this.usedTextures[shapeKey].push(newTexture),this.numUsedTextures++,this._numBytesAllocated+=texBytes,this.log(),newTexture}releaseTexture(texture,shape,logicalTexType,isPacked){if(this.freeTextures!=null){let physicalTexType=getPhysicalFromLogicalTextureType(logicalTexType,isPacked),shapeKey=getKeyFromTextureShape(shape,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]);let texBytes=computeBytes(shape,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked),deleteTexThreshold=env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");deleteTexThreshold!==-1&&this._numBytesAllocated>deleteTexThreshold?(this.gpgpu.deleteMatrixTexture(texture),this._numBytesAllocated-=texBytes):(this.freeTextures[shapeKey].push(texture),this.numFreeTextures++,this._numBytesFree+=texBytes),this.numUsedTextures--;let texList=this.usedTextures[shapeKey],texIndex=texList.indexOf(texture);if(texIndex<0)throw new Error("Cannot release a texture that was never provided by this texture manager");texList.splice(texIndex,1),this.log()}}log(){if(!!this.logEnabled){let total=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${total})`);let freeRatio=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*freeRatio)}%)`)}}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let texShape in this.freeTextures)this.freeTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});for(let texShape in this.usedTextures)this.usedTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function numBytesForInternalFormat(gl,internalFormat){let glany=gl;if(internalFormat===glany.R32F)return 4;if(internalFormat===glany.R16F)return 2;if(internalFormat===glany.RGBA32F)return 16;if(internalFormat===gl.RGBA)return 16;if(internalFormat===glany.RGBA16F)return 8;throw new Error(`Unknown internal format ${internalFormat}`)}function computeBytes(shape,physicalTexType,gl,textureConfig,isPacked){let internalFormat=internalFormatForPhysicalTexType(physicalTexType,textureConfig),numElements;if(isPacked){let[packedWidth,packedHeight]=getPackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=packedWidth*packedHeight}else{let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=width*height}let bytesPerElement2=numBytesForInternalFormat(gl,internalFormat);return numElements*bytesPerElement2}function internalFormatForPhysicalTexType(physicalTexType,textureConfig){switch(physicalTexType){case PhysicalTextureType.PACKED_2X2_FLOAT32:return getInternalFormatForPackedMatrixTexture(textureConfig);case PhysicalTextureType.PACKED_2X2_FLOAT16:return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT32:return getInternalFormatForFloat32MatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT16:return getInternalFormatForFloat16MatrixTexture(textureConfig);case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);default:throw new Error(`Unknown physical texture type ${physicalTexType}`)}}function getPhysicalTextureForRendering(isPacked){return env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?isPacked?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32:isPacked?PhysicalTextureType.PACKED_2X2_FLOAT16:PhysicalTextureType.UNPACKED_FLOAT16}function getPhysicalFromLogicalTextureType(logicalTexType,isPacked){if(logicalTexType===TextureUsage.UPLOAD)return PhysicalTextureType.PACKED_2X2_FLOAT32;if(logicalTexType===TextureUsage.RENDER||logicalTexType==null)return getPhysicalTextureForRendering(isPacked);if(logicalTexType===TextureUsage.DOWNLOAD||logicalTexType===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${logicalTexType}`)}function getKeyFromTextureShape(shapeRowsCol,physicalTexType,isPacked){return`${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`}var UnaryOpProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
+  `}var TextureManager=class{constructor(gpgpu){this.gpgpu=gpgpu,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(shapeRC,usage,isPacked){let physicalTexType=getPhysicalFromLogicalTextureType(usage,isPacked),shapeKey=getKeyFromTextureShape(shapeRC,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]),shapeKey in this.usedTextures||(this.usedTextures[shapeKey]=[]);let texBytes=computeBytes(shapeRC,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked);if(this.freeTextures[shapeKey].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=texBytes,this.log();let newTexture2=this.freeTextures[shapeKey].shift();return this.usedTextures[shapeKey].push(newTexture2),newTexture2}let newTexture;return physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT32?newTexture=this.gpgpu.createPackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT16?newTexture=this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT32?newTexture=this.gpgpu.createFloat32MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT16?newTexture=this.gpgpu.createFloat16MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(newTexture=this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0],shapeRC[1])),this.usedTextures[shapeKey].push(newTexture),this.numUsedTextures++,this._numBytesAllocated+=texBytes,this.log(),newTexture}releaseTexture(texture,shape,logicalTexType,isPacked){if(this.freeTextures==null)return;let physicalTexType=getPhysicalFromLogicalTextureType(logicalTexType,isPacked),shapeKey=getKeyFromTextureShape(shape,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]);let texBytes=computeBytes(shape,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked),deleteTexThreshold=env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");deleteTexThreshold!==-1&&this._numBytesAllocated>deleteTexThreshold?(this.gpgpu.deleteMatrixTexture(texture),this._numBytesAllocated-=texBytes):(this.freeTextures[shapeKey].push(texture),this.numFreeTextures++,this._numBytesFree+=texBytes),this.numUsedTextures--;let texList=this.usedTextures[shapeKey],texIndex=texList.indexOf(texture);if(texIndex<0)throw new Error("Cannot release a texture that was never provided by this texture manager");texList.splice(texIndex,1),this.log()}log(){if(!this.logEnabled)return;let total=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${total})`);let freeRatio=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*freeRatio)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let texShape in this.freeTextures)this.freeTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});for(let texShape in this.usedTextures)this.usedTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function numBytesForInternalFormat(gl,internalFormat){let glany=gl;if(internalFormat===glany.R32F)return 4;if(internalFormat===glany.R16F)return 2;if(internalFormat===glany.RGBA32F)return 16;if(internalFormat===gl.RGBA)return 16;if(internalFormat===glany.RGBA16F)return 8;throw new Error(`Unknown internal format ${internalFormat}`)}function computeBytes(shape,physicalTexType,gl,textureConfig,isPacked){let internalFormat=internalFormatForPhysicalTexType(physicalTexType,textureConfig),numElements;if(isPacked){let[packedWidth,packedHeight]=getPackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=packedWidth*packedHeight}else{let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=width*height}let bytesPerElement2=numBytesForInternalFormat(gl,internalFormat);return numElements*bytesPerElement2}function internalFormatForPhysicalTexType(physicalTexType,textureConfig){switch(physicalTexType){case PhysicalTextureType.PACKED_2X2_FLOAT32:return getInternalFormatForPackedMatrixTexture(textureConfig);case PhysicalTextureType.PACKED_2X2_FLOAT16:return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT32:return getInternalFormatForFloat32MatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT16:return getInternalFormatForFloat16MatrixTexture(textureConfig);case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);default:throw new Error(`Unknown physical texture type ${physicalTexType}`)}}function getPhysicalTextureForRendering(isPacked){return env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?isPacked?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32:isPacked?PhysicalTextureType.PACKED_2X2_FLOAT16:PhysicalTextureType.UNPACKED_FLOAT16}function getPhysicalFromLogicalTextureType(logicalTexType,isPacked){if(logicalTexType===TextureUsage.UPLOAD)return PhysicalTextureType.PACKED_2X2_FLOAT32;if(logicalTexType===TextureUsage.RENDER||logicalTexType==null)return getPhysicalTextureForRendering(isPacked);if(logicalTexType===TextureUsage.DOWNLOAD||logicalTexType===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${logicalTexType}`)}function getKeyFromTextureShape(shapeRowsCol,physicalTexType,isPacked){return`${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`}var UnaryOpProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
       float unaryOperation(float x) {
         ${opSnippet}
       }
@@ -900,7 +900,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},CHECK_NAN_SNIPPET="if (isnan(x)) return x;",LINEAR="return x;",ABS="return abs(x);",ELU2="return (x >= 0.0) ? x : (exp(x) - 1.0);",RELU=CHECK_NAN_SNIPPET+`
+    `}},CHECK_NAN_SNIPPET="if (isnan(x)) return x;",LINEAR="return x;",ABS="return abs(x);";var ELU2="return (x >= 0.0) ? x : (exp(x) - 1.0);",RELU=CHECK_NAN_SNIPPET+`
   return (x < 0.0) ? 0.0 : x;
 `,RELU6=CHECK_NAN_SNIPPET+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
@@ -951,10 +951,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(getChannel(packedInput, ${coords2}));
       }
-    `}};var whereImpl3=kernel_impls_exports.whereImpl,EPSILON_FLOAT322=1e-7,EPSILON_FLOAT162=1e-4,binaryCaches={};function getBinaryCache(webGLVersion){return webGLVersion in binaryCaches||(binaryCaches[webGLVersion]={}),binaryCaches[webGLVersion]}var CPU_HANDOFF_SIZE_THRESHOLD=128,BEFORE_PAGING_CONSTANT=600;function numMBBeforeWarning(){return env().global.screen==null?1024:env().global.screen.height*env().global.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024/1024}var MathBackendWebGL=class extends KernelBackend{constructor(gpgpu){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(gpgpu==null){let gl=getWebGLContext(env().getNumber("WEBGL_VERSION"));this.binaryCache=getBinaryCache(env().getNumber("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(gl),this.canvas=gl.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=gpgpu,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=gpgpu.gl.canvas;this.textureManager=new TextureManager(this.gpgpu),this.numMBBeforeWarning=numMBBeforeWarning(),this.texData=new DataStorage(this,engine())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(values,shape,dtype){if((env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||env().getBool("DEBUG"))&&this.checkNumericalProblems(values),dtype==="complex64"&&values!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let dataId={};return this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0}),dataId}incRef(dataId){let texData=this.texData.get(dataId);texData.refCount++}decRef(dataId){if(this.texData.has(dataId)){let texData=this.texData.get(dataId);texData.refCount--}}move(dataId,values,shape,dtype){if(env().getBool("DEBUG")&&this.checkNumericalProblems(values),dtype==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.texData.has(dataId)){let textureData=this.texData.get(dataId);textureData.refCount--,textureData.refCount<1&&this.disposeData(dataId)}}readSync(dataId){let texData=this.texData.get(dataId),{values,dtype,complexTensorInfos,slice:slice5,shape,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.readSync(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(dtype==="string")return values;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let result;if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);result=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else result=this.getValuesFromTexture(dataId);return shouldTimeProgram&&(this.downloadWaitMs+=util_exports.now()-start),this.convertAndCacheOnCPU(dataId,result)}async read(dataId){if(this.pendingRead.has(dataId)){let subscribers2=this.pendingRead.get(dataId);return new Promise(resolve=>subscribers2.push(resolve))}let texData=this.texData.get(dataId),{values,shape,slice:slice5,dtype,complexTensorInfos,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.read(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let buffer2=null,tmpDownloadTarget;if(dtype!=="complex64"&&env().get("WEBGL_BUFFER_SUPPORTED")){tmpDownloadTarget=this.decode(dataId);let tmpData=this.texData.get(tmpDownloadTarget.dataId);buffer2=this.gpgpu.createBufferFromTexture(tmpData.texture,...getDenseTexShape(shape))}this.pendingRead.set(dataId,[]),dtype!=="complex64"&&await this.gpgpu.createAndWaitForFence();let vals;if(dtype==="complex64"){let ps=await Promise.all([this.read(complexTensorInfos.real.dataId),this.read(complexTensorInfos.imag.dataId)]),realValues=ps[0],imagValues=ps[1];vals=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else if(buffer2==null)vals=this.getValuesFromTexture(dataId);else{let size=util_exports.sizeFromShape(shape);vals=this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2,size)}tmpDownloadTarget!=null&&this.disposeIntermediateTensorInfo(tmpDownloadTarget);let dTypeVals=this.convertAndCacheOnCPU(dataId,vals),subscribers=this.pendingRead.get(dataId);return this.pendingRead.delete(dataId),subscribers.forEach(resolve=>resolve(dTypeVals)),this.pendingDisposal.has(dataId)&&(this.pendingDisposal.delete(dataId),this.disposeData(dataId),this.pendingDeletes--),dTypeVals}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}checkNumericalProblems(values){if(values!=null)for(let i=0;i<values.length;i++){let num=values[i];if(!canBeRepresented(num))throw env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${num} cannot be represented on this device.`)}}getValuesFromTexture(dataId){let{shape,dtype,isPacked}=this.texData.get(dataId),size=util_exports.sizeFromShape(shape);if(env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let tmpTarget=this.decode(dataId),tmpData2=this.texData.get(tmpTarget.dataId),vals2=this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture,...getDenseTexShape(shape)).subarray(0,size);return this.disposeIntermediateTensorInfo(tmpTarget),vals2}let shouldUsePackedProgram=env().getBool("WEBGL_PACK")&&isPacked===!0,outputShape=shouldUsePackedProgram?getShapeAs3D(shape):shape,program=shouldUsePackedProgram?new EncodeFloatPackedProgram(outputShape):new EncodeFloatProgram(outputShape),output=this.runWebGLProgram(program,[{shape:outputShape,dtype,dataId}],"float32"),tmpData=this.texData.get(output.dataId),vals=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture,tmpData.texShape[0],tmpData.texShape[1]).subarray(0,size);return this.disposeIntermediateTensorInfo(output),vals}async time(f){let oldActiveTimers=this.activeTimers,newActiveTimers=[],outerMostTime=!1;this.programTimersStack==null?(this.programTimersStack=newActiveTimers,outerMostTime=!0):this.activeTimers.push(newActiveTimers),this.activeTimers=newActiveTimers,f();let flattenedActiveTimerQueries=util_exports.flatten(this.activeTimers.map(d=>d.query)).filter(d=>d!=null),flattenedActiveTimerNames=util_exports.flatten(this.activeTimers.map(d=>d.name)).filter(d=>d!=null);this.activeTimers=oldActiveTimers,outerMostTime&&(this.programTimersStack=null);let res={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let kernelMs=await Promise.all(flattenedActiveTimerQueries);res.kernelMs=util_exports.sum(kernelMs),res.getExtraProfileInfo=()=>kernelMs.map((d,i)=>({name:flattenedActiveTimerNames[i],ms:d})).map(d=>`${d.name}: ${d.ms}`).join(", ")}else res.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,res}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:util_exports.now(),endMs:null}}endTimer(query){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),query):(query.endMs=util_exports.now(),query)}async getQueryTime(query){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(query);let timerQuery=query;return timerQuery.endMs-timerQuery.startMs}disposeData(dataId){if(!this.pendingDisposal.has(dataId)){if(this.pendingRead.has(dataId)){this.pendingDisposal.add(dataId),this.pendingDeletes++;return}if(!!this.texData.has(dataId)){if(this.texData.get(dataId).complexParentRefCount>0){this.texData.get(dataId).refCount--;return}this.releaseGPUData(dataId);let{complexTensorInfos}=this.texData.get(dataId);complexTensorInfos!=null&&(this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.real),this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.imag)),this.texData.delete(dataId)}}}releaseGPUData(dataId){let{texture,dtype,texShape,usage,isPacked,slice:slice5}=this.texData.get(dataId),key=slice5&&slice5.origDataId||dataId,refCount=this.dataRefCount.get(key);refCount>1?this.dataRefCount.set(key,refCount-1):(this.dataRefCount.delete(key),texture!=null&&(this.numBytesInGPU-=this.computeBytes(texShape,dtype),this.textureManager.releaseTexture(texture,texShape,usage,isPacked)));let texData=this.texData.get(dataId);texData.texture=null,texData.texShape=null,texData.isPacked=!1,texData.slice=null}getTexture(dataId){return this.uploadToGPU(dataId),this.texData.get(dataId).texture}getDataInfo(dataId){return this.texData.get(dataId)}getCPUBackend(){return env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=engine().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(inputs,sizeThreshold=CPU_HANDOFF_SIZE_THRESHOLD){let cpuBackend=this.getCPUBackend();return!env().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&cpuBackend==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),cpuBackend!=null&&inputs.every(input2=>this.texData.get(input2.dataId).texture==null&&util_exports.sizeFromShape(input2.shape)<sizeThreshold)}getGPGPUContext(){return this.gpgpu}where(condition){backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let condVals=condition.dataSync();return whereImpl3(condition.shape,condVals)}packedUnaryOp(x,op2,dtype){let program=new UnaryOpPackedProgram(x.shape,op2);return this.compileAndRun(program,[x],dtype)}abs(x){if(this.shouldExecuteOnCPU([x])&&x.dtype!=="complex64"){let outValues=simpleAbsImplCPU(this.texData.get(x.dataId).values);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ABS,x.dtype);let program=new UnaryOpProgram(x.shape,ABS);return this.compileAndRun(program,[x])}makeTensorInfo(shape,dtype,values){let dataId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));dataId=this.write(encodedValues,shape,dtype)}else dataId=this.write(values,shape,dtype);return this.texData.get(dataId).usage=null,{dataId,shape,dtype}}makeOutput(shape,dtype,values){let{dataId}=this.makeTensorInfo(shape,dtype,values);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}unpackTensor(input2){let program=new UnpackProgram(input2.shape);return this.runWebGLProgram(program,[input2],input2.dtype)}packTensor(input2){let program=new PackProgram(input2.shape),preventEagerUnpackingOutput=!0;return this.runWebGLProgram(program,[input2],input2.dtype,null,preventEagerUnpackingOutput)}packedReshape(input2,afterShape){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=this.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}decode(dataId){let texData=this.texData.get(dataId),{isPacked,shape,dtype}=texData,shapeAs3D=getShapeAs3D(shape),program;isPacked?program=new DecodeMatrixPackedProgram(shapeAs3D):program=new DecodeMatrixProgram(shapeAs3D);let preventEagerUnpackingOfOutput=!0,out=this.runWebGLProgram(program,[{shape:shapeAs3D,dtype,dataId}],dtype,null,preventEagerUnpackingOfOutput);return{dtype,shape,dataId:out.dataId}}runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){let output=this.makeTensorInfo(program.outputShape,outputDtype),outData=this.texData.get(output.dataId);if(program.packedOutput&&(outData.isPacked=!0),program.outPackingScheme===PackingScheme.DENSE){let texelShape=getDenseTexShape(program.outputShape);outData.texShape=texelShape.map(d=>d*2)}if(program.outTexUsage!=null&&(outData.usage=program.outTexUsage),util_exports.sizeFromShape(output.shape)===0)return outData.values=util_exports.getTypedArrayFromDType(output.dtype,0),output;let dataToDispose=[],inputsData=inputs.map(input2=>{if(input2.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let texData=this.texData.get(input2.dataId);if(texData.texture==null){if(!program.packedInputs&&util_exports.sizeFromShape(input2.shape)<=env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:input2.shape,texData:null,isUniform:!0,uniformValues:texData.values};program.packedInputs&&(texData.isPacked=!0,texData.shape=input2.shape)}else if(!!texData.isPacked!==!!program.packedInputs)input2=texData.isPacked?this.unpackTensor(input2):this.packTensor(input2),dataToDispose.push(input2),texData=this.texData.get(input2.dataId);else if(texData.isPacked&&!isReshapeFree(texData.shape,input2.shape)){let savedInput=input2,targetShape=input2.shape;input2.shape=texData.shape,input2=this.packedReshape(input2,targetShape),dataToDispose.push(input2),texData=this.texData.get(input2.dataId),savedInput.shape=targetShape}return this.uploadToGPU(input2.dataId),{shape:input2.shape,texData,isUniform:!1}});this.uploadToGPU(output.dataId);let outputData={shape:output.shape,texData:outData,isUniform:!1},key=makeShaderKey(program,inputsData,outputData),binary=this.getAndSaveBinary(key,()=>compileProgram(this.gpgpu,program,inputsData,outputData)),shouldTimeProgram=this.activeTimers!=null,query;if(shouldTimeProgram&&(query=this.startTimer()),runProgram(this.gpgpu,binary,inputsData,outputData,customSetup),dataToDispose.forEach(info=>this.disposeIntermediateTensorInfo(info)),shouldTimeProgram&&(query=this.endTimer(query),this.activeTimers.push({name:program.constructor.name,query:this.getQueryTime(query)})),!env().getBool("WEBGL_LAZILY_UNPACK")&&outData.isPacked&&preventEagerUnpackingOfOutput===!1){let unpacked=this.unpackTensor(output);return this.disposeIntermediateTensorInfo(output),unpacked}return output}compileAndRun(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){outputDtype=outputDtype||inputs[0].dtype;let outInfo=this.runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput);return engine().makeTensorFromDataId(outInfo.dataId,outInfo.shape,outInfo.dtype)}getAndSaveBinary(key,getBinary){return key in this.binaryCache||(this.binaryCache[key]=getBinary()),this.binaryCache[key]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(env().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(key=>{this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram),delete this.binaryCache[key]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=tidy(()=>{if(!env().get("WEBGL_RENDER_FLOAT32_ENABLED")){let debugFlag=env().getBool("DEBUG");env().set("DEBUG",!1);let underflowCheckValue=this.abs(scalar(1e-8)).dataSync()[0];if(env().set("DEBUG",debugFlag),underflowCheckValue>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT322:EPSILON_FLOAT162}uploadToGPU(dataId){let texData=this.texData.get(dataId),{shape,dtype,values,texture,usage,isPacked}=texData;if(texture==null){let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let texShape=texData.texShape;if(texShape==null&&(texShape=getTextureShapeFromLogicalShape(shape,isPacked),texData.texShape=texShape),values!=null){let shapeAs3D=getShapeAs3D(shape),program,width=texShape[1],height=texShape[0],isByteArray=values instanceof Uint8Array;isPacked?([width,height]=getPackedMatrixTextureShapeWidthHeight(texShape[0],texShape[1]),program=new EncodeMatrixPackedProgram(shapeAs3D,[height,width],isByteArray)):program=new EncodeMatrixProgram(shapeAs3D,[height,width],isByteArray);let tempDenseInputHandle=this.makeTensorInfo([height,width],dtype);isByteArray?this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.PIXELS:this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId),width,height,values);let preventEagerUnpacking=!0,encodedOutputTarget=this.runWebGLProgram(program,[tempDenseInputHandle],dtype,null,preventEagerUnpacking),outputTexData=this.texData.get(encodedOutputTarget.dataId);texData.texture=outputTexData.texture,texData.texShape=outputTexData.texShape,texData.isPacked=outputTexData.isPacked,texData.usage=outputTexData.usage,this.disposeIntermediateTensorInfo(tempDenseInputHandle),this.texData.delete(encodedOutputTarget.dataId),texData.values=null,shouldTimeProgram&&(this.uploadWaitMs+=util_exports.now()-start)}else{let newTexture=this.acquireTexture(texShape,usage,dtype,isPacked);texData.texture=newTexture}}}convertAndCacheOnCPU(dataId,float32Values){let texData=this.texData.get(dataId),{dtype}=texData;return this.releaseGPUData(dataId),float32Values!=null&&(texData.values=float32ToTypedArray(float32Values,dtype)),texData.values}acquireTexture(texShape,texType,dtype,isPacked){if(this.numBytesInGPU+=this.computeBytes(texShape,dtype),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let mb=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(texShape,texType,isPacked)}computeBytes(shape,dtype){return shape[0]*shape[1]*util_exports.bytesPerElement(dtype)}};function float32ToTypedArray(a,dtype){if(dtype==="float32"||dtype==="complex64")return a;if(dtype==="int32"||dtype==="bool"){let result=dtype==="int32"?new Int32Array(a.length):new Uint8Array(a.length);for(let i=0;i<result.length;++i)result[i]=Math.round(a[i]);return result}else throw new Error(`Unknown dtype ${dtype}`)}var version6="2.8.2";device_util_exports.isBrowser()&&registerBackend("webgl",()=>new MathBackendWebGL,2);var CHECK_NAN_SNIPPET2=`
+    `}};var whereImpl3=kernel_impls_exports.whereImpl,EPSILON_FLOAT322=1e-7,EPSILON_FLOAT162=1e-4,binaryCaches={};function getBinaryCache(webGLVersion){return webGLVersion in binaryCaches||(binaryCaches[webGLVersion]={}),binaryCaches[webGLVersion]}var CPU_HANDOFF_SIZE_THRESHOLD=128,BEFORE_PAGING_CONSTANT=600;function numMBBeforeWarning(){return env().global.screen==null?1024:env().global.screen.height*env().global.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024/1024}var MathBackendWebGL=class extends KernelBackend{constructor(gpgpu){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(gpgpu==null){let gl=getWebGLContext(env().getNumber("WEBGL_VERSION"));this.binaryCache=getBinaryCache(env().getNumber("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(gl),this.canvas=gl.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=gpgpu,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=gpgpu.gl.canvas;this.textureManager=new TextureManager(this.gpgpu),this.numMBBeforeWarning=numMBBeforeWarning(),this.texData=new DataStorage(this,engine())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(values,shape,dtype){if((env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||env().getBool("DEBUG"))&&this.checkNumericalProblems(values),dtype==="complex64"&&values!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let dataId={};return this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0}),dataId}incRef(dataId){let texData=this.texData.get(dataId);texData.refCount++}decRef(dataId){if(this.texData.has(dataId)){let texData=this.texData.get(dataId);texData.refCount--}}move(dataId,values,shape,dtype){if(env().getBool("DEBUG")&&this.checkNumericalProblems(values),dtype==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.texData.has(dataId)){let textureData=this.texData.get(dataId);textureData.refCount--,textureData.refCount<1&&this.disposeData(dataId)}}readSync(dataId){let texData=this.texData.get(dataId),{values,dtype,complexTensorInfos,slice:slice5,shape,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.readSync(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(dtype==="string")return values;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let result;if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);result=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else result=this.getValuesFromTexture(dataId);return shouldTimeProgram&&(this.downloadWaitMs+=util_exports.now()-start),this.convertAndCacheOnCPU(dataId,result)}async read(dataId){if(this.pendingRead.has(dataId)){let subscribers2=this.pendingRead.get(dataId);return new Promise(resolve=>subscribers2.push(resolve))}let texData=this.texData.get(dataId),{values,shape,slice:slice5,dtype,complexTensorInfos,isPacked}=texData;if(slice5!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.read(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let buffer2=null,tmpDownloadTarget;if(dtype!=="complex64"&&env().get("WEBGL_BUFFER_SUPPORTED")){tmpDownloadTarget=this.decode(dataId);let tmpData=this.texData.get(tmpDownloadTarget.dataId);buffer2=this.gpgpu.createBufferFromTexture(tmpData.texture,...getDenseTexShape(shape))}this.pendingRead.set(dataId,[]),dtype!=="complex64"&&await this.gpgpu.createAndWaitForFence();let vals;if(dtype==="complex64"){let ps=await Promise.all([this.read(complexTensorInfos.real.dataId),this.read(complexTensorInfos.imag.dataId)]),realValues=ps[0],imagValues=ps[1];vals=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else if(buffer2==null)vals=this.getValuesFromTexture(dataId);else{let size=util_exports.sizeFromShape(shape);vals=this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2,size)}tmpDownloadTarget!=null&&this.disposeIntermediateTensorInfo(tmpDownloadTarget);let dTypeVals=this.convertAndCacheOnCPU(dataId,vals),subscribers=this.pendingRead.get(dataId);return this.pendingRead.delete(dataId),subscribers.forEach(resolve=>resolve(dTypeVals)),this.pendingDisposal.has(dataId)&&(this.pendingDisposal.delete(dataId),this.disposeData(dataId),this.pendingDeletes--),dTypeVals}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}checkNumericalProblems(values){if(values!=null)for(let i=0;i<values.length;i++){let num=values[i];if(!canBeRepresented(num))throw env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${num} cannot be represented on this device.`)}}getValuesFromTexture(dataId){let{shape,dtype,isPacked}=this.texData.get(dataId),size=util_exports.sizeFromShape(shape);if(env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let tmpTarget=this.decode(dataId),tmpData2=this.texData.get(tmpTarget.dataId),vals2=this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture,...getDenseTexShape(shape)).subarray(0,size);return this.disposeIntermediateTensorInfo(tmpTarget),vals2}let shouldUsePackedProgram=env().getBool("WEBGL_PACK")&&isPacked===!0,outputShape=shouldUsePackedProgram?getShapeAs3D(shape):shape,program=shouldUsePackedProgram?new EncodeFloatPackedProgram(outputShape):new EncodeFloatProgram(outputShape),output=this.runWebGLProgram(program,[{shape:outputShape,dtype,dataId}],"float32"),tmpData=this.texData.get(output.dataId),vals=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture,tmpData.texShape[0],tmpData.texShape[1]).subarray(0,size);return this.disposeIntermediateTensorInfo(output),vals}async time(f){let oldActiveTimers=this.activeTimers,newActiveTimers=[],outerMostTime=!1;this.programTimersStack==null?(this.programTimersStack=newActiveTimers,outerMostTime=!0):this.activeTimers.push(newActiveTimers),this.activeTimers=newActiveTimers,f();let flattenedActiveTimerQueries=util_exports.flatten(this.activeTimers.map(d=>d.query)).filter(d=>d!=null),flattenedActiveTimerNames=util_exports.flatten(this.activeTimers.map(d=>d.name)).filter(d=>d!=null);this.activeTimers=oldActiveTimers,outerMostTime&&(this.programTimersStack=null);let res={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let kernelMs=await Promise.all(flattenedActiveTimerQueries);res.kernelMs=util_exports.sum(kernelMs),res.getExtraProfileInfo=()=>kernelMs.map((d,i)=>({name:flattenedActiveTimerNames[i],ms:d})).map(d=>`${d.name}: ${d.ms}`).join(", ")}else res.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,res}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:util_exports.now(),endMs:null}}endTimer(query){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),query):(query.endMs=util_exports.now(),query)}async getQueryTime(query){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(query);let timerQuery=query;return timerQuery.endMs-timerQuery.startMs}disposeData(dataId){if(this.pendingDisposal.has(dataId))return;if(this.pendingRead.has(dataId)){this.pendingDisposal.add(dataId),this.pendingDeletes++;return}if(!this.texData.has(dataId))return;if(this.texData.get(dataId).complexParentRefCount>0){this.texData.get(dataId).refCount--;return}this.releaseGPUData(dataId);let{complexTensorInfos}=this.texData.get(dataId);complexTensorInfos!=null&&(this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.real),this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.imag)),this.texData.delete(dataId)}releaseGPUData(dataId){let{texture,dtype,texShape,usage,isPacked,slice:slice5}=this.texData.get(dataId),key=slice5&&slice5.origDataId||dataId,refCount=this.dataRefCount.get(key);refCount>1?this.dataRefCount.set(key,refCount-1):(this.dataRefCount.delete(key),texture!=null&&(this.numBytesInGPU-=this.computeBytes(texShape,dtype),this.textureManager.releaseTexture(texture,texShape,usage,isPacked)));let texData=this.texData.get(dataId);texData.texture=null,texData.texShape=null,texData.isPacked=!1,texData.slice=null}getTexture(dataId){return this.uploadToGPU(dataId),this.texData.get(dataId).texture}getDataInfo(dataId){return this.texData.get(dataId)}getCPUBackend(){return env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=engine().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(inputs,sizeThreshold=CPU_HANDOFF_SIZE_THRESHOLD){let cpuBackend=this.getCPUBackend();return!env().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&cpuBackend==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),cpuBackend!=null&&inputs.every(input2=>this.texData.get(input2.dataId).texture==null&&util_exports.sizeFromShape(input2.shape)<sizeThreshold)}getGPGPUContext(){return this.gpgpu}where(condition){backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let condVals=condition.dataSync();return whereImpl3(condition.shape,condVals)}packedUnaryOp(x,op2,dtype){let program=new UnaryOpPackedProgram(x.shape,op2);return this.compileAndRun(program,[x],dtype)}abs(x){if(this.shouldExecuteOnCPU([x])&&x.dtype!=="complex64"){let outValues=simpleAbsImplCPU(this.texData.get(x.dataId).values);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ABS,x.dtype);let program=new UnaryOpProgram(x.shape,ABS);return this.compileAndRun(program,[x])}makeTensorInfo(shape,dtype,values){let dataId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));dataId=this.write(encodedValues,shape,dtype)}else dataId=this.write(values,shape,dtype);return this.texData.get(dataId).usage=null,{dataId,shape,dtype}}makeOutput(shape,dtype,values){let{dataId}=this.makeTensorInfo(shape,dtype,values);return engine().makeTensorFromDataId(dataId,shape,dtype,this)}unpackTensor(input2){let program=new UnpackProgram(input2.shape);return this.runWebGLProgram(program,[input2],input2.dtype)}packTensor(input2){let program=new PackProgram(input2.shape),preventEagerUnpackingOutput=!0;return this.runWebGLProgram(program,[input2],input2.dtype,null,preventEagerUnpackingOutput)}packedReshape(input2,afterShape){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=this.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}decode(dataId){let texData=this.texData.get(dataId),{isPacked,shape,dtype}=texData,shapeAs3D=getShapeAs3D(shape),program;isPacked?program=new DecodeMatrixPackedProgram(shapeAs3D):program=new DecodeMatrixProgram(shapeAs3D);let preventEagerUnpackingOfOutput=!0,out=this.runWebGLProgram(program,[{shape:shapeAs3D,dtype,dataId}],dtype,null,preventEagerUnpackingOfOutput);return{dtype,shape,dataId:out.dataId}}runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){let output=this.makeTensorInfo(program.outputShape,outputDtype),outData=this.texData.get(output.dataId);if(program.packedOutput&&(outData.isPacked=!0),program.outPackingScheme===PackingScheme.DENSE){let texelShape=getDenseTexShape(program.outputShape);outData.texShape=texelShape.map(d=>d*2)}if(program.outTexUsage!=null&&(outData.usage=program.outTexUsage),util_exports.sizeFromShape(output.shape)===0)return outData.values=util_exports.getTypedArrayFromDType(output.dtype,0),output;let dataToDispose=[],inputsData=inputs.map(input2=>{if(input2.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let texData=this.texData.get(input2.dataId);if(texData.texture==null){if(!program.packedInputs&&util_exports.sizeFromShape(input2.shape)<=env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:input2.shape,texData:null,isUniform:!0,uniformValues:texData.values};program.packedInputs&&(texData.isPacked=!0,texData.shape=input2.shape)}else if(!!texData.isPacked!=!!program.packedInputs)input2=texData.isPacked?this.unpackTensor(input2):this.packTensor(input2),dataToDispose.push(input2),texData=this.texData.get(input2.dataId);else if(texData.isPacked&&!isReshapeFree(texData.shape,input2.shape)){let savedInput=input2,targetShape=input2.shape;input2.shape=texData.shape,input2=this.packedReshape(input2,targetShape),dataToDispose.push(input2),texData=this.texData.get(input2.dataId),savedInput.shape=targetShape}return this.uploadToGPU(input2.dataId),{shape:input2.shape,texData,isUniform:!1}});this.uploadToGPU(output.dataId);let outputData={shape:output.shape,texData:outData,isUniform:!1},key=makeShaderKey(program,inputsData,outputData),binary=this.getAndSaveBinary(key,()=>compileProgram(this.gpgpu,program,inputsData,outputData)),shouldTimeProgram=this.activeTimers!=null,query;if(shouldTimeProgram&&(query=this.startTimer()),runProgram(this.gpgpu,binary,inputsData,outputData,customSetup),dataToDispose.forEach(info=>this.disposeIntermediateTensorInfo(info)),shouldTimeProgram&&(query=this.endTimer(query),this.activeTimers.push({name:program.constructor.name,query:this.getQueryTime(query)})),!env().getBool("WEBGL_LAZILY_UNPACK")&&outData.isPacked&&preventEagerUnpackingOfOutput===!1){let unpacked=this.unpackTensor(output);return this.disposeIntermediateTensorInfo(output),unpacked}return output}compileAndRun(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){outputDtype=outputDtype||inputs[0].dtype;let outInfo=this.runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput);return engine().makeTensorFromDataId(outInfo.dataId,outInfo.shape,outInfo.dtype)}getAndSaveBinary(key,getBinary){return key in this.binaryCache||(this.binaryCache[key]=getBinary()),this.binaryCache[key]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(env().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(key=>{this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram),delete this.binaryCache[key]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=tidy(()=>{if(!env().get("WEBGL_RENDER_FLOAT32_ENABLED")){let debugFlag=env().getBool("DEBUG");env().set("DEBUG",!1);let underflowCheckValue=this.abs(scalar(1e-8)).dataSync()[0];if(env().set("DEBUG",debugFlag),underflowCheckValue>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT322:EPSILON_FLOAT162}uploadToGPU(dataId){let texData=this.texData.get(dataId),{shape,dtype,values,texture,usage,isPacked}=texData;if(texture!=null)return;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let texShape=texData.texShape;if(texShape==null&&(texShape=getTextureShapeFromLogicalShape(shape,isPacked),texData.texShape=texShape),values!=null){let shapeAs3D=getShapeAs3D(shape),program,width=texShape[1],height=texShape[0],isByteArray=values instanceof Uint8Array;isPacked?([width,height]=getPackedMatrixTextureShapeWidthHeight(texShape[0],texShape[1]),program=new EncodeMatrixPackedProgram(shapeAs3D,[height,width],isByteArray)):program=new EncodeMatrixProgram(shapeAs3D,[height,width],isByteArray);let tempDenseInputHandle=this.makeTensorInfo([height,width],dtype);isByteArray?this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.PIXELS:this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId),width,height,values);let preventEagerUnpacking=!0,encodedOutputTarget=this.runWebGLProgram(program,[tempDenseInputHandle],dtype,null,preventEagerUnpacking),outputTexData=this.texData.get(encodedOutputTarget.dataId);texData.texture=outputTexData.texture,texData.texShape=outputTexData.texShape,texData.isPacked=outputTexData.isPacked,texData.usage=outputTexData.usage,this.disposeIntermediateTensorInfo(tempDenseInputHandle),this.texData.delete(encodedOutputTarget.dataId),texData.values=null,shouldTimeProgram&&(this.uploadWaitMs+=util_exports.now()-start)}else{let newTexture=this.acquireTexture(texShape,usage,dtype,isPacked);texData.texture=newTexture}}convertAndCacheOnCPU(dataId,float32Values){let texData=this.texData.get(dataId),{dtype}=texData;return this.releaseGPUData(dataId),float32Values!=null&&(texData.values=float32ToTypedArray(float32Values,dtype)),texData.values}acquireTexture(texShape,texType,dtype,isPacked){if(this.numBytesInGPU+=this.computeBytes(texShape,dtype),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let mb=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(texShape,texType,isPacked)}computeBytes(shape,dtype){return shape[0]*shape[1]*util_exports.bytesPerElement(dtype)}};function float32ToTypedArray(a,dtype){if(dtype==="float32"||dtype==="complex64")return a;if(dtype==="int32"||dtype==="bool"){let result=dtype==="int32"?new Int32Array(a.length):new Uint8Array(a.length);for(let i=0;i<result.length;++i)result[i]=Math.round(a[i]);return result}else throw new Error(`Unknown dtype ${dtype}`)}var version6="2.8.3";device_util_exports.isBrowser()&&registerBackend("webgl",()=>new MathBackendWebGL,2);var CHECK_NAN_SNIPPET2=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,BinaryOpProgram=class{constructor(op2,aShape,bShape){this.variableNames=["A","B"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
+`;var BinaryOpProgram=class{constructor(op2,aShape,bShape){this.variableNames=["A","B"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
       float binaryOperation(float a, float b) {
         ${op2}
       }
@@ -969,7 +969,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`,BinaryOpPackedProgram=class{constructor(op2,aShape,bShape,checkOutOfBounds=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape);let rank=this.outputShape.length,checkOutOfBoundsString="";if(checkOutOfBounds)if(rank===0||util_exports.sizeFromShape(this.outputShape)===1)checkOutOfBoundsString=`
+`;var BinaryOpPackedProgram=class{constructor(op2,aShape,bShape,checkOutOfBounds=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape);let rank=this.outputShape.length,checkOutOfBoundsString="";if(checkOutOfBounds)if(rank===0||util_exports.sizeFromShape(this.outputShape)===1)checkOutOfBoundsString=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
@@ -1814,7 +1814,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         ${lowerRow}
         setOutput(result);
       }
-    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null)||gpgpu.gl.uniform1iv(this.startLoc,start)}}};function shallowSlice(x,begin,size,backend2){let xTexData=backend2.texData.get(x.dataId),t=backend2.makeTensorInfo(size,x.dtype),newTexData=backend2.texData.get(t.dataId);Object.assign(newTexData,xTexData),newTexData.shape=size,newTexData.dtype=x.dtype;let flatOffset=slice_util_exports.computeFlatOffset(begin,util_exports.computeStrides(x.shape));xTexData.slice&&(flatOffset+=xTexData.slice.flatOffset),newTexData.slice={flatOffset,origDataId:xTexData.slice&&xTexData.slice.origDataId||x.dataId};let refCount=backend2.dataRefCount.get(newTexData.slice.origDataId)||1;return backend2.dataRefCount.set(newTexData.slice.origDataId,refCount+1),t}function slice3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs,[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);if(slice_util_exports.assertParamsValid(x,$begin,$size),util_exports.sizeFromShape($size)===0)return backend2.makeTensorInfo($size,x.dtype,[]);if(backend2.shouldExecuteOnCPU([x])||x.dtype==="string"){let xTexData=backend2.texData.get(x.dataId),outValues=sliceImplCPU(xTexData.values,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outValues)}let{isPacked}=backend2.texData.get(x.dataId),isContinous=slice_util_exports.isSliceContinous(x.shape,$begin,$size);if(isPacked||!isContinous){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram($size):new SliceProgram($size),customSetup=program.getCustomSetupFunc($begin);return backend2.runWebGLProgram(program,[x],x.dtype,customSetup)}return backend2.uploadToGPU(x.dataId),shallowSlice(x,$begin,$size,backend2)}var sliceConfig2={kernelName:Slice,backendName:"webgl",kernelFunc:slice3};var batchToSpaceND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;util_exports.assert(x.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),toDispose=[],reshapedIntermediate=reshape4({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),transposedIntermediate=transpose3({inputs:{x:reshapedIntermediate},backend:backend2,attrs:{perm:permuted}}),reshapedIntermediate2=reshape4({inputs:{x:transposedIntermediate},backend:backend2,attrs:{shape:reshapedPermuted}}),sliced=slice3({inputs:{x:reshapedIntermediate2},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return toDispose.push(reshapedIntermediate),toDispose.push(transposedIntermediate),toDispose.push(reshapedIntermediate2),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),sliced},batchToSpaceNDConfig2={kernelName:BatchToSpaceND,backendName:"webgl",kernelFunc:batchToSpaceND3};function bincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.texData.get(x.dataId).values,weightsVals=backend2.texData.get(weights.dataId).values,outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig2={kernelName:Bincount,backendName:"webgl",kernelFunc:bincount3};var NOT_EQUAL="return float(a != b);",notEqual3=binaryKernelFunc2({opSnippet:NOT_EQUAL,dtype:"bool"}),notEqualConfig2={kernelName:NotEqual,backendName:"webgl",kernelFunc:notEqual3};function real3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.real},backend:backend2})}var realConfig2={kernelName:Real,backendName:"webgl",kernelFunc:real3};var TO_INT="return float(int(x));";function int(input2,backend2){let program=new UnaryOpProgram(input2.shape,TO_INT),output=backend2.runWebGLProgram(program,[input2],"int32");return{dataId:output.dataId,shape:output.shape,dtype:output.dtype}}function cast4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity3({inputs:{x},backend:backend2});let zerosTensor=zeros(x.shape),floatX=cast4({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex3({inputs:{real:floatX,imag:zerosTensor},backend:backend2});return zerosTensor.dispose(),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real3({inputs:{input:x},backend:backend2}),result=cast4({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity3({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32")return int(x,backend2);if(dtype==="bool"){let zerosTensorInfo=backend2.makeTensorInfo([],"bool",util_exports.getTypedArrayFromDType("bool",1)),result=notEqual3({inputs:{a:x,b:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),result}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig2={kernelName:Cast,backendName:"webgl",kernelFunc:cast4};var CEIL="return ceil(x);",ceil3=unaryKernelFunc2({opSnippet:CEIL,packedOpSnippet:CEIL,cpuKernelImpl:ceilImplCPU}),ceilConfig2={kernelName:Ceil,backendName:"webgl",kernelFunc:ceil3};var ClipProgram=class{constructor(aShape){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
+    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null)||gpgpu.gl.uniform1iv(this.startLoc,start)}}};function shallowSlice(x,begin,size,backend2){let xTexData=backend2.texData.get(x.dataId),t=backend2.makeTensorInfo(size,x.dtype),newTexData=backend2.texData.get(t.dataId);Object.assign(newTexData,xTexData),newTexData.shape=size,newTexData.dtype=x.dtype;let flatOffset=slice_util_exports.computeFlatOffset(begin,util_exports.computeStrides(x.shape));xTexData.slice&&(flatOffset+=xTexData.slice.flatOffset),newTexData.slice={flatOffset,origDataId:xTexData.slice&&xTexData.slice.origDataId||x.dataId};let refCount=backend2.dataRefCount.get(newTexData.slice.origDataId)||1;return backend2.dataRefCount.set(newTexData.slice.origDataId,refCount+1),t}function slice3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{begin,size}=attrs,[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);if(slice_util_exports.assertParamsValid(x,$begin,$size),util_exports.sizeFromShape($size)===0)return backend2.makeTensorInfo($size,x.dtype,[]);if(backend2.shouldExecuteOnCPU([x])||x.dtype==="string"){let xTexData=backend2.texData.get(x.dataId),outValues=sliceImplCPU(xTexData.values,$begin,$size,x.shape,x.dtype);return backend2.makeTensorInfo($size,x.dtype,outValues)}let{isPacked}=backend2.texData.get(x.dataId),isContinous=slice_util_exports.isSliceContinous(x.shape,$begin,$size);if(isPacked||!isContinous){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram($size):new SliceProgram($size),customSetup=program.getCustomSetupFunc($begin);return backend2.runWebGLProgram(program,[x],x.dtype,customSetup)}return backend2.uploadToGPU(x.dataId),shallowSlice(x,$begin,$size,backend2)}var sliceConfig2={kernelName:Slice,backendName:"webgl",kernelFunc:slice3};var batchToSpaceND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,crops}=attrs;util_exports.assert(x.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length),toDispose=[],reshapedIntermediate=reshape4({inputs:{x},backend:backend2,attrs:{shape:reshaped}}),transposedIntermediate=transpose3({inputs:{x:reshapedIntermediate},backend:backend2,attrs:{perm:permuted}}),reshapedIntermediate2=reshape4({inputs:{x:transposedIntermediate},backend:backend2,attrs:{shape:reshapedPermuted}}),sliced=slice3({inputs:{x:reshapedIntermediate2},backend:backend2,attrs:{begin:sliceBeginCoords,size:sliceSize}});return toDispose.push(reshapedIntermediate),toDispose.push(transposedIntermediate),toDispose.push(reshapedIntermediate2),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),sliced},batchToSpaceNDConfig2={kernelName:BatchToSpaceND,backendName:"webgl",kernelFunc:batchToSpaceND3};function bincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size}=attrs,xVals=backend2.readSync(x.dataId),weightsVals=backend2.readSync(weights.dataId),outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}var bincountConfig2={kernelName:Bincount,backendName:"webgl",kernelFunc:bincount3};var NOT_EQUAL="return float(a != b);",notEqual3=binaryKernelFunc2({opSnippet:NOT_EQUAL,dtype:"bool"}),notEqualConfig2={kernelName:NotEqual,backendName:"webgl",kernelFunc:notEqual3};function real3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.real},backend:backend2})}var realConfig2={kernelName:Real,backendName:"webgl",kernelFunc:real3};var TO_INT="return float(int(x));";function int(input2,backend2){let program=new UnaryOpProgram(input2.shape,TO_INT),output=backend2.runWebGLProgram(program,[input2],"int32");return{dataId:output.dataId,shape:output.shape,dtype:output.dtype}}function cast4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity3({inputs:{x},backend:backend2});let zerosTensor=zeros(x.shape),floatX=cast4({inputs:{x},backend:backend2,attrs:{dtype:"float32"}}),result=complex3({inputs:{real:floatX,imag:zerosTensor},backend:backend2});return zerosTensor.dispose(),backend2.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real3({inputs:{input:x},backend:backend2}),result=cast4({inputs:{x:realPart},backend:backend2,attrs:{dtype}});return backend2.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity3({inputs:{x},backend:backend2});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32")return int(x,backend2);if(dtype==="bool"){let zerosTensorInfo=backend2.makeTensorInfo([],"bool",util_exports.getTypedArrayFromDType("bool",1)),result=notEqual3({inputs:{a:x,b:zerosTensorInfo},backend:backend2});return backend2.disposeIntermediateTensorInfo(zerosTensorInfo),result}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig2={kernelName:Cast,backendName:"webgl",kernelFunc:cast4};var CEIL="return ceil(x);",ceil3=unaryKernelFunc2({opSnippet:CEIL,packedOpSnippet:CEIL,cpuKernelImpl:ceilImplCPU}),ceilConfig2={kernelName:Ceil,backendName:"webgl",kernelFunc:ceil3};var ClipProgram=class{constructor(aShape){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1900,7 +1900,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         }
         setOutput(result);
       }
-    `}};function shiftedChannels(channels,channel,shift){let channelIdx=channels.indexOf(channel);return channels.map((c,idx)=>idx===channelIdx?`${c} - ${shift}`:c).join()}function imag3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.imag},backend:backend2})}var imagConfig2={kernelName:Imag,backendName:"webgl",kernelFunc:imag3};function concatImpl2(inputs,axis,backend2){let dtype=inputs[0].dtype;if(dtype==="complex64"){let reals=inputs.map(t=>real3({inputs:{input:t},backend:backend2})),imags=inputs.map(t=>imag3({inputs:{input:t},backend:backend2})),realConcated=concatImpl2(reals,axis,backend2),imagConcated=concatImpl2(imags,axis,backend2),result2=complex3({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result2}if(dtype==="string"){let{tensors2D:tensors2D2,outShape:outShape2}=computeTensors2D(inputs,axis,backend2),inputsValShapes=tensors2D2.map(t=>({vals:backend2.texData.get(t.dataId).values,shape:t.shape})),simplyConcat=tensors2D2[0].shape[0]===1,outVals=concatImplCPU(inputsValShapes,outShape2,dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),outInfo=backend2.makeTensorInfo(finalOutShape,dtype,outVals);return tensors2D2.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}if(inputs.length>env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(inputs.length/2),leftSide=concatImpl2(inputs.slice(0,midIndex),axis,backend2),rightSide=concatImpl2(inputs.slice(midIndex),axis,backend2),result2=concatImpl2([leftSide,rightSide],axis,backend2);return backend2.disposeIntermediateTensorInfo(leftSide),backend2.disposeIntermediateTensorInfo(rightSide),result2}if(env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&inputs[0].shape.length>1){let program2=new ConcatPackedProgram(inputs.map(t=>t.shape),axis);return backend2.runWebGLProgram(program2,inputs,dtype)}let{tensors2D,outShape}=computeTensors2D(inputs,axis,backend2),program=new ConcatProgram(tensors2D.map(t=>t.shape)),result=backend2.runWebGLProgram(program,tensors2D,dtype);tensors2D.forEach(r=>backend2.disposeIntermediateTensorInfo(r));let reshapedResult=reshape4({inputs:{x:result},attrs:{shape:outShape},backend:backend2});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}function computeTensors2D(inputs,axis,backend2){let outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis);return{tensors2D:inputs.map(x=>reshape4({inputs:{x},attrs:{shape:[-1,util_exports.sizeFromShape(x.shape.slice(axis))]},backend:backend2})),outShape}}function concat3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);return backend_util_exports.assertParamsConsistent(shapes,$axis),concatImpl2($inputs,$axis,backend2)}var concatConfig2={kernelName:Concat,backendName:"webgl",kernelFunc:concat3};var Conv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivationWeights=!1,hasLeakyreluAlpha=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4,isChannelsLast=convInfo.dataFormat==="channelsLast",rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivationWeights?activationSnippet=`float activation(float a) {
+    `}};function shiftedChannels(channels,channel,shift){let channelIdx=channels.indexOf(channel);return channels.map((c,idx)=>idx===channelIdx?`${c} - ${shift}`:c).join()}function imag3(args){let{inputs,backend:backend2}=args,{input:input2}=inputs,inputData=backend2.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.imag},backend:backend2})}var imagConfig2={kernelName:Imag,backendName:"webgl",kernelFunc:imag3};function concatImpl2(inputs,axis,backend2){let dtype=inputs[0].dtype;if(dtype==="complex64"){let reals=inputs.map(t=>real3({inputs:{input:t},backend:backend2})),imags=inputs.map(t=>imag3({inputs:{input:t},backend:backend2})),realConcated=concatImpl2(reals,axis,backend2),imagConcated=concatImpl2(imags,axis,backend2),result2=complex3({inputs:{real:realConcated,imag:imagConcated},backend:backend2});return reals.forEach(r=>backend2.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend2.disposeIntermediateTensorInfo(i)),backend2.disposeIntermediateTensorInfo(realConcated),backend2.disposeIntermediateTensorInfo(imagConcated),result2}if(dtype==="string"){let{tensors2D:tensors2D2,outShape:outShape2}=computeTensors2D(inputs,axis,backend2),inputsValShapes=tensors2D2.map(t=>({vals:backend2.readSync(t.dataId),shape:t.shape})),simplyConcat=tensors2D2[0].shape[0]===1,outVals=concatImplCPU(inputsValShapes,outShape2,dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),outInfo=backend2.makeTensorInfo(finalOutShape,dtype,outVals);return tensors2D2.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outInfo}if(inputs.length>env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(inputs.length/2),leftSide=concatImpl2(inputs.slice(0,midIndex),axis,backend2),rightSide=concatImpl2(inputs.slice(midIndex),axis,backend2),result2=concatImpl2([leftSide,rightSide],axis,backend2);return backend2.disposeIntermediateTensorInfo(leftSide),backend2.disposeIntermediateTensorInfo(rightSide),result2}if(env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&inputs[0].shape.length>1){let program2=new ConcatPackedProgram(inputs.map(t=>t.shape),axis);return backend2.runWebGLProgram(program2,inputs,dtype)}let{tensors2D,outShape}=computeTensors2D(inputs,axis,backend2),program=new ConcatProgram(tensors2D.map(t=>t.shape)),result=backend2.runWebGLProgram(program,tensors2D,dtype);tensors2D.forEach(r=>backend2.disposeIntermediateTensorInfo(r));let reshapedResult=reshape4({inputs:{x:result},attrs:{shape:outShape},backend:backend2});return backend2.disposeIntermediateTensorInfo(result),reshapedResult}function computeTensors2D(inputs,axis,backend2){let outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis);return{tensors2D:inputs.map(x=>reshape4({inputs:{x},attrs:{shape:[-1,util_exports.sizeFromShape(x.shape.slice(axis))]},backend:backend2})),outShape}}function concat3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend2.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend2});let shapes=$inputs.map(t=>t.shape);return backend_util_exports.assertParamsConsistent(shapes,$axis),concatImpl2($inputs,$axis,backend2)}var concatConfig2={kernelName:Concat,backendName:"webgl",kernelFunc:concat3};var Conv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivationWeights=!1,hasLeakyreluAlpha=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4,isChannelsLast=convInfo.dataFormat==="channelsLast",rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivationWeights?activationSnippet=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${activation2}
         }`:hasLeakyreluAlpha?activationSnippet=`float activation(float a) {
@@ -2166,7 +2166,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
 
         ${glsl.output} = result;
       }
-    `}};function conv2dByMatMul({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let xShape=x.shape,xTexData=backend2.texData.get(x.dataId),sharedMatMulDim=convInfo.inChannels,outerShapeX=xShape[0]*xShape[1]*xShape[2],outerShapeFilter=convInfo.outChannels,isChannelsLast=convInfo.dataFormat==="channelsLast",transposeA=!1,transposeB=!1,out,intermediates=[],batchMatMulWillBeUnpacked=(outerShapeX===1||outerShapeFilter===1)&&sharedMatMulDim>MATMUL_SHARED_DIM_THRESHOLD,reshapeWillBeExpensive=xShape[2]%2!==0&&!!xTexData.isPacked;if(batchMatMulWillBeUnpacked||!env().getBool("WEBGL_LAZILY_UNPACK")||!env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!reshapeWillBeExpensive){let targetShape=isChannelsLast?xShape[0]*xShape[1]*xShape[2]:xShape[0]*xShape[2]*xShape[3],xReshaped=reshape4({inputs:{x},backend:backend2,attrs:{shape:[1,targetShape,convInfo.inChannels]}}),filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}}),result=batchMatMulImpl({a:xReshaped,b:filterReshaped,transposeA,transposeB,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});out=reshape4({inputs:{x:result},backend:backend2,attrs:{shape:convInfo.outShape}}),intermediates.push(xReshaped),intermediates.push(filterReshaped),intermediates.push(result)}else{let targetShape=isChannelsLast?xShape[0]*xShape[1]*(xShape[2]+1):xShape[0]*xShape[2]*(xShape[3]+1),xReshaped={dataId:x.dataId,shape:[1,targetShape,convInfo.inChannels],dtype:x.dtype},originalXTexDataShape=xTexData.shape;xTexData.shape=xTexData.shape.slice(),xTexData.shape[xTexData.shape.length-2]++,util_exports.assert(isReshapeFree(xTexData.shape,xReshaped.shape),()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);let filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}});intermediates.push(filterReshaped);let pointwiseConv=batchMatMulImpl({a:xReshaped,b:filterReshaped,backend:backend2,transposeA,transposeB,bias,activation:activation2,preluActivationWeights,leakyreluAlpha}),pointwiseConvTexData=backend2.texData.get(pointwiseConv.dataId);util_exports.assert(pointwiseConvTexData.isPacked,()=>"batchMatMul result is expected to be packed"),xTexData.shape=originalXTexDataShape,pointwiseConvTexData.shape=convInfo.outShape,out=identity3({inputs:{x:pointwiseConv},backend:backend2}),out.shape=convInfo.outShape,intermediates.push(pointwiseConv)}for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let{filterWidth,filterHeight,inChannels,outWidth,outHeight,dataFormat}=convInfo,isChannelsLast=dataFormat==="channelsLast",sharedDim=filterWidth*filterHeight*inChannels,numCols=outHeight*outWidth,x2ColShape=[sharedDim,numCols],transposeA=!0,transposeB=!1,intermediates=[],xSqueezed=reshape4({inputs:{x},backend:backend2,attrs:{shape:x.shape.slice(1)}}),w2Row=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,sharedDim,util_exports.sizeFromShape(filter.shape)/sharedDim]}});intermediates.push(xSqueezed),intermediates.push(w2Row);let im2ColProgram=new Im2ColPackedProgram(x2ColShape,xSqueezed.shape,convInfo),im2Col=backend2.runWebGLProgram(im2ColProgram,[xSqueezed],"float32"),im2ColReshaped=reshape4({inputs:{x:im2Col},backend:backend2,attrs:{shape:[1,x2ColShape[0],x2ColShape[1]]}});intermediates.push(im2Col),intermediates.push(im2ColReshaped);let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,matmulProgram=new MatMulPackedProgram(im2ColReshaped.shape,w2Row.shape,[1,numCols,convInfo.outChannels],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs=[im2ColReshaped,w2Row];if(bias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let product=backend2.runWebGLProgram(matmulProgram,inputs,"float32"),outShape=isChannelsLast?[1,outHeight,outWidth,convInfo.outChannels]:[1,convInfo.outChannels,outHeight,outWidth],out=reshape4({inputs:{x:product},backend:backend2,attrs:{shape:outShape}});intermediates.push(product);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2d4(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out;if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2});else{let program=new Conv2DProgram(convInfo);out=backend2.runWebGLProgram(program,[x,filter],"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return backend2.disposeIntermediateTensorInfo(out),outReshaped}var conv2DConfig2={kernelName:Conv2D,backendName:"webgl",kernelFunc:conv2d4};var Conv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast";this.userCode=`
+    `}};function conv2dByMatMul({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let xShape=x.shape,xTexData=backend2.texData.get(x.dataId),sharedMatMulDim=convInfo.inChannels,outerShapeX=xShape[0]*xShape[1]*xShape[2],outerShapeFilter=convInfo.outChannels,isChannelsLast=convInfo.dataFormat==="channelsLast",transposeA=!1,transposeB=!1,out,intermediates=[],batchMatMulWillBeUnpacked=(outerShapeX===1||outerShapeFilter===1)&&sharedMatMulDim>MATMUL_SHARED_DIM_THRESHOLD,reshapeWillBeExpensive=xShape[2]%2!=0&&!!xTexData.isPacked;if(batchMatMulWillBeUnpacked||!env().getBool("WEBGL_LAZILY_UNPACK")||!env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!reshapeWillBeExpensive){let targetShape=isChannelsLast?xShape[0]*xShape[1]*xShape[2]:xShape[0]*xShape[2]*xShape[3],xReshaped=reshape4({inputs:{x},backend:backend2,attrs:{shape:[1,targetShape,convInfo.inChannels]}}),filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}}),result=batchMatMulImpl({a:xReshaped,b:filterReshaped,transposeA,transposeB,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});out=reshape4({inputs:{x:result},backend:backend2,attrs:{shape:convInfo.outShape}}),intermediates.push(xReshaped),intermediates.push(filterReshaped),intermediates.push(result)}else{let targetShape=isChannelsLast?xShape[0]*xShape[1]*(xShape[2]+1):xShape[0]*xShape[2]*(xShape[3]+1),xReshaped={dataId:x.dataId,shape:[1,targetShape,convInfo.inChannels],dtype:x.dtype},originalXTexDataShape=xTexData.shape;xTexData.shape=xTexData.shape.slice(),xTexData.shape[xTexData.shape.length-2]++,util_exports.assert(isReshapeFree(xTexData.shape,xReshaped.shape),()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);let filterReshaped=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,convInfo.inChannels,convInfo.outChannels]}});intermediates.push(filterReshaped);let pointwiseConv=batchMatMulImpl({a:xReshaped,b:filterReshaped,backend:backend2,transposeA,transposeB,bias,activation:activation2,preluActivationWeights,leakyreluAlpha}),pointwiseConvTexData=backend2.texData.get(pointwiseConv.dataId);util_exports.assert(pointwiseConvTexData.isPacked,()=>"batchMatMul result is expected to be packed"),xTexData.shape=originalXTexDataShape,pointwiseConvTexData.shape=convInfo.outShape,out=identity3({inputs:{x:pointwiseConv},backend:backend2}),out.shape=convInfo.outShape,intermediates.push(pointwiseConv)}for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias=null,preluActivationWeights=null,leakyreluAlpha=0,activation:activation2=null}){let{filterWidth,filterHeight,inChannels,outWidth,outHeight,dataFormat}=convInfo,isChannelsLast=dataFormat==="channelsLast",sharedDim=filterWidth*filterHeight*inChannels,numCols=outHeight*outWidth,x2ColShape=[sharedDim,numCols],transposeA=!0,transposeB=!1,intermediates=[],xSqueezed=reshape4({inputs:{x},backend:backend2,attrs:{shape:x.shape.slice(1)}}),w2Row=reshape4({inputs:{x:filter},backend:backend2,attrs:{shape:[1,sharedDim,util_exports.sizeFromShape(filter.shape)/sharedDim]}});intermediates.push(xSqueezed),intermediates.push(w2Row);let im2ColProgram=new Im2ColPackedProgram(x2ColShape,xSqueezed.shape,convInfo),im2Col=backend2.runWebGLProgram(im2ColProgram,[xSqueezed],"float32"),im2ColReshaped=reshape4({inputs:{x:im2Col},backend:backend2,attrs:{shape:[1,x2ColShape[0],x2ColShape[1]]}});intermediates.push(im2Col),intermediates.push(im2ColReshaped);let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,matmulProgram=new MatMulPackedProgram(im2ColReshaped.shape,w2Row.shape,[1,numCols,convInfo.outChannels],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs=[im2ColReshaped,w2Row];if(bias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let product=backend2.runWebGLProgram(matmulProgram,inputs,"float32"),outShape=isChannelsLast?[1,outHeight,outWidth,convInfo.outChannels]:[1,convInfo.outChannels,outHeight,outWidth],out=reshape4({inputs:{x:product},backend:backend2,attrs:{shape:outShape}});intermediates.push(product);for(let i of intermediates)backend2.disposeIntermediateTensorInfo(i);return out}function conv2d4(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out;if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2});else{let program=new Conv2DProgram(convInfo);out=backend2.runWebGLProgram(program,[x,filter],"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return backend2.disposeIntermediateTensorInfo(out),outReshaped}var conv2DConfig2={kernelName:Conv2D,backendName:"webgl",kernelFunc:conv2d4};var Conv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2426,7 +2426,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
           setOutput(newValue);
         }
       }
-    `}};var cropAndResize3=args=>{let{inputs,backend:backend2,attrs}=args,{image:image3,boxes,boxInd}=inputs,{cropSize,method,extrapolationValue}=attrs,program=new CropAndResizeProgram(image3.shape,boxes.shape,cropSize,method,extrapolationValue);return backend2.runWebGLProgram(program,[image3,boxes,boxInd],"float32")},cropAndResizeConfig2={kernelName:CropAndResize,backendName:"webgl",kernelFunc:cropAndResize3},CumSumProgram=class{constructor(shape,exclusive,reverse5){this.variableNames=["x"],this.outputShape=shape;let rank=shape.length,val=exclusive?"0.0":`getX(${getCoords2(rank,"coords")})`,length=shape[shape.length-1],condition="",idxString="";exclusive?(condition=reverse5?`end != ${length-1}`:"end != 0",idxString=reverse5?"end + 1":"end - 1"):(condition=reverse5?`end + pow2 < ${length}`:"end >= pow2",idxString=reverse5?"end + pow2":"end - pow2"),this.userCode=`
+    `}};var cropAndResize3=args=>{let{inputs,backend:backend2,attrs}=args,{image:image3,boxes,boxInd}=inputs,{cropSize,method,extrapolationValue}=attrs,program=new CropAndResizeProgram(image3.shape,boxes.shape,cropSize,method,extrapolationValue);return backend2.runWebGLProgram(program,[image3,boxes,boxInd],"float32")},cropAndResizeConfig2={kernelName:CropAndResize,backendName:"webgl",kernelFunc:cropAndResize3};var CumSumProgram=class{constructor(shape,exclusive,reverse5){this.variableNames=["x"],this.outputShape=shape;let rank=shape.length,val=exclusive?"0.0":`getX(${getCoords2(rank,"coords")})`,length=shape[shape.length-1],condition="",idxString="";exclusive?(condition=reverse5?`end != ${length-1}`:"end != 0",idxString=reverse5?"end + 1":"end - 1"):(condition=reverse5?`end + pow2 < ${length}`:"end >= pow2",idxString=reverse5?"end + pow2":"end - pow2"),this.userCode=`
       uniform float index;
       void main() {
         ${getCoordsDataType(rank)} coords = getOutputCoords();
@@ -2440,7 +2440,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(index){return(gpgpu,webGLProgram)=>{this.index==null&&(this.index=gpgpu.getUniformLocation(webGLProgram,"index")),gpgpu.gl.uniform1f(this.index,index)}}};function getCoords2(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.x, ${name}.y`;if(rank===3)return`${name}.x, ${name}.y, ${name}.z`;if(rank===4)return`${name}.x, ${name}.y, ${name}.z, ${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function getFinalCoord(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.y`;if(rank===3)return`${name}.z`;if(rank===4)return`${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function cumsum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];if(permutedAxis!==xRank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.shape.length-1} but got axis=${axis}`);let size=x.shape[permutedAxis],result=identity3({inputs:{x:permutedX},backend:backend2});for(let i=0;i<=Math.ceil(Math.log2(size))-1;i++){let program=new CumSumProgram(permutedX.shape,!1,reverse5),customSetup=program.getCustomSetupFunc(i),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype,customSetup),backend2.disposeIntermediateTensorInfo(prevResult)}if(exclusive){let program=new CumSumProgram(permutedX.shape,exclusive,reverse5),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype),backend2.disposeIntermediateTensorInfo(prevResult)}if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose3({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo(permutedX),reverseTransposedResult}return result}var cumsumConfig2={kernelName:Cumsum,backendName:"webgl",kernelFunc:cumsum3};function denseBincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.texData.get(x.dataId).values,weightsVals=backend2.texData.get(weights.dataId).values,outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImplCPU(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig2={kernelName:DenseBincount,backendName:"webgl",kernelFunc:denseBincount3};var DepthToSpaceProgram=class{constructor(outputShape,blockSize,dataFormat){this.variableNames=["x"],this.outputShape=[],this.outputShape=outputShape,this.blockSize=blockSize,this.dataFormat=dataFormat,this.userCode=`
+    `}getCustomSetupFunc(index){return(gpgpu,webGLProgram)=>{this.index==null&&(this.index=gpgpu.getUniformLocation(webGLProgram,"index")),gpgpu.gl.uniform1f(this.index,index)}}};function getCoords2(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.x, ${name}.y`;if(rank===3)return`${name}.x, ${name}.y, ${name}.z`;if(rank===4)return`${name}.x, ${name}.y, ${name}.z, ${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function getFinalCoord(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.y`;if(rank===3)return`${name}.z`;if(rank===4)return`${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function cumsum3(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];if(permutedAxis!==xRank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.shape.length-1} but got axis=${axis}`);let size=x.shape[permutedAxis],result=identity3({inputs:{x:permutedX},backend:backend2});for(let i=0;i<=Math.ceil(Math.log2(size))-1;i++){let program=new CumSumProgram(permutedX.shape,!1,reverse5),customSetup=program.getCustomSetupFunc(i),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype,customSetup),backend2.disposeIntermediateTensorInfo(prevResult)}if(exclusive){let program=new CumSumProgram(permutedX.shape,exclusive,reverse5),prevResult=result;result=backend2.runWebGLProgram(program,[result],result.dtype),backend2.disposeIntermediateTensorInfo(prevResult)}if(permutation!=null){let reversePermutation=backend_util_exports.getUndoAxesPermutation(permutation),reverseTransposedResult=transpose3({inputs:{x:result},backend:backend2,attrs:{perm:reversePermutation}});return backend2.disposeIntermediateTensorInfo(result),backend2.disposeIntermediateTensorInfo(permutedX),reverseTransposedResult}return result}var cumsumConfig2={kernelName:Cumsum,backendName:"webgl",kernelFunc:cumsum3};function denseBincount3(args){let{inputs,backend:backend2,attrs}=args,{x,weights}=inputs,{size,binaryOutput}=attrs;if(x.shape.length===1){let xVals=backend2.readSync(x.dataId),weightsVals=backend2.readSync(weights.dataId),outVals=bincountImplCPU(xVals,weightsVals,weights.dtype,weights.shape,size);return backend2.makeTensorInfo([size],weights.dtype,outVals)}else if(x.shape.length===2){let xBuf=backend2.bufferSync(x),weightsBuf=backend2.bufferSync(weights),outBuf=bincountReduceImplCPU(xBuf,weightsBuf,size,binaryOutput);return backend2.makeTensorInfo(outBuf.shape,weights.dtype,outBuf.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`)}var denseBincountConfig2={kernelName:DenseBincount,backendName:"webgl",kernelFunc:denseBincount3};var DepthToSpaceProgram=class{constructor(outputShape,blockSize,dataFormat){this.variableNames=["x"],this.outputShape=[],this.outputShape=outputShape,this.blockSize=blockSize,this.dataFormat=dataFormat,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2521,7 +2521,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
           vec4 xR${r}C${c} = vec4(0.);`;for(let r=0;r<filterHeight;r++)for(let texelC=0;texelC<texelsAcross;texelC++){let c=texelC*2;if(mainLoop+=`
           xR = xRCorner + ${r*dilationHeight};
           xC = xCCorner + ${c*dilationWidth};
-        `,strideWidth===1){if(c<filterWidth&&(padLeft%2===1?mainLoop+=`
+        `,strideWidth===1){if(c<filterWidth&&(padLeft%2==1?mainLoop+=`
                 xCOffset = xC + 1;
                 if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
                   xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
@@ -2557,7 +2557,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
                 }
 
                 xR${r}C${c} = xTexelR${r}C${c};
-              `,c+1<filterWidth)){let nextTexelOffset=padLeft%2===0?util_exports.nearestLargerEven(dilationWidth):dilationWidth;dilationWidth%2===0&&padLeft%2===1||dilationWidth%2!==0&&padLeft%2!==1?(mainLoop+=`
+              `,c+1<filterWidth)){let nextTexelOffset=padLeft%2==0?util_exports.nearestLargerEven(dilationWidth):dilationWidth;dilationWidth%2==0&&padLeft%2==1||dilationWidth%2!=0&&padLeft%2!=1?(mainLoop+=`
                   xCOffset = xC + ${padLeft%2} + ${nextTexelOffset};
 
                   if(xR >= 0 && xR < ${xNumRows} &&
@@ -2586,7 +2586,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
                   xR${r}C${c+1} = xTexelR${r}C${c+2};
                 `}}else c<filterWidth&&(mainLoop+=`
               if(xR >= 0 && xR < ${xNumRows}) {
-            `,padLeft%2===1?(mainLoop+=`
+            `,padLeft%2==1?(mainLoop+=`
                 xCOffset = xC + 1 - ${strideWidth};
                 if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                   xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
@@ -2668,7 +2668,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
         ${applyActivationSnippet}
         setOutput(result);
       }
-    `}};function depthwiseConv2dNative2(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs,$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),program;return env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1?program=new DepthwiseConvPacked2DProgram(convInfo):program=new DepthwiseConv2DProgram(convInfo),backend2.runWebGLProgram(program,[x,filter],"float32")}var depthwiseConv2dNativeConfig2={kernelName:DepthwiseConv2dNative,backendName:"webgl",kernelFunc:depthwiseConv2dNative2};var DepthwiseConv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
+    `}};function depthwiseConv2dNative2(args){let{inputs,backend:backend2,attrs}=args,{x,filter}=inputs,{strides,pad:pad3,dilations,dimRoundingMode}=attrs,$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),program;return env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels==1?program=new DepthwiseConvPacked2DProgram(convInfo):program=new DepthwiseConv2DProgram(convInfo),backend2.runWebGLProgram(program,[x,filter],"float32")}var depthwiseConv2dNativeConfig2={kernelName:DepthwiseConv2dNative,backendName:"webgl",kernelFunc:depthwiseConv2dNative2};var DepthwiseConv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2961,7 +2961,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,atanh3=unaryKernelFunc2({opSnippet:
 
         ${glsl.output} = result;
       }
-    `}};var fromPixelsConfig={kernelName:FromPixels,backendName:"webgl",kernelFunc:fromPixels2},fromPixels2DContext2;function fromPixels2(args){let{inputs,backend:backend2,attrs}=args,{pixels}=inputs,{numChannels}=attrs,isVideo=typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement,isImage=typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement,[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],texShape=[height,width],outShape=[height,width,numChannels];(isImage||isVideo)&&(fromPixels2DContext2==null&&(fromPixels2DContext2=document.createElement("canvas").getContext("2d")),fromPixels2DContext2.canvas.width=width,fromPixels2DContext2.canvas.height=height,fromPixels2DContext2.drawImage(pixels,0,0,width,height),pixels=fromPixels2DContext2.canvas);let tempPixelHandle=backend2.makeTensorInfo(texShape,"int32");backend2.texData.get(tempPixelHandle.dataId).usage=TextureUsage.PIXELS,backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId),pixels);let program=env().getBool("WEBGL_PACK")?new FromPixelsPackedProgram(outShape):new FromPixelsProgram(outShape),res=backend2.runWebGLProgram(program,[tempPixelHandle],"int32");return backend2.disposeData(tempPixelHandle.dataId),res}function fusedConv2d(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out,intermediates=[];if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else{let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!1):null,program=new Conv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs2=[x,filter];if(bias&&inputs2.push(bias),preluActivationWeights&&inputs2.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs2.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}out=backend2.runWebGLProgram(program,inputs2,"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return intermediates.push(out),intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outReshaped}var fusedConv2DConfig2={kernelName:FusedConv2D,backendName:"webgl",kernelFunc:fusedConv2d};function fusedDepthwiseConv2D2(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,intermediates=[],$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),shouldPackDepthwiseConv=env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1,fusedActivation=activation2?mapActivationToShaderProgram(activation2,shouldPackDepthwiseConv):null,programInputs=[x,filter],hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu";if(hasBias&&programInputs.push(bias),hasPreluActivationWeights&&programInputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));programInputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let program;shouldPackDepthwiseConv?program=new DepthwiseConvPacked2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha):program=new DepthwiseConv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha);let result=backend2.runWebGLProgram(program,programInputs,"float32");return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var fusedDepthwiseConv2DConfig2={kernelName:FusedDepthwiseConv2D,backendName:"webgl",kernelFunc:fusedDepthwiseConv2D2},GatherNDProgram=class{constructor(sliceDim,strides,shape){this.sliceDim=sliceDim,this.strides=strides,this.variableNames=["x","indices"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),strideString=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}};var fromPixelsConfig={kernelName:FromPixels,backendName:"webgl",kernelFunc:fromPixels2},fromPixels2DContext2;function fromPixels2(args){let{inputs,backend:backend2,attrs}=args,{pixels}=inputs,{numChannels}=attrs,isVideo=typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement,isImage=typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement,isImageBitmap=typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap,[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],texShape=[height,width],outShape=[height,width,numChannels];(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext2==null&&(fromPixels2DContext2=document.createElement("canvas").getContext("2d")),fromPixels2DContext2.canvas.width=width,fromPixels2DContext2.canvas.height=height,fromPixels2DContext2.drawImage(pixels,0,0,width,height),pixels=fromPixels2DContext2.canvas);let tempPixelHandle=backend2.makeTensorInfo(texShape,"int32");backend2.texData.get(tempPixelHandle.dataId).usage=TextureUsage.PIXELS,backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId),pixels);let program=env().getBool("WEBGL_PACK")?new FromPixelsPackedProgram(outShape):new FromPixelsProgram(outShape),res=backend2.runWebGLProgram(program,[tempPixelHandle],"int32");return backend2.disposeData(tempPixelHandle.dataId),res}function fusedConv2d(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),out,intermediates=[];if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))out=conv2dByMatMul({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)out=conv2dWithIm2Row({x,filter,convInfo,backend:backend2,bias,activation:activation2,preluActivationWeights,leakyreluAlpha});else{let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu",fusedActivation=activation2?mapActivationToShaderProgram(activation2,!1):null,program=new Conv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha),inputs2=[x,filter];if(bias&&inputs2.push(bias),preluActivationWeights&&inputs2.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));inputs2.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}out=backend2.runWebGLProgram(program,inputs2,"float32")}let outReshaped=reshape4({inputs:{x:out},backend:backend2,attrs:{shape:convInfo.outShape}});return intermediates.push(out),intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),outReshaped}var fusedConv2DConfig2={kernelName:FusedConv2D,backendName:"webgl",kernelFunc:fusedConv2d};function fusedDepthwiseConv2D2(args){let{inputs,backend:backend2,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,intermediates=[],$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),shouldPackDepthwiseConv=env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels==1,fusedActivation=activation2?mapActivationToShaderProgram(activation2,shouldPackDepthwiseConv):null,programInputs=[x,filter],hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,hasLeakyreluAlpha=activation2==="leakyrelu";if(hasBias&&programInputs.push(bias),hasPreluActivationWeights&&programInputs.push(preluActivationWeights),hasLeakyreluAlpha){let $leakyreluAlpha=backend2.makeTensorInfo([],"float32",util_exports.createScalarValue(leakyreluAlpha,"float32"));programInputs.push($leakyreluAlpha),intermediates.push($leakyreluAlpha)}let program;shouldPackDepthwiseConv?program=new DepthwiseConvPacked2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha):program=new DepthwiseConv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights,hasLeakyreluAlpha);let result=backend2.runWebGLProgram(program,programInputs,"float32");return intermediates.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var fusedDepthwiseConv2DConfig2={kernelName:FusedDepthwiseConv2D,backendName:"webgl",kernelFunc:fusedDepthwiseConv2D2};var GatherNDProgram=class{constructor(sliceDim,strides,shape){this.sliceDim=sliceDim,this.strides=strides,this.variableNames=["x","indices"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),strideString=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${stridesType} strides = ${stridesType}(${this.strides});
          void main() {
           ${dtype} coords = getOutputCoords();
@@ -3921,7 +3921,7 @@ return a / b;`,DIV_PACKED=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,softplus3=unaryKernelFunc2({opSnippet:SOFTPLUS}),softplusConfig2={kernelName:Softplus,backendName:"webgl",kernelFunc:softplus3};var spaceToBatchND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;util_exports.assert(x.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let toDispose=[],paddedX=padV22({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),reshapedPaddedX=reshape4({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose3({inputs:{x:reshapedPaddedX},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape4({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return toDispose.push(paddedX),toDispose.push(reshapedPaddedX),toDispose.push(paddedXT),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result},spaceToBatchNDConfig2={kernelName:SpaceToBatchND,backendName:"webgl",kernelFunc:spaceToBatchND3};function sparseToDense3(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,program=new ScatterProgram(numUpdates,sliceRank,sparseIndices.shape.length,sparseValues.shape.length,strides,[outputSize,1],sumDupeIndices),res=backend2.runWebGLProgram(program,[sparseValues,sparseIndices,defaultValue],sparseValues.dtype),reshaped=reshape4({inputs:{x:res},backend:backend2,attrs:{shape:outputShape}});return backend2.disposeIntermediateTensorInfo(res),reshaped}var sparseToDenseConfig2={kernelName:SparseToDense,backendName:"webgl",kernelFunc:sparseToDense3};function splitV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),xRank=x.shape.length,begin=new Array(xRank).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[axis]=s;let sliceT=slice3({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[axis]+=s,sliceT})}var splitVConfig2={kernelName:SplitV,backendName:"webgl",kernelFunc:splitV2};var SQRT="return sqrt(x);",sqrt3=unaryKernelFunc2({opSnippet:SQRT}),sqrtConfig2={kernelName:Sqrt,backendName:"webgl",kernelFunc:sqrt3};var SQUARE="return x * x;",square3=unaryKernelFunc2({opSnippet:SQUARE}),squareConfig2={kernelName:Square,backendName:"webgl",kernelFunc:square3};var SQUARED_DIFFERENCE="return (a - b) * (a - b);",squaredDifference3=binaryKernelFunc2({opSnippet:SQUARED_DIFFERENCE,packedOpSnippet:SQUARED_DIFFERENCE}),squaredDifferenceConfig2={kernelName:SquaredDifference,backendName:"webgl",kernelFunc:squaredDifference3};function step3({inputs,attrs,backend:backend2}){let{x}=inputs,opSnippet=CHECK_NAN_SNIPPET+`
+`,softplus3=unaryKernelFunc2({opSnippet:SOFTPLUS}),softplusConfig2={kernelName:Softplus,backendName:"webgl",kernelFunc:softplus3};var spaceToBatchND3=args=>{let{inputs,backend:backend2,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;util_exports.assert(x.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let toDispose=[],paddedX=padV22({inputs:{x},backend:backend2,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),reshapedPaddedX=reshape4({inputs:{x:paddedX},backend:backend2,attrs:{shape:reshapedPaddedShape}}),paddedXT=transpose3({inputs:{x:reshapedPaddedX},backend:backend2,attrs:{perm:permutedReshapedPaddedPermutation}}),result=reshape4({inputs:{x:paddedXT},backend:backend2,attrs:{shape:flattenShape}});return toDispose.push(paddedX),toDispose.push(reshapedPaddedX),toDispose.push(paddedXT),toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result},spaceToBatchNDConfig2={kernelName:SpaceToBatchND,backendName:"webgl",kernelFunc:spaceToBatchND3};function sparseToDense3(args){let{inputs,backend:backend2,attrs}=args,{sparseIndices,sparseValues,defaultValue}=inputs,{outputShape}=attrs,{sliceRank,numUpdates,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,program=new ScatterProgram(numUpdates,sliceRank,sparseIndices.shape.length,sparseValues.shape.length,strides,[outputSize,1],sumDupeIndices),res=backend2.runWebGLProgram(program,[sparseValues,sparseIndices,defaultValue],sparseValues.dtype),reshaped=reshape4({inputs:{x:res},backend:backend2,attrs:{shape:outputShape}});return backend2.disposeIntermediateTensorInfo(res),reshaped}var sparseToDenseConfig2={kernelName:SparseToDense,backendName:"webgl",kernelFunc:sparseToDense3};function splitV2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),xRank=x.shape.length,begin=new Array(xRank).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let sliceSize=[...size];sliceSize[$axis]=s;let sliceT=slice3({inputs:{x},backend:backend2,attrs:{begin,size:sliceSize}});return begin[$axis]+=s,sliceT})}var splitVConfig2={kernelName:SplitV,backendName:"webgl",kernelFunc:splitV2};var SQRT="return sqrt(x);",sqrt3=unaryKernelFunc2({opSnippet:SQRT}),sqrtConfig2={kernelName:Sqrt,backendName:"webgl",kernelFunc:sqrt3};var SQUARE="return x * x;",square3=unaryKernelFunc2({opSnippet:SQUARE}),squareConfig2={kernelName:Square,backendName:"webgl",kernelFunc:square3};var SQUARED_DIFFERENCE="return (a - b) * (a - b);",squaredDifference3=binaryKernelFunc2({opSnippet:SQUARED_DIFFERENCE,packedOpSnippet:SQUARED_DIFFERENCE}),squaredDifferenceConfig2={kernelName:SquaredDifference,backendName:"webgl",kernelFunc:squaredDifference3};function step3({inputs,attrs,backend:backend2}){let{x}=inputs,opSnippet=CHECK_NAN_SNIPPET+`
     return x > 0.0 ? 1.0 : float(${attrs.alpha});
   `,program=new UnaryOpProgram(x.shape,opSnippet);return backend2.runWebGLProgram(program,[x],x.dtype)}var stepConfig2={kernelName:Step,backendName:"webgl",kernelFunc:step3};var StridedSliceProgram=class{constructor(begin,strides,size){this.variableNames=["x"],this.outputShape=size;let rank=size.length,inputDtype=getCoordsDataType(size.length),dtype=getCoordsDataType(size.length),newCoords="";if(rank===1)newCoords="coords * strides + begin";else{let outputAxis=0;newCoords=size.map((_,i)=>(outputAxis++,size.length===1?`coords * strides[${i}] + begin[${i}]`:`coords[${outputAxis-1}] * strides[${i}] + begin[${i}]`)).join(",")}this.userCode=`
       ${inputDtype} begin = ${inputDtype}(${begin});
@@ -3939,7 +3939,7 @@ return a / b;`,DIV_PACKED=`
         ${dtype} resRC = getOutputCoords();
         setOutput(getA(${sourceCoords}));
       }
-    `}};function getSourceCoords3(aShape){let rank=aShape.length;if(rank>5)throw Error(`Tile for rank ${rank} is not yet supported`);if(rank===1)return`imod(resRC, ${aShape[0]})`;let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],sourceCoords=[];for(let i=0;i<aShape.length;i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);return sourceCoords.join()}function tile4(params){let{inputs,backend:backend2,attrs}=params,{x}=inputs,{reps}=attrs;if(x.dtype==="string"){let decodedData=backend2.texData.get(x.dataId).values.map(d=>util_exports.decodeString(d)),buf=buffer(x.shape,x.dtype,decodedData),outBuf=tileImplCPU(buf,reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let program=new TileProgram(x.shape,reps);return backend2.runWebGLProgram(program,[x],x.dtype)}var tileConfig2={kernelName:Tile,backendName:"webgl",kernelFunc:tile4};function topK2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs,xVals=backend2.texData.get(x.dataId).values,[allTopKVals,allTopKIndices]=topKImplCPU(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig2={kernelName:TopK,backendName:"webgl",kernelFunc:topK2};function unique4(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex2(x,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let values=backend2.readSync(x.dataId),{outputValues,outputShape,indices}=uniqueImplCPU(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig2={kernelName:Unique,backendName:"webgl",kernelFunc:unique4};function unpack2(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let x=value,xRank=x.shape.length,num=value.shape[axis],outShape=new Array(xRank-1),outIndex=0;for(let i=0;i<xRank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let toDispose=[],begin=new Array(xRank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let sliced=slice3({inputs:{x},backend:backend2,attrs:{begin,size}}),reshaped=reshape4({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}});res[i]=reshaped,toDispose.push(sliced)}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),res}var unpackConfig2={kernelName:Unpack,backendName:"webgl",kernelFunc:unpack2};var SegmentOpProgram=class{constructor(segOpInfo,segOpType){this.variableNames=["x","segmentIds"];let windowSize=segOpInfo.windowSize,batchSize=segOpInfo.batchSize,inSize=segOpInfo.inSize,numSegments=segOpInfo.numSegments,outSize=numSegments*Math.ceil(inSize/windowSize);this.outputShape=[batchSize,outSize];let initializationValue="0.0",returnValue="sumValue",windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
+    `}};function getSourceCoords3(aShape){let rank=aShape.length;if(rank>5)throw Error(`Tile for rank ${rank} is not yet supported`);if(rank===1)return`imod(resRC, ${aShape[0]})`;let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],sourceCoords=[];for(let i=0;i<aShape.length;i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);return sourceCoords.join()}function tile4(params){let{inputs,backend:backend2,attrs}=params,{x}=inputs,{reps}=attrs;if(x.dtype==="string"){let decodedData=backend2.readSync(x.dataId).map(d=>util_exports.decodeString(d)),buf=buffer(x.shape,x.dtype,decodedData),outBuf=tileImplCPU(buf,reps);return backend2.makeTensorInfo(outBuf.shape,outBuf.dtype,outBuf.values)}let program=new TileProgram(x.shape,reps);return backend2.runWebGLProgram(program,[x],x.dtype)}var tileConfig2={kernelName:Tile,backendName:"webgl",kernelFunc:tile4};function topK2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{k,sorted}=attrs,xVals=backend2.readSync(x.dataId),[allTopKVals,allTopKIndices]=topKImplCPU(xVals,x.shape,x.dtype,k,sorted);return[backend2.makeTensorInfo(allTopKVals.shape,allTopKVals.dtype,allTopKVals.values),backend2.makeTensorInfo(allTopKIndices.shape,allTopKIndices.dtype,allTopKIndices.values)]}var topKConfig2={kernelName:TopK,backendName:"webgl",kernelFunc:topK2};function unique4(args){let{inputs,attrs,backend:backend2}=args,{axis}=attrs,{x}=inputs;assertNotComplex2(x,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let values=backend2.readSync(x.dataId),{outputValues,outputShape,indices}=uniqueImplCPU(values,axis,x.shape,x.dtype);return[backend2.makeTensorInfo(outputShape,x.dtype,outputValues),backend2.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig2={kernelName:Unique,backendName:"webgl",kernelFunc:unique4};function unpack2(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let x=value,xRank=x.shape.length,num=value.shape[axis],outShape=new Array(xRank-1),outIndex=0;for(let i=0;i<xRank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let toDispose=[],begin=new Array(xRank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++){begin[axis]=i;let sliced=slice3({inputs:{x},backend:backend2,attrs:{begin,size}}),reshaped=reshape4({inputs:{x:sliced},backend:backend2,attrs:{shape:outShape}});res[i]=reshaped,toDispose.push(sliced)}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),res}var unpackConfig2={kernelName:Unpack,backendName:"webgl",kernelFunc:unpack2};var SegmentOpProgram=class{constructor(segOpInfo,segOpType){this.variableNames=["x","segmentIds"];let windowSize=segOpInfo.windowSize,batchSize=segOpInfo.batchSize,inSize=segOpInfo.inSize,numSegments=segOpInfo.numSegments,outSize=numSegments*Math.ceil(inSize/windowSize);this.outputShape=[batchSize,outSize];let initializationValue="0.0",returnValue="sumValue",windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
         sumValue += dot(values, segFilter);
     `,checkValueOutOfBounds="";inSize%windowSize>0&&(checkValueOutOfBounds=`
         if (inIdx < 0 || inIdx >= ${inSize}) {
@@ -4045,7 +4045,7 @@ return a / b;`,DIV_PACKED=`
         }
         setOutput(${returnValue});
       }
-    `}};function unsortedSegmentSum3(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape4({inputs:{x:permutedX},backend:backend2,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend2.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range4({backend:backend2,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile4({inputs:{x:rangeInfo},backend:backend2,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape4({inputs:{x:segOpResult},backend:backend2,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend2,attrs:{perm}})}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3};var kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var version7="2.8.2";var version8={"tfjs-core":version,"tfjs-backend-cpu":version5,"tfjs-backend-webgl":version6,"tfjs-data":version4,"tfjs-layers":version2,"tfjs-converter":version3,tfjs:version7};var CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend2){wasmFusedMatMul=backend2.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend2.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend2,inputs}=args,{a,b}=inputs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend2.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend2.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast);var wasmFunc;function setupFunc(backend2){wasmFunc=backend2.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend2}=args,out=backend2.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend2.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity4};var wasmTranspose;function setup2(backend2){wasmTranspose=backend2.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend2,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i<perm.length;i++)perm[i]!==i&&(permIsNoOp=!1);let outShape=computeOutShape4(inputs.x.shape,attrs.perm),x={dataId:inputs.x.dataId,shape:reducedShape,dtype:inputs.x.dtype};if(permIsNoOp){let cloned=identity4({inputs,backend:backend2});return cloned.shape=outShape,cloned}let out=backend2.makeOutput(outShape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,permBytes=new Uint8Array(new Int32Array(perm).buffer),xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmTranspose(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],outId,permBytes,perm.length),out}function computeOutShape4(inShape,perm){let outShape=new Array(inShape.length);for(let i=0;i<outShape.length;i++)outShape[i]=inShape[perm[i]];return outShape}function removeOneSizeDims(shape,perm){let newShape=[],newPerm=[];for(let i=0;i<shape.length;++i)shape[i]!==1&&newShape.push(shape[i]),shape[perm[i]]!==1&&newPerm.push(perm[i]);for(let i=0;i<newPerm.length;++i){let minValIdx=-1;for(let j=0;j<newPerm.length;++j)newPerm[j]>=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend2){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xTransposed=transpose4({inputs:{x},attrs:{perm:permutedAxes},backend:backend2});let xId=backend2.dataIdMap.get(x.dataId).id;backend2.dataIdMap.get(xTransposed.dataId).id!==xId&&(inputWasTransposed=!0)}return{transposed:xTransposed,originalAxes,axes,inputWasTransposed}}var wasmFunc2;function setup3(backend2){wasmFunc2=backend2.wasm.cwrap(ArgMax,null,["number","number","number","number","number"])}function argmax(args){let{backend:backend2,inputs,attrs}=args,{axis}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let outShape=input2.shape.slice(0,-1),out=backend2.makeOutput(outShape,"int32"),outId=backend2.dataIdMap.get(out.dataId).id,outerSize=util_exports.sizeFromShape(out.shape),innerSize=input2.shape[axes[0]];return wasmFunc2(inputId,CppDType[input2.dtype],outerSize,innerSize,outId),inputWasTransposed&&backend2.disposeData(transposed.dataId),out}var argMaxConfig3={kernelName:ArgMax,backendName:"wasm",kernelFunc:argmax,setupFunc:setup3};var wasmAvgPool;function setup4(backend2){wasmAvgPool=backend2.wasm.cwrap(AvgPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function avgPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,channels=convInfo.inChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);if(convInfo.dilationWidth!==1||convInfo.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmAvgPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,strideHeight,strideWidth,channels,outId),out}var avgPoolConfig3={kernelName:AvgPool,backendName:"wasm",setupFunc:setup4,kernelFunc:avgPool4};function reshape5(args){let{inputs,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize);return util_exports.assert(xSize===util_exports.sizeFromShape($shape),()=>`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape5};var wasmBatchMatMul;function setup5(backend2){wasmBatchMatMul=backend2.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape5({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape5({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),a3dId=backend2.dataIdMap.get(a3d.dataId).id,b3dId=backend2.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend2.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast5(args){let{inputs:{x},attrs:{dtype},backend:backend2}=args,out=backend2.makeOutput(x.shape,dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast5};var wasmClip;function setup6(backend2){wasmClip=backend2.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend2}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity4({inputs:{x:$inputs[0]},backend:backend2});let out=backend2.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape5({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend2.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend2.typedArrayFromHeap(input2)),outVals=backend2.typedArrayFromHeap(out);for(let b=0;b<batchDim;b++){let outOffset=b*sumInnerDims;for(let i=0;i<inVals.length;i++){let innerDim=innerDims[i],inOffset=b*innerDim,vals=inVals[i].subarray(inOffset,inOffset+innerDim);outVals.set(vals,outOffset),outOffset+=innerDim}}return out}var concatConfig3={kernelName:Concat,backendName:"wasm",kernelFunc:concat4};var wasmConv2d;function setup7(backend2){wasmConv2d=backend2.wasm.cwrap(Conv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode,dataFormat}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var conv2DConfig3={kernelName:Conv2D,backendName:"wasm",setupFunc:setup7,kernelFunc:conv2d5};var wasmConv2DBackpropInput;function setup8(backend2){wasmConv2DBackpropInput=backend2.wasm.cwrap(Conv2DBackpropInput,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2DBackpropInput4(args){let{backend:backend2,inputs,attrs}=args,{dy,filter}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,inputShape}=attrs,dilations=1,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast",dxStrides=util_exports.computeStrides(convInfo.inShape),dyStrides=util_exports.computeStrides(dy.shape),[fltS0,fltS1,fltS2]=util_exports.computeStrides(filter.shape),xBatchStride=dxStrides[0],xRowStride=isChannelsLast?dxStrides[1]:dxStrides[2],xColStride=isChannelsLast?dxStrides[2]:1,xChannelStride=isChannelsLast?1:dxStrides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1],out=backend2.makeOutput(convInfo.inShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,dyId=backend2.dataIdMap.get(dy.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id;return wasmConv2DBackpropInput(dyId,filterId,batchSize,filterHeight,filterWidth,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,strideHeight,strideWidth,topPad,leftPad,fltS0,fltS1,fltS2,xBatchStride,xRowStride,xColStride,xChannelStride,yBatchStride,yRowStride,yColStride,yChannelStride,outId),out}var conv2DBackpropInputConfig3={kernelName:Conv2DBackpropInput,backendName:"wasm",setupFunc:setup8,kernelFunc:conv2DBackpropInput4};var cosConfig3=createUnaryKernelConfig(Cos);var InterpolationMethod;(function(InterpolationMethod2){InterpolationMethod2[InterpolationMethod2.bilinear=0]="bilinear",InterpolationMethod2[InterpolationMethod2.nearest=1]="nearest"})(InterpolationMethod||(InterpolationMethod={}));var wasmCropAndResize;function setup9(backend2){wasmCropAndResize=backend2.wasm.cwrap(CropAndResize,null,["number","number","number","number","array","number","number","number","number","number"])}function cropAndResize4(args){let{backend:backend2,inputs,attrs}=args,{method,extrapolationValue,cropSize}=attrs,{image:image3,boxes,boxInd}=inputs,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,outShape=[numBoxes,cropHeight,cropWidth,image3.shape[3]],imagesData=backend2.dataIdMap.get(image3.dataId),castedData;image3.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:image3},attrs:{dtype:"float32"}}),imagesData=backend2.dataIdMap.get(castedData.dataId));let imagesId=imagesData.id,boxesId=backend2.dataIdMap.get(boxes.dataId).id,boxIndId=backend2.dataIdMap.get(boxInd.dataId).id,out=backend2.makeOutput(outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,imagesShapeBytes=new Uint8Array(new Int32Array(image3.shape).buffer);return wasmCropAndResize(imagesId,boxesId,boxIndId,numBoxes,imagesShapeBytes,cropHeight,cropWidth,InterpolationMethod[method],extrapolationValue,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var cropAndResizeConfig3={kernelName:CropAndResize,backendName:"wasm",setupFunc:setup9,kernelFunc:cropAndResize4};var wasmCumsum;function setup10(backend2){wasmCumsum=backend2.wasm.cwrap(Cumsum,null,["number","number","number","number","number","number"])}function cumsum4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length;util_exports.assert(x.dtype==="float32"||x.dtype==="int32",()=>`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend2}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend2.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend2.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend2.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend2}),backend2.disposeData(permutedX.dataId),backend2.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4};var wasmDepthToSpace;function setup11(backend2){wasmDepthToSpace=backend2.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend2.makeOutput(outputShape,"float32"),xId=backend2.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4};var wasmDepthwiseConv2d;function setup12(backend2){wasmDepthwiseConv2d=backend2.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d5};var supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool");var expConfig3=createUnaryKernelConfig(Exp);function expandDims5(args){let{inputs,attrs,backend:backend2}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape5({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims5};function fill4(args){let{attrs:{shape,value,dtype},backend:backend2}=args,out=backend2.makeOutput(shape,dtype);return backend2.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4};var wasmFlipLeftRight;function setup13(backend2){wasmFlipLeftRight=backend2.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend2}=args,{image:image3}=inputs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13};var floorConfig3=createUnaryKernelConfig(Floor);var supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3);var wasmBatchNorm;function setup14(backend2){wasmBatchNorm=backend2.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend2,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale2}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,meanId=backend2.dataIdMap.get(mean4.dataId).id,varianceId=backend2.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend2.dataIdMap.get(offset.dataId).id:0,scaleId=scale2!=null?backend2.dataIdMap.get(scale2.dataId).id:0,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm};var wasmFusedConv2d;function setup15(backend2){wasmFusedConv2d=backend2.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2};var wasmFusedDepthwiseConv2d;function setup16(backend2){wasmFusedDepthwiseConv2d=backend2.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d};var wasmGatherNd;function setup17(backend2){wasmGatherNd=backend2.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend2,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend2.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend2.dataIdMap.get(params.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3};var wasmGather;function setup18(backend2){wasmGather=backend2.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend2,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape5({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend2}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape5({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend2}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend2.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend2.dataIdMap.get(flattenX.dataId).id,indicesId=backend2.dataIdMap.get(flattenIndex.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23};var supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool");var supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool");var wasmFunc3;function setupFunc2(backend2){wasmFunc3=backend2.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend2}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4};var supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool");var supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool");var logConfig3=createUnaryKernelConfig(Log);var supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool");var wasmMax;function setup19(backend2){wasmMax=backend2.wasm.cwrap(Max,null,["number, number, number"])}function max5(args){let{backend:backend2,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend2.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max5};var supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9);var wasmMaxPool;function setup20(backend2){wasmMaxPool=backend2.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4};var wasmMean;function setup21(backend2){wasmMean=backend2.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast5({backend:backend2,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend2.dataIdMap.get(castedInput.dataId).id);let out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend2.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3};var wasmMin;function setup22(backend2){wasmMin=backend2.wasm.cwrap(Min,null,["number, number, number"])}function min5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min5};var supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10);var supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11);var negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend2,resOffset){let result=new Int32Array(backend2.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend2.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend2){wasmFunc4=backend2.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);return backend2.wasm._free(pSelectedScores),backend2.wasm._free(pValidOutputs),backend2.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc};var wasmFunc5;function setup24(backend2){wasmFunc5=backend2.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pSelectedScores);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend2.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43};var wasmFunc6;function setup25(backend2){wasmFunc6=backend2.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pValidOutputs);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend2.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2};var supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool");var wasmOneHot;function setup26(backend2){wasmOneHot=backend2.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend2.makeOutput([...indices.shape,depth],"int32"),outId=backend2.dataIdMap.get(out.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims5({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims5({inputs:{input:t},backend:backend2,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend2,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3};var wasmPadV2;function setup27(backend2){wasmPadV2=backend2.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend2,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(outShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27};var supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13);var wasmPrelu;function setup28(backend2){wasmPrelu=backend2.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu5(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,weightsId=backend2.dataIdMap.get(alpha.dataId).id,out=backend2.makeOutput(x.shape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu5};var wasmProd;function setup29(backend2){wasmProd=backend2.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4};var range5=args=>{let{backend:backend2,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend2.makeOutput([values.length],dtype);return backend2.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range5};var supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14);var reluConfig3=createUnaryKernelConfig(Relu);var relu6Config3=createUnaryKernelConfig(Relu6);var wasmResizeBilinear;function setup30(backend2){wasmResizeBilinear=backend2.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend2,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend2.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend2.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4};var wasmReverse;function setup31(backend2){wasmReverse=backend2.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity4({inputs:{x},backend:backend2});let out=backend2.makeOutput(x.shape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape5({inputs:{x:out},attrs:{shape:x.shape},backend:backend2})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31};var wasmRotate;function setup32(backend2){wasmRotate=backend2.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend2,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32};var rsqrtConfig3=createUnaryKernelConfig(Rsqrt);var wasmScatterNd;function setup33(backend2){wasmScatterNd=backend2.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend2,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend2.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend2.dataIdMap.get(indices.dataId).id,updatesId=backend2.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3};var wasmSelect;function setup34(backend2){wasmSelect=backend2.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs,conditionId=backend2.dataIdMap.get(condition.dataId).id,tId=backend2.dataIdMap.get(t.dataId).id,eId=backend2.dataIdMap.get(e.dataId).id,out=backend2.makeOutput(t.shape,t.dtype),outId=backend2.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34};var wasmFunc7;function setup35(backend2){wasmFunc7=backend2.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4};var sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend2}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend2.readSync(x.dataId),out=backend2.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend2.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend2.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend2.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i<endI;i++){let xOffset=i*xStride+beginJ;outVals.set(xVals.subarray(xOffset,xOffset+size[1]),outOffset),outOffset+=size[1]}}function slice3d2(xVals,xStride1,xStride2,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++){let xOffset=i*xStride1+j*xStride2+beginK;outVals.set(xVals.subarray(xOffset,xOffset+size[2]),outOffset),outOffset+=size[2]}}function slice4d2(xVals,xStride1,xStride2,xStride3,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1],endK=beginK+size[2],beginL=begin[3];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++)for(let k=beginK;k<endK;k++){let xOffset=i*xStride1+j*xStride2+k*xStride3+beginL;outVals.set(xVals.subarray(xOffset,xOffset+size[3]),outOffset),outOffset+=size[3]}}var sliceConfig3={kernelName:Slice,backendName:"wasm",kernelFunc:slice4};var wasmFunc8;function setup36(backend2){wasmFunc8=backend2.wasm.cwrap(Softmax,null,["number","number","number","number"])}function softmax5(args){let{backend:backend2,inputs:{logits},attrs:{dim}}=args,xId=backend2.dataIdMap.get(logits.dataId).id,out=backend2.makeOutput(logits.shape,logits.dtype),outId=backend2.dataIdMap.get(out.dataId).id,channels=logits.shape[dim],batch=util_exports.sizeFromShape(logits.shape)/channels;return util_exports.sizeFromShape(out.shape)===0||wasmFunc8(xId,outId,channels,batch),out}var softmaxConfig3={kernelName:Softmax,backendName:"wasm",setupFunc:setup36,kernelFunc:softmax5};function splitV3(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend2});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3};var sqrtConfig3=createUnaryKernelConfig(Sqrt);var squareConfig3=createUnaryKernelConfig(Square);var supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15);var wasmStridedSlice;function setup37(backend2){wasmStridedSlice=backend2.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape5({inputs:{x},attrs:{shape:newShape},backend:backend2}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend2});return reshape5({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend2})}let out=backend2.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend2.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape5({inputs:{x:out},attrs:{shape:outShape},backend:backend2})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4};var supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16);var wasmSum;function setup38(backend2){wasmSum=backend2.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5};var tanhConfig3=createUnaryKernelConfig(Tanh);var wasmTile;function setup39(backend2){wasmTile=backend2.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile5(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[i]*reps[i];let xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),newShapeBytes=new Uint8Array(new Int32Array(newShape).buffer),out=backend2.makeOutput(newShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmTile(xId,xShapeBytes,x.shape.length,newShapeBytes,newShape.length,CppDType[out.dtype],outId),out}var tileConfig3={kernelName:Tile,backendName:"wasm",setupFunc:setup39,kernelFunc:tile5};var wasmTopK;function setup40(backend2){wasmTopK=backend2.wasm.cwrap(TopK,null,["number","array","number","number","number","bool","number","number"])}var topk2=({inputs,backend:backend2,attrs})=>{let{x}=inputs,{k,sorted}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend2.makeOutput(outputShape,x.dtype),outValuesId=backend2.dataIdMap.get(outValues.dataId).id,outIndices=backend2.makeOutput(outputShape,"int32"),outIndicesId=backend2.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i<rank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let outs=new Array(numOutputs),begin=new Array(rank).fill(0),size=value.shape.slice();size[axis]=1;for(let i=0;i<outs.length;i++)begin[axis]=i,outs[i]=slice4({inputs:{x:value},attrs:{begin,size},backend:backend2});return outs.map(({dataId,dtype})=>({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4};var kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule(require_tfjs_backend_wasm());var BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data=this.dataIdMap.get(dataId);this.wasm._free(data.memoryOffset),this.wasm.tfjs.disposeData(data.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||(initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.2";var WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);export{Abs,Acos,Acosh,AdadeltaOptimizer,AdagradOptimizer,AdamOptimizer,AdamaxOptimizer,Add,AddN,All,Any,ArgMax,ArgMin,Asin,Asinh,Atan,Atan2,Atanh,AvgPool,AvgPool3D,AvgPool3DGrad,AvgPoolGrad,BackendWasm,BatchMatMul,BatchToSpaceND,Bincount,BroadcastTo,Callback,CallbackList,Cast,Ceil,ClipByValue,Complex,ComplexAbs,Concat,Conv2D,Conv2DBackpropFilter,Conv2DBackpropInput,Conv3D,Conv3DBackpropFilterV2,Conv3DBackpropInputV2,Cos,Cosh,CropAndResize,Cumsum,CustomCallback,DataStorage,DenseBincount,DepthToSpace,DepthwiseConv2dNative,DepthwiseConv2dNativeBackpropFilter,DepthwiseConv2dNativeBackpropInput,Diag,Dilation2D,Dilation2DBackpropFilter,Dilation2DBackpropInput,ENV,EarlyStopping,Elu,EluGrad,Environment,Equal,Erf,Exp,ExpandDims,Expm1,FFT,Fill,FlipLeftRight,Floor,FloorDiv,FromPixels,FusedBatchNorm,FusedConv2D,FusedDepthwiseConv2D,GatherNd,GatherV2,GraphModel,Greater,GreaterEqual,History,IFFT,Identity,Imag,InputSpec,IsFinite,IsInf,IsNan,KernelBackend,LRN,LRNGrad,LayerVariable,LayersModel,LeakyRelu,Less,LessEqual,LinSpace,Log,Log1p,LogSoftmax,LogicalAnd,LogicalNot,LogicalOr,Max,MaxPool,MaxPool3D,MaxPool3DGrad,MaxPoolGrad,MaxPoolWithArgmax,Maximum,Mean,Min,Minimum,MirrorPad,Mod,MomentumOptimizer,Multinomial,Multiply,Neg,NonMaxSuppressionV3,NonMaxSuppressionV4,NonMaxSuppressionV5,NotEqual,OP_SCOPE_SUFFIX,OneHot,OnesLike,Optimizer,Pack,PadV2,Pool,Pow,Prelu,Prod,RMSPropOptimizer,RNN,Range,Rank,Real,RealDiv,Reciprocal,Reduction,Relu,Relu6,Reshape,ResizeBilinear,ResizeBilinearGrad,ResizeNearestNeighbor,ResizeNearestNeighborGrad,Reverse,RotateWithOffset,Round,Rsqrt,SGDOptimizer,ScatterNd,Select,Selu,Sequential,Sigmoid,Sign,Sin,Sinh,Slice,Softmax,Softplus,SpaceToBatchND,SparseToDense,SplitV,Sqrt,Square,SquaredDifference,Step,StridedSlice,Sub,Sum,SymbolicTensor,Tan,Tanh,Tensor,TensorBuffer,Tile,TopK,Transpose,Unique,Unpack,UnsortedSegmentSum,Variable,ZerosLike,_FusedMatMul,abs,acos,acosh,add2 as add,addN,addStrict,all,any,argMax,argMin,asin,asinh,atan,atan2,atanh,avgPool,avgPool3d,backend,backend_util_exports as backend_util,basicLSTMCell,batchNorm,batchNorm2d,batchNorm3d,batchNorm4d,batchToSpaceND,bincount,booleanMaskAsync,broadcastTo,browser_exports as browser,buffer,callbacks,cast,ceil,clipByValue,clone,complex,concat,concat1d,concat2d,concat3d,concat4d,exports_constraints_exports as constraints,conv1d,conv2d,conv2dTranspose,conv3d,conv3dTranspose,copyRegisteredKernels,cos,cosh,cosineWindow,cumsum,customGrad,dist_exports as data,denseBincount,deprecationWarn,depthToSpace,depthwiseConv2d,deregisterOp,device_util_exports as device_util,diag,dilation2d,disableDeprecationWarnings,dispose,disposeVariables,div,divNoNan,divStrict,dot,dropout,elu,enableDebugMode,enableProdMode,enclosingPowerOfTwo,engine,env,equal,equalStrict,erf,exp,expandDims,expm1,eye,fft,fill,findBackend,findBackendFactory,floor,floorDiv,fused_ops_exports as fused,gather,gatherND,gather_nd_util_exports as gather_util,getBackend,getGradient,getKernel,getKernelsForBackend,grad,grads,greater,greaterEqual,greaterEqualStrict,greaterStrict,ifft,imag,image,inTopKAsync,exports_initializers_exports as initializers,input,io_exports as io,irfft,isFinite2 as isFinite,isInf,isNaN2 as isNaN,keep,kernel_impls_exports as kernel_impls,exports_layers_exports as layers,leakyRelu,less,lessEqual,lessEqualStrict,lessStrict,linalg,linspace,loadGraphModel,loadLayersModel,localResponseNormalization,log,log1p,logSigmoid,logSoftmax,logSumExp,logicalAnd,logicalNot,logicalOr,logicalXor,losses,matMul,math_exports as math,max,maxPool,maxPool3d,maxPoolWithArgmax,maximum,maximumStrict,mean,memory,exports_metrics_exports as metrics,min,minimum,minimumStrict,mirrorPad,mod,modStrict,model,exports_models_exports as models,moments,movingAverage,mul,mulStrict,multiRNNCell,multinomial,neg,nextFrame,norm,notEqual,notEqualStrict,oneHot,ones2 as ones,onesLike,op,outerProduct,pad,pad1d,pad2d,pad3d,pad4d,pool,pow,powStrict,prelu,print2 as print,prod,profile,rand,randomGamma,randomNormal,randomUniform,range,ready,real,reciprocal,registerBackend,registerCallbackConstructor,registerGradient,registerKernel,registerOp,exports_regularizers_exports as regularizers,relu,relu6,removeBackend,reshape,reverse,reverse1d,reverse2d,reverse3d,reverse4d,rfft,round2 as round,rsqrt,scalar,scatterND,scatter_nd_util_exports as scatter_util,selu,separableConv2d,sequential,serialization_exports as serialization,setBackend,setPlatform,setWasmPath,setWasmPaths,setdiff1dAsync,sigmoid,sign,signal,sin,sinh,slice,slice1d,slice2d,slice3d,slice4d,slice_util_exports as slice_util,softmax,softplus,spaceToBatchND,sparseToDense,spectral,split,sqrt,square,squaredDifference,squaredDifferenceStrict,squeeze,stack,step,stridedSlice,sub,subStrict,sum2 as sum,sumOutType,tan,tanh2 as tanh,tensor,tensor1d,tensor2d,tensor3d,tensor4d,tensor5d,tensor6d,tensor_util_exports as tensor_util,test_util_exports as test_util,tidy,tile,time,topk,train,transpose,truncatedNormal,unique,unregisterGradient,unregisterKernel,unsortedSegmentSum,unstack,upcastType,util_exports as util,valueAndGrad,valueAndGrads,variable,variableGrads,version8 as version,version3 as version_converter,version as version_core,version2 as version_layers,version9 as version_wasm,where,whereAsync,zeros,zerosLike};
+    `}};function unsortedSegmentSum3(args){let{inputs,backend:backend2,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend2,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape4({inputs:{x:permutedX},backend:backend2,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend2.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range4({backend:backend2,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile4({inputs:{x:rangeInfo},backend:backend2,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape4({inputs:{x:segOpResult},backend:backend2,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend2,attrs:{perm}})}return toDispose.forEach(t=>backend2.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3};var kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var version7="2.8.3";var version8={"tfjs-core":version,"tfjs-backend-cpu":version5,"tfjs-backend-webgl":version6,"tfjs-data":version4,"tfjs-layers":version2,"tfjs-converter":version3,tfjs:version7};var CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend2){wasmFusedMatMul=backend2.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend2,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend2.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend2){wasmFunc9=backend2.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend2,inputs}=args,{a,b}=inputs,aId=backend2.dataIdMap.get(a.dataId).id,bId=backend2.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend2.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend2.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast);var wasmFunc;function setupFunc(backend2){wasmFunc=backend2.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend2}=args,out=backend2.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend2.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity4};var wasmTranspose;function setup2(backend2){wasmTranspose=backend2.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend2,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i<perm.length;i++)perm[i]!==i&&(permIsNoOp=!1);let outShape=computeOutShape4(inputs.x.shape,attrs.perm),x={dataId:inputs.x.dataId,shape:reducedShape,dtype:inputs.x.dtype};if(permIsNoOp){let cloned=identity4({inputs,backend:backend2});return cloned.shape=outShape,cloned}let out=backend2.makeOutput(outShape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,permBytes=new Uint8Array(new Int32Array(perm).buffer),xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmTranspose(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],outId,permBytes,perm.length),out}function computeOutShape4(inShape,perm){let outShape=new Array(inShape.length);for(let i=0;i<outShape.length;i++)outShape[i]=inShape[perm[i]];return outShape}function removeOneSizeDims(shape,perm){let newShape=[],newPerm=[];for(let i=0;i<shape.length;++i)shape[i]!==1&&newShape.push(shape[i]),shape[perm[i]]!==1&&newPerm.push(perm[i]);for(let i=0;i<newPerm.length;++i){let minValIdx=-1;for(let j=0;j<newPerm.length;++j)newPerm[j]>=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend2){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xTransposed=transpose4({inputs:{x},attrs:{perm:permutedAxes},backend:backend2});let xId=backend2.dataIdMap.get(x.dataId).id;backend2.dataIdMap.get(xTransposed.dataId).id!==xId&&(inputWasTransposed=!0)}return{transposed:xTransposed,originalAxes,axes,inputWasTransposed}}var wasmFunc2;function setup3(backend2){wasmFunc2=backend2.wasm.cwrap(ArgMax,null,["number","number","number","number","number"])}function argmax(args){let{backend:backend2,inputs,attrs}=args,{axis}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let outShape=input2.shape.slice(0,-1),out=backend2.makeOutput(outShape,"int32"),outId=backend2.dataIdMap.get(out.dataId).id,outerSize=util_exports.sizeFromShape(out.shape),innerSize=input2.shape[axes[0]];return wasmFunc2(inputId,CppDType[input2.dtype],outerSize,innerSize,outId),inputWasTransposed&&backend2.disposeData(transposed.dataId),out}var argMaxConfig3={kernelName:ArgMax,backendName:"wasm",kernelFunc:argmax,setupFunc:setup3};var wasmAvgPool;function setup4(backend2){wasmAvgPool=backend2.wasm.cwrap(AvgPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function avgPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,channels=convInfo.inChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);if(convInfo.dilationWidth!==1||convInfo.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmAvgPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,strideHeight,strideWidth,channels,outId),out}var avgPoolConfig3={kernelName:AvgPool,backendName:"wasm",setupFunc:setup4,kernelFunc:avgPool4};function reshape5(args){let{inputs,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize);return util_exports.assert(xSize===util_exports.sizeFromShape($shape),()=>`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape5};var wasmBatchMatMul;function setup5(backend2){wasmBatchMatMul=backend2.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend2,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape5({inputs:{x:a},backend:backend2,attrs:{shape:a3dShape}}),b3d=reshape5({inputs:{x:b},backend:backend2,attrs:{shape:b3dShape}}),a3dId=backend2.dataIdMap.get(a3d.dataId).id,b3dId=backend2.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend2.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend2.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast5(args){let{inputs:{x},attrs:{dtype},backend:backend2}=args,out=backend2.makeOutput(x.shape,dtype),inVals=backend2.typedArrayFromHeap(x);return backend2.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast5};var wasmClip;function setup6(backend2){wasmClip=backend2.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend2}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity4({inputs:{x:$inputs[0]},backend:backend2});let out=backend2.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape5({inputs:{x:t},backend:backend2,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend2.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend2.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend2.typedArrayFromHeap(input2)),outVals=backend2.typedArrayFromHeap(out);for(let b=0;b<batchDim;b++){let outOffset=b*sumInnerDims;for(let i=0;i<inVals.length;i++){let innerDim=innerDims[i],inOffset=b*innerDim,vals=inVals[i].subarray(inOffset,inOffset+innerDim);outVals.set(vals,outOffset),outOffset+=innerDim}}return out}var concatConfig3={kernelName:Concat,backendName:"wasm",kernelFunc:concat4};var wasmConv2d;function setup7(backend2){wasmConv2d=backend2.wasm.cwrap(Conv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode,dataFormat}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var conv2DConfig3={kernelName:Conv2D,backendName:"wasm",setupFunc:setup7,kernelFunc:conv2d5};var wasmConv2DBackpropInput;function setup8(backend2){wasmConv2DBackpropInput=backend2.wasm.cwrap(Conv2DBackpropInput,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2DBackpropInput4(args){let{backend:backend2,inputs,attrs}=args,{dy,filter}=inputs,{strides,pad:pad3,dataFormat,dimRoundingMode,inputShape}=attrs,dilations=1,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad3,dimRoundingMode,!1,$dataFormat),{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast",dxStrides=util_exports.computeStrides(convInfo.inShape),dyStrides=util_exports.computeStrides(dy.shape),[fltS0,fltS1,fltS2]=util_exports.computeStrides(filter.shape),xBatchStride=dxStrides[0],xRowStride=isChannelsLast?dxStrides[1]:dxStrides[2],xColStride=isChannelsLast?dxStrides[2]:1,xChannelStride=isChannelsLast?1:dxStrides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1],out=backend2.makeOutput(convInfo.inShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,dyId=backend2.dataIdMap.get(dy.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id;return wasmConv2DBackpropInput(dyId,filterId,batchSize,filterHeight,filterWidth,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,strideHeight,strideWidth,topPad,leftPad,fltS0,fltS1,fltS2,xBatchStride,xRowStride,xColStride,xChannelStride,yBatchStride,yRowStride,yColStride,yChannelStride,outId),out}var conv2DBackpropInputConfig3={kernelName:Conv2DBackpropInput,backendName:"wasm",setupFunc:setup8,kernelFunc:conv2DBackpropInput4};var cosConfig3=createUnaryKernelConfig(Cos);var InterpolationMethod;(function(InterpolationMethod2){InterpolationMethod2[InterpolationMethod2.bilinear=0]="bilinear",InterpolationMethod2[InterpolationMethod2.nearest=1]="nearest"})(InterpolationMethod||(InterpolationMethod={}));var wasmCropAndResize;function setup9(backend2){wasmCropAndResize=backend2.wasm.cwrap(CropAndResize,null,["number","number","number","number","array","number","number","number","number","number"])}function cropAndResize4(args){let{backend:backend2,inputs,attrs}=args,{method,extrapolationValue,cropSize}=attrs,{image:image3,boxes,boxInd}=inputs,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,outShape=[numBoxes,cropHeight,cropWidth,image3.shape[3]],imagesData=backend2.dataIdMap.get(image3.dataId),castedData;image3.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:image3},attrs:{dtype:"float32"}}),imagesData=backend2.dataIdMap.get(castedData.dataId));let imagesId=imagesData.id,boxesId=backend2.dataIdMap.get(boxes.dataId).id,boxIndId=backend2.dataIdMap.get(boxInd.dataId).id,out=backend2.makeOutput(outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,imagesShapeBytes=new Uint8Array(new Int32Array(image3.shape).buffer);return wasmCropAndResize(imagesId,boxesId,boxIndId,numBoxes,imagesShapeBytes,cropHeight,cropWidth,InterpolationMethod[method],extrapolationValue,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var cropAndResizeConfig3={kernelName:CropAndResize,backendName:"wasm",setupFunc:setup9,kernelFunc:cropAndResize4};var wasmCumsum;function setup10(backend2){wasmCumsum=backend2.wasm.cwrap(Cumsum,null,["number","number","number","number","number","number"])}function cumsum4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse5}=attrs,xRank=x.shape.length;util_exports.assert(x.dtype==="float32"||x.dtype==="int32",()=>`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend2}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend2.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend2.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend2.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend2}),backend2.disposeData(permutedX.dataId),backend2.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4};var wasmDepthToSpace;function setup11(backend2){wasmDepthToSpace=backend2.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend2.makeOutput(outputShape,"float32"),xId=backend2.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4};var wasmDepthwiseConv2d;function setup12(backend2){wasmDepthwiseConv2d=backend2.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d5(args){let{inputs,attrs,backend:backend2}=args,{x,filter}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d5};var supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool");var expConfig3=createUnaryKernelConfig(Exp);function expandDims5(args){let{inputs,attrs,backend:backend2}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape5({inputs:{x:input2},backend:backend2,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims5};function fill4(args){let{attrs:{shape,value,dtype},backend:backend2}=args,out=backend2.makeOutput(shape,dtype);return backend2.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4};var wasmFlipLeftRight;function setup13(backend2){wasmFlipLeftRight=backend2.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend2}=args,{image:image3}=inputs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13};var floorConfig3=createUnaryKernelConfig(Floor);var supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3);var wasmBatchNorm;function setup14(backend2){wasmBatchNorm=backend2.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend2,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale2}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,meanId=backend2.dataIdMap.get(mean4.dataId).id,varianceId=backend2.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend2.dataIdMap.get(offset.dataId).id:0,scaleId=scale2!=null?backend2.dataIdMap.get(scale2.dataId).id:0,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm};var wasmFusedConv2d;function setup15(backend2){wasmFusedConv2d=backend2.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2};var wasmFusedDepthwiseConv2d;function setup16(backend2){wasmFusedDepthwiseConv2d=backend2.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend2}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend2.dataIdMap.get(x.dataId).id,filterId=backend2.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend2.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend2.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d};var wasmGatherNd;function setup17(backend2){wasmGatherNd=backend2.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend2,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend2.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend2.dataIdMap.get(params.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3};var wasmGather;function setup18(backend2){wasmGather=backend2.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend2,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape5({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend2}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape5({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend2}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend2.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend2.dataIdMap.get(flattenX.dataId).id,indicesId=backend2.dataIdMap.get(flattenIndex.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23};var supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool");var supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool");var wasmFunc3;function setupFunc2(backend2){wasmFunc3=backend2.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend2}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4};var supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool");var supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool");var logConfig3=createUnaryKernelConfig(Log);var supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool");var wasmMax;function setup19(backend2){wasmMax=backend2.wasm.cwrap(Max,null,["number, number, number"])}function max5(args){let{backend:backend2,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend2.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max5};var supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9);var wasmMaxPool;function setup20(backend2){wasmMaxPool=backend2.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend2}=args,x=inputs.x,xId=backend2.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend2.makeOutput(convInfo.outShape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4};var wasmMean;function setup21(backend2){wasmMean=backend2.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast5({backend:backend2,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend2.dataIdMap.get(castedInput.dataId).id);let out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend2.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3};var wasmMin;function setup22(backend2){wasmMin=backend2.wasm.cwrap(Min,null,["number, number, number"])}function min5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2);if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min5};var supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10);var supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11);var negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend2,resOffset){let result=new Int32Array(backend2.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend2.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend2){wasmFunc4=backend2.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);return backend2.wasm._free(pSelectedScores),backend2.wasm._free(pValidOutputs),backend2.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc};var wasmFunc5;function setup24(backend2){wasmFunc5=backend2.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pSelectedScores);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend2.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43};var wasmFunc6;function setup25(backend2){wasmFunc6=backend2.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend2,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend2.dataIdMap.get(boxes.dataId).id,scoresId=backend2.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend2,resOffset);backend2.wasm._free(pValidOutputs);let selectedIndicesTensor=backend2.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend2.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2};var supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool");var wasmOneHot;function setup26(backend2){wasmOneHot=backend2.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend2,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend2.makeOutput([...indices.shape,depth],"int32"),outId=backend2.dataIdMap.get(out.dataId).id,indicesId=backend2.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend2,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims5({inputs:{input:inputs[0]},backend:backend2,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims5({inputs:{input:t},backend:backend2,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend2,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3};var wasmPadV2;function setup27(backend2){wasmPadV2=backend2.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend2,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(outShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27};var supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13);var wasmPrelu;function setup28(backend2){wasmPrelu=backend2.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu5(args){let{inputs,backend:backend2}=args,{x,alpha}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,weightsId=backend2.dataIdMap.get(alpha.dataId).id,out=backend2.makeOutput(x.shape,"float32"),outId=backend2.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu5};var wasmProd;function setup29(backend2){wasmProd=backend2.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4};var range5=args=>{let{backend:backend2,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend2.makeOutput([values.length],dtype);return backend2.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range5};var supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14);var reluConfig3=createUnaryKernelConfig(Relu);var relu6Config3=createUnaryKernelConfig(Relu6);var wasmResizeBilinear;function setup30(backend2){wasmResizeBilinear=backend2.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend2,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend2.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast5({backend:backend2,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend2.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend2.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend2.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend2.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4};var wasmReverse;function setup31(backend2){wasmReverse=backend2.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity4({inputs:{x},backend:backend2});let out=backend2.makeOutput(x.shape,x.dtype),xId=backend2.dataIdMap.get(x.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape5({inputs:{x:out},attrs:{shape:x.shape},backend:backend2})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31};var wasmRotate;function setup32(backend2){wasmRotate=backend2.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend2,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend2.makeOutput(image3.shape,image3.dtype),imageId=backend2.dataIdMap.get(image3.dataId).id,outId=backend2.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32};var roundConfig3=createUnaryKernelConfig(Round);var rsqrtConfig3=createUnaryKernelConfig(Rsqrt);var wasmScatterNd;function setup33(backend2){wasmScatterNd=backend2.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend2,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend2.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend2.dataIdMap.get(indices.dataId).id,updatesId=backend2.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend2.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3};var wasmSelect;function setup34(backend2){wasmSelect=backend2.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend2}=args,{condition,t,e}=inputs,conditionId=backend2.dataIdMap.get(condition.dataId).id,tId=backend2.dataIdMap.get(t.dataId).id,eId=backend2.dataIdMap.get(e.dataId).id,out=backend2.makeOutput(t.shape,t.dtype),outId=backend2.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34};var wasmFunc7;function setup35(backend2){wasmFunc7=backend2.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend2,inputs:{x}}=args,xId=backend2.dataIdMap.get(x.dataId).id,out=backend2.makeOutput(x.shape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4};var sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend2}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend2.readSync(x.dataId),out=backend2.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend2.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend2.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend2.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i<endI;i++){let xOffset=i*xStride+beginJ;outVals.set(xVals.subarray(xOffset,xOffset+size[1]),outOffset),outOffset+=size[1]}}function slice3d2(xVals,xStride1,xStride2,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++){let xOffset=i*xStride1+j*xStride2+beginK;outVals.set(xVals.subarray(xOffset,xOffset+size[2]),outOffset),outOffset+=size[2]}}function slice4d2(xVals,xStride1,xStride2,xStride3,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1],endK=beginK+size[2],beginL=begin[3];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++)for(let k=beginK;k<endK;k++){let xOffset=i*xStride1+j*xStride2+k*xStride3+beginL;outVals.set(xVals.subarray(xOffset,xOffset+size[3]),outOffset),outOffset+=size[3]}}var sliceConfig3={kernelName:Slice,backendName:"wasm",kernelFunc:slice4};var wasmFunc8;function setup36(backend2){wasmFunc8=backend2.wasm.cwrap(Softmax,null,["number","number","number","number"])}function softmax5(args){let{backend:backend2,inputs:{logits},attrs:{dim}}=args,xId=backend2.dataIdMap.get(logits.dataId).id,out=backend2.makeOutput(logits.shape,logits.dtype),outId=backend2.dataIdMap.get(out.dataId).id,channels=logits.shape[dim],batch=util_exports.sizeFromShape(logits.shape)/channels;return util_exports.sizeFromShape(out.shape)===0||wasmFunc8(xId,outId,channels,batch),out}var softmaxConfig3={kernelName:Softmax,backendName:"wasm",setupFunc:setup36,kernelFunc:softmax5};function splitV3(args){let{inputs,attrs,backend:backend2}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,$axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend2});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3};var sqrtConfig3=createUnaryKernelConfig(Sqrt);var squareConfig3=createUnaryKernelConfig(Square);var supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15);var wasmStridedSlice;function setup37(backend2){wasmStridedSlice=backend2.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend2,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape5({inputs:{x},attrs:{shape:newShape},backend:backend2}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend2});return reshape5({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend2})}let out=backend2.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend2.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend2.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape5({inputs:{x:out},attrs:{shape:outShape},backend:backend2})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4};var supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16);var wasmSum;function setup38(backend2){wasmSum=backend2.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend2,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend2),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend2.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend2.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend2.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend2.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5};var tanhConfig3=createUnaryKernelConfig(Tanh);var wasmTile;function setup39(backend2){wasmTile=backend2.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile5(args){let{inputs,backend:backend2,attrs}=args,{x}=inputs,xId=backend2.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[i]*reps[i];let xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),newShapeBytes=new Uint8Array(new Int32Array(newShape).buffer),out=backend2.makeOutput(newShape,x.dtype),outId=backend2.dataIdMap.get(out.dataId).id;return wasmTile(xId,xShapeBytes,x.shape.length,newShapeBytes,newShape.length,CppDType[out.dtype],outId),out}var tileConfig3={kernelName:Tile,backendName:"wasm",setupFunc:setup39,kernelFunc:tile5};var wasmTopK;function setup40(backend2){wasmTopK=backend2.wasm.cwrap(TopK,null,["number","array","number","number","number","bool","number","number"])}var topk2=({inputs,backend:backend2,attrs})=>{let{x}=inputs,{k,sorted}=attrs,xId=backend2.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend2.makeOutput(outputShape,x.dtype),outValuesId=backend2.dataIdMap.get(outValues.dataId).id,outIndices=backend2.makeOutput(outputShape,"int32"),outIndicesId=backend2.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend2,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i<rank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let outs=new Array(numOutputs),begin=new Array(rank).fill(0),size=value.shape.slice();size[axis]=1;for(let i=0;i<outs.length;i++)begin[axis]=i,outs[i]=slice4({inputs:{x:value},attrs:{begin,size},backend:backend2});return outs.map(({dataId,dtype})=>({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend2}=args,out=backend2.makeOutput(x.shape,x.dtype);return backend2.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4};var kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,roundConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule(require_tfjs_backend_wasm_threaded_simd());var wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else 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Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}';var import_tfjs_backend_wasm=__toModule(require_tfjs_backend_wasm());var BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data=this.dataIdMap.get(dataId);this.wasm._free(data.memoryOffset),this.wasm.tfjs.disposeData(data.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{if(initialized||initAborted)return;initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.3";var WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);export{Abs,Acos,Acosh,AdadeltaOptimizer,AdagradOptimizer,AdamOptimizer,AdamaxOptimizer,Add,AddN,All,Any,ArgMax,ArgMin,Asin,Asinh,Atan,Atan2,Atanh,AvgPool,AvgPool3D,AvgPool3DGrad,AvgPoolGrad,BackendWasm,BatchMatMul,BatchToSpaceND,Bincount,BroadcastTo,Callback,CallbackList,Cast,Ceil,ClipByValue,Complex,ComplexAbs,Concat,Conv2D,Conv2DBackpropFilter,Conv2DBackpropInput,Conv3D,Conv3DBackpropFilterV2,Conv3DBackpropInputV2,Cos,Cosh,CropAndResize,Cumsum,CustomCallback,DataStorage,DenseBincount,DepthToSpace,DepthwiseConv2dNative,DepthwiseConv2dNativeBackpropFilter,DepthwiseConv2dNativeBackpropInput,Diag,Dilation2D,Dilation2DBackpropFilter,Dilation2DBackpropInput,ENV,EarlyStopping,Elu,EluGrad,Environment,Equal,Erf,Exp,ExpandDims,Expm1,FFT,Fill,FlipLeftRight,Floor,FloorDiv,FromPixels,FusedBatchNorm,FusedConv2D,FusedDepthwiseConv2D,GatherNd,GatherV2,GraphModel,Greater,GreaterEqual,History,IFFT,Identity,Imag,InputSpec,IsFinite,IsInf,IsNan,KernelBackend,LRN,LRNGrad,LayerVariable,LayersModel,LeakyRelu,Less,LessEqual,LinSpace,Log,Log1p,LogSoftmax,LogicalAnd,LogicalNot,LogicalOr,Max,MaxPool,MaxPool3D,MaxPool3DGrad,MaxPoolGrad,MaxPoolWithArgmax,Maximum,Mean,Min,Minimum,MirrorPad,Mod,MomentumOptimizer,Multinomial,Multiply,Neg,NonMaxSuppressionV3,NonMaxSuppressionV4,NonMaxSuppressionV5,NotEqual,OP_SCOPE_SUFFIX,OneHot,OnesLike,Optimizer,Pack,PadV2,Pool,Pow,Prelu,Prod,RMSPropOptimizer,RNN,Range,Rank,Real,RealDiv,Reciprocal,Reduction,Relu,Relu6,Reshape,ResizeBilinear,ResizeBilinearGrad,ResizeNearestNeighbor,ResizeNearestNeighborGrad,Reverse,RotateWithOffset,Round,Rsqrt,SGDOptimizer,ScatterNd,Select,Selu,Sequential,Sigmoid,Sign,Sin,Sinh,Slice,Softmax,Softplus,SpaceToBatchND,SparseToDense,SplitV,Sqrt,Square,SquaredDifference,Step,StridedSlice,Sub,Sum,SymbolicTensor,Tan,Tanh,Tensor,TensorBuffer,Tile,TopK,Transpose,Unique,Unpack,UnsortedSegmentSum,Variable,ZerosLike,_FusedMatMul,abs,acos,acosh,add2 as add,addN,addStrict,all,any,argMax,argMin,asin,asinh,atan,atan2,atanh,avgPool,avgPool3d,backend,backend_util_exports as backend_util,basicLSTMCell,batchNorm,batchNorm2d,batchNorm3d,batchNorm4d,batchToSpaceND,bincount,booleanMaskAsync,broadcastTo,browser_exports as browser,buffer,callbacks,cast,ceil,clipByValue,clone,complex,concat,concat1d,concat2d,concat3d,concat4d,exports_constraints_exports as constraints,conv1d,conv2d,conv2dTranspose,conv3d,conv3dTranspose,copyRegisteredKernels,cos,cosh,cosineWindow,cumsum,customGrad,dist_exports as data,denseBincount,deprecationWarn,depthToSpace,depthwiseConv2d,deregisterOp,device_util_exports as device_util,diag,dilation2d,disableDeprecationWarnings,dispose,disposeVariables,div,divNoNan,divStrict,dot,dropout,elu,enableDebugMode,enableProdMode,enclosingPowerOfTwo,engine,env,equal,equalStrict,erf,exp,expandDims,expm1,eye,fft,fill,findBackend,findBackendFactory,floor,floorDiv,fused_ops_exports as fused,gather,gatherND,gather_nd_util_exports as gather_util,getBackend,getGradient,getKernel,getKernelsForBackend,grad,grads,greater,greaterEqual,greaterEqualStrict,greaterStrict,ifft,imag,image,inTopKAsync,exports_initializers_exports as initializers,input,io_exports as io,irfft,isFinite2 as isFinite,isInf,isNaN2 as isNaN,keep,kernel_impls_exports as kernel_impls,exports_layers_exports as layers,leakyRelu,less,lessEqual,lessEqualStrict,lessStrict,linalg,linspace,loadGraphModel,loadLayersModel,localResponseNormalization,log,log1p,logSigmoid,logSoftmax,logSumExp,logicalAnd,logicalNot,logicalOr,logicalXor,losses,matMul,math_exports as math,max,maxPool,maxPool3d,maxPoolWithArgmax,maximum,maximumStrict,mean,memory,exports_metrics_exports as metrics,min,minimum,minimumStrict,mirrorPad,mod,modStrict,model,exports_models_exports as models,moments,movingAverage,mul,mulStrict,multiRNNCell,multinomial,neg,nextFrame,norm,notEqual,notEqualStrict,oneHot,ones2 as ones,onesLike,op,outerProduct,pad,pad1d,pad2d,pad3d,pad4d,pool,pow,powStrict,prelu,print2 as print,prod,profile,rand,randomGamma,randomNormal,randomUniform,range,ready,real,reciprocal,registerBackend,registerCallbackConstructor,registerGradient,registerKernel,registerOp,exports_regularizers_exports as regularizers,relu,relu6,removeBackend,reshape,reverse,reverse1d,reverse2d,reverse3d,reverse4d,rfft,round2 as round,rsqrt,scalar,scatterND,scatter_nd_util_exports as scatter_util,selu,separableConv2d,sequential,serialization_exports as serialization,setBackend,setPlatform,setWasmPath,setWasmPaths,setdiff1dAsync,sigmoid,sign,signal,sin,sinh,slice,slice1d,slice2d,slice3d,slice4d,slice_util_exports as slice_util,softmax,softplus,spaceToBatchND,sparseToDense,spectral,split,sqrt,square,squaredDifference,squaredDifferenceStrict,squeeze,stack,step,stridedSlice,sub,subStrict,sum2 as sum,sumOutType,tan,tanh2 as tanh,tensor,tensor1d,tensor2d,tensor3d,tensor4d,tensor5d,tensor6d,tensor_util_exports as tensor_util,test_util_exports as test_util,tidy,tile,time,topk,train,transpose,truncatedNormal,unique,unregisterGradient,unregisterKernel,unsortedSegmentSum,unstack,upcastType,util_exports as util,valueAndGrad,valueAndGrads,variable,variableGrads,version8 as version,version3 as version_converter,version as version_core,version2 as version_layers,version9 as version_wasm,where,whereAsync,zeros,zerosLike};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -4203,5 +4203,21 @@ return a / b;`,DIV_PACKED=`
  * limitations under the License.
  * =============================================================================
  */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
 /** @license See the LICENSE file. */
 //# sourceMappingURL=tfjs.esm.js.map
diff --git a/dist/tfjs.esm.js.map b/dist/tfjs.esm.js.map
index 507318d..9f41066 100644
--- a/dist/tfjs.esm.js.map
+++ b/dist/tfjs.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["empty:/home/vlado/dev/face-api/node_modules/node-fetch/browser.js", "../node_modules/seedrandom/lib/alea.js", "../node_modules/seedrandom/lib/xor128.js", "../node_modules/seedrandom/lib/xorwow.js", "../node_modules/seedrandom/lib/xorshift7.js", "../node_modules/seedrandom/lib/xor4096.js", "../node_modules/seedrandom/lib/tychei.js", "empty:crypto", "../node_modules/seedrandom/seedrandom.js", "../node_modules/seedrandom/index.js", "empty:/home/vlado/dev/face-api/node_modules/string_decoder/lib/string_decoder.js", "empty:path", "empty:worker_threads", "empty:perf_hooks", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js", "../node_modules/@tensorflow/tfjs-core/src/backends/backend.ts", "../node_modules/@tensorflow/tfjs-core/src/util_base.ts", "../node_modules/@tensorflow/tfjs-core/src/environment.ts", "../node_modules/@tensorflow/tfjs-core/src/global_util.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_names.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/util.ts", "../node_modules/@tensorflow/tfjs-core/src/profiler.ts", "../node_modules/@tensorflow/tfjs-core/src/tape.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_format.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util.ts", "../node_modules/@tensorflow/tfjs-core/src/types.ts", "../node_modules/@tensorflow/tfjs-core/src/engine.ts", "../node_modules/@tensorflow/tfjs-core/src/device_util.ts", "../node_modules/@tensorflow/tfjs-core/src/flags.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util_env.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/operation.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/complex.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor_ops_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/io/types.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io_utils.ts", "../node_modules/@tensorflow/tfjs-core/src/io/router_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/io/indexed_db.ts", "../node_modules/@tensorflow/tfjs-core/src/io/local_storage.ts", "../node_modules/@tensorflow/tfjs-core/src/io/model_management.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_browser.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_node.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/buffer.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cast.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clone.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/print.ts", "../node_modules/@tensorflow/tfjs-core/src/base_side_effects.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io.ts", "../node_modules/@tensorflow/tfjs-core/src/io/browser_files.ts", "../node_modules/@tensorflow/tfjs-core/src/io/progress.ts", "../node_modules/@tensorflow/tfjs-core/src/io/weights_loader.ts", "../node_modules/@tensorflow/tfjs-core/src/io/http.ts", "../node_modules/@tensorflow/tfjs-core/src/io/passthrough.ts", "../node_modules/@tensorflow/tfjs-core/src/math.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mat_mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/one_hot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/confusion_matrix.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/browser.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/scatter_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice_util.ts", "../node_modules/@tensorflow/tfjs-core/src/serialization.ts", "../node_modules/@tensorflow/tfjs-core/src/test_util.ts", "../node_modules/@tensorflow/tfjs-core/src/version.ts", "../node_modules/@tensorflow/tfjs-core/src/globals.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floorDiv.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/abs.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add_n.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/all.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/any.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_max.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_min.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asin.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asinh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan2.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/reshape.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/basic_lstm_cell.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batch_to_space_nd.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm.ts", 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-  "sourcesContent": ["", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var _scriptDir=undefined;var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":167,\"maximum\":167+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 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stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"vb\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"wb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"xb\"]).apply(null,arguments)};var dynCall_vi=Module[\"dynCall_vi\"]=function(){return(dynCall_vi=Module[\"dynCall_vi\"]=Module[\"asm\"][\"yb\"]).apply(null,arguments)};var dynCall_v=Module[\"dynCall_v\"]=function(){return(dynCall_v=Module[\"dynCall_v\"]=Module[\"asm\"][\"zb\"]).apply(null,arguments)};var dynCall_ii=Module[\"dynCall_ii\"]=function(){return(dynCall_ii=Module[\"dynCall_ii\"]=Module[\"asm\"][\"Ab\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"_pthread_self\"]=_pthread_self;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(!ENVIRONMENT_IS_PTHREAD)noExitRuntime=true;if(!ENVIRONMENT_IS_PTHREAD)run();\n\n\n  return WasmBackendModuleThreadedSimd\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModuleThreadedSimd;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModuleThreadedSimd; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n    ", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":149,\"maximum\":149+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Floor\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"LeakyRelu\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"Mean\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"Neg\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Prod\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"RealDiv\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"TopK\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var 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+  "sourcesContent": ["", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":169,\"maximum\":169+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"Ba\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Ca\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var 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_free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"_a\"]).apply(null,arguments)};var _emscripten_get_global_libc=Module[\"_emscripten_get_global_libc\"]=function(){return(_emscripten_get_global_libc=Module[\"_emscripten_get_global_libc\"]=Module[\"asm\"][\"$a\"]).apply(null,arguments)};var ___errno_location=Module[\"___errno_location\"]=function(){return(___errno_location=Module[\"___errno_location\"]=Module[\"asm\"][\"ab\"]).apply(null,arguments)};var ___em_js__initPthreadsJS=Module[\"___em_js__initPthreadsJS\"]=function(){return(___em_js__initPthreadsJS=Module[\"___em_js__initPthreadsJS\"]=Module[\"asm\"][\"bb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"cb\"]).apply(null,arguments)};var 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_emscripten_sync_run_in_main_thread_5=Module[\"_emscripten_sync_run_in_main_thread_5\"]=function(){return(_emscripten_sync_run_in_main_thread_5=Module[\"_emscripten_sync_run_in_main_thread_5\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_6=Module[\"_emscripten_sync_run_in_main_thread_6\"]=function(){return(_emscripten_sync_run_in_main_thread_6=Module[\"_emscripten_sync_run_in_main_thread_6\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_7=Module[\"_emscripten_sync_run_in_main_thread_7\"]=function(){return(_emscripten_sync_run_in_main_thread_7=Module[\"_emscripten_sync_run_in_main_thread_7\"]=Module[\"asm\"][\"sb\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"tb\"]).apply(null,arguments)};var _emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=function(){return(_emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=Module[\"asm\"][\"ub\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"vb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"wb\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"xb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"yb\"]).apply(null,arguments)};var dynCall_vi=Module[\"dynCall_vi\"]=function(){return(dynCall_vi=Module[\"dynCall_vi\"]=Module[\"asm\"][\"zb\"]).apply(null,arguments)};var dynCall_v=Module[\"dynCall_v\"]=function(){return(dynCall_v=Module[\"dynCall_v\"]=Module[\"asm\"][\"Ab\"]).apply(null,arguments)};var dynCall_ii=Module[\"dynCall_ii\"]=function(){return(dynCall_ii=Module[\"dynCall_ii\"]=Module[\"asm\"][\"Bb\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"_pthread_self\"]=_pthread_self;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(!ENVIRONMENT_IS_PTHREAD)noExitRuntime=true;if(!ENVIRONMENT_IS_PTHREAD)run();\n\n\n  return WasmBackendModuleThreadedSimd\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModuleThreadedSimd;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModuleThreadedSimd; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n    ", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new 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assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function 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callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function 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diff --git a/dist/tfjs.esm.json b/dist/tfjs.esm.json
index 54c4a6e..dc0c2f7 100644
--- a/dist/tfjs.esm.json
+++ b/dist/tfjs.esm.json
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         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js": {
-          "bytesInOutput": 20033
+          "bytesInOutput": 20045
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/version.js": {
           "bytesInOutput": 21
@@ -24106,10 +24120,10 @@
           "bytesInOutput": 0
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_gpu.js": {
-          "bytesInOutput": 480
+          "bytesInOutput": 485
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_packed_gpu.js": {
-          "bytesInOutput": 1803
+          "bytesInOutput": 1808
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Identity.js": {
           "bytesInOutput": 232
@@ -24274,7 +24288,7 @@
           "bytesInOutput": 1379
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Bincount.js": {
-          "bytesInOutput": 417
+          "bytesInOutput": 397
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NotEqual.js": {
           "bytesInOutput": 181
@@ -24316,7 +24330,7 @@
           "bytesInOutput": 276
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js": {
-          "bytesInOutput": 2435
+          "bytesInOutput": 2425
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js": {
           "bytesInOutput": 677
@@ -24328,7 +24342,7 @@
           "bytesInOutput": 2022
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js": {
-          "bytesInOutput": 4688
+          "bytesInOutput": 4687
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js": {
           "bytesInOutput": 1083
@@ -24361,16 +24375,16 @@
           "bytesInOutput": 3046
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js": {
-          "bytesInOutput": 405
+          "bytesInOutput": 406
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/cumsum_gpu.js": {
-          "bytesInOutput": 1581
+          "bytesInOutput": 1585
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumsum.js": {
           "bytesInOutput": 1532
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DenseBincount.js": {
-          "bytesInOutput": 818
+          "bytesInOutput": 798
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/depth_to_space_gpu.js": {
           "bytesInOutput": 1259
@@ -24382,10 +24396,10 @@
           "bytesInOutput": 2765
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu_depthwise.js": {
-          "bytesInOutput": 7679
+          "bytesInOutput": 7672
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js": {
-          "bytesInOutput": 940
+          "bytesInOutput": 939
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu_depthwise.js": {
           "bytesInOutput": 3142
@@ -24463,7 +24477,7 @@
           "bytesInOutput": 1174
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels.js": {
-          "bytesInOutput": 1276
+          "bytesInOutput": 1368
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedConv2D.js": {
           "bytesInOutput": 1933
@@ -24472,7 +24486,7 @@
           "bytesInOutput": 1850
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_nd_gpu.js": {
-          "bytesInOutput": 733
+          "bytesInOutput": 737
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js": {
           "bytesInOutput": 960
@@ -24748,7 +24762,7 @@
           "bytesInOutput": 752
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SplitV.js": {
-          "bytesInOutput": 555
+          "bytesInOutput": 557
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sqrt.js": {
           "bytesInOutput": 135
@@ -24778,10 +24792,10 @@
           "bytesInOutput": 833
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js": {
-          "bytesInOutput": 510
+          "bytesInOutput": 500
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js": {
-          "bytesInOutput": 454
+          "bytesInOutput": 444
         },
         "node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unique.js": {
           "bytesInOutput": 522
@@ -25014,6 +25028,9 @@
         "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RotateWithOffset.js": {
           "bytesInOutput": 1106
         },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Round.js": {
+          "bytesInOutput": 48
+        },
         "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Rsqrt.js": {
           "bytesInOutput": 48
         },
@@ -25036,7 +25053,7 @@
           "bytesInOutput": 612
         },
         "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SplitV.js": {
-          "bytesInOutput": 546
+          "bytesInOutput": 547
         },
         "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sqrt.js": {
           "bytesInOutput": 46
@@ -25072,16 +25089,16 @@
           "bytesInOutput": 242
         },
         "node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js": {
-          "bytesInOutput": 1412
+          "bytesInOutput": 1425
         },
         "node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js": {
           "bytesInOutput": 494
         },
         "node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js": {
-          "bytesInOutput": 6440
+          "bytesInOutput": 6450
         },
         "node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js": {
-          "bytesInOutput": 3425
+          "bytesInOutput": 3430
         },
         "node_modules/@tensorflow/tfjs-backend-wasm/dist/version.js": {
           "bytesInOutput": 21
@@ -25093,7 +25110,7 @@
           "bytesInOutput": 0
         }
       },
-      "bytes": 1565414
+      "bytes": 1566890
     }
   }
 }
diff --git a/example/index.html b/example/index.html
index 6972920..eaca086 100644
--- a/example/index.html
+++ b/example/index.html
@@ -9,6 +9,5 @@
   </head>
   <body style="font-family: monospace; background: black; color: white; font-size: 16px; line-height: 22px; margin: 0;">
     <div id="log"></div>
-    </script>
   </body>
 </html>
diff --git a/example/index.js b/example/index.js
index a3df15e..8d94758 100644
--- a/example/index.js
+++ b/example/index.js
@@ -110,7 +110,7 @@ async function main() {
   log('FaceAPI Test');
 
   // if you want to use wasm backend location for wasm binaries must be specified
-  // await faceapi.tf.setWasmPaths('../node_modules/@tensorflow/tfjs-backend-wasm/dist/')
+  // await faceapi.tf.setWasmPaths('../node_modules/@tensorflow/tfjs-backend-wasm/dist/');
   // await faceapi.tf.setBackend('wasm');
 
   // default is webgl backend
diff --git a/example/webcam.html b/example/webcam.html
new file mode 100644
index 0000000..3be7af0
--- /dev/null
+++ b/example/webcam.html
@@ -0,0 +1,15 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <title>OpenImages Test</title>
+    <meta http-equiv="content-type">
+    <meta content="text/html">
+    <meta charset="UTF-8">
+    <script src="./webcam.js" type="module"></script>
+  </head>
+  <body style="font-family: monospace; background: black; color: white; font-size: 16px; line-height: 22px; margin: 0;">
+    <video id="video" playsinline class="video"></video>
+    <canvas id="canvas" class="canvas" style="position: fixed; top: 0; left: 0; z-index: 10"></canvas>
+    <div id="log"></div>
+  </body>
+</html>
diff --git a/example/webcam.js b/example/webcam.js
new file mode 100644
index 0000000..26f7358
--- /dev/null
+++ b/example/webcam.js
@@ -0,0 +1,173 @@
+import * as faceapi from '../dist/face-api.esm.js';
+
+// configuration options
+const modelPath = 'https://vladmandic.github.io/face-api/model/'; // path to model folder that will be loaded using http
+const minScore = 0.1; // minimum score
+const maxResults = 5; // maximum number of results to return
+let optionsSSDMobileNet;
+
+// helper function to pretty-print json object to string
+function str(json) {
+  let text = '<font color="lightblue">';
+  text += json ? JSON.stringify(json).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ', ') : '';
+  text += '</font>';
+  return text;
+}
+
+// helper function to print strings to html document as a log
+function log(...txt) {
+  // eslint-disable-next-line no-console
+  console.log(...txt);
+  // @ts-ignore
+  document.getElementById('log').innerHTML += `<br>${txt}`;
+}
+
+// helper function to draw detected faces
+function drawFaces(canvas, data, fps) {
+  const ctx = canvas.getContext('2d');
+  if (!ctx) return;
+  ctx.clearRect(0, 0, canvas.width, canvas.height);
+  // draw title
+  ctx.font = '1.4rem sans-serif';
+  ctx.fillStyle = 'white';
+  ctx.fillText(`FPS: ${fps}`, 10, 25);
+  for (const person of data) {
+    // draw box around each face
+    ctx.lineWidth = 3;
+    ctx.strokeStyle = 'deepskyblue';
+    ctx.fillStyle = 'deepskyblue';
+    ctx.globalAlpha = 0.4;
+    ctx.beginPath();
+    ctx.rect(person.detection.box.x, person.detection.box.y, person.detection.box.width, person.detection.box.height);
+    ctx.stroke();
+    ctx.globalAlpha = 1;
+    // const expression = person.expressions.sort((a, b) => Object.values(a)[0] - Object.values(b)[0]);
+    const expression = Object.entries(person.expressions).sort((a, b) => b[1] - a[1]);
+    ctx.fillText(`gender ${Math.round(100 * person.genderProbability)}% ${person.gender}`, person.detection.box.x, person.detection.box.y - 45);
+    ctx.fillText(`expression ${Math.round(100 * expression[0][1])}% ${expression[0][0]}`, person.detection.box.x, person.detection.box.y - 25);
+    ctx.fillText(`age ${Math.round(person.age)} years`, person.detection.box.x, person.detection.box.y - 5);
+    // draw face points for each face
+    ctx.fillStyle = 'lightblue';
+    ctx.globalAlpha = 0.5;
+    const pointSize = 2;
+    for (const pt of person.landmarks.positions) {
+      ctx.beginPath();
+      ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);
+      ctx.fill();
+    }
+  }
+}
+
+async function detectVideo(video, canvas) {
+  const t0 = performance.now();
+  faceapi
+    .detectAllFaces(video, optionsSSDMobileNet)
+    .withFaceLandmarks()
+    .withFaceExpressions()
+    // .withFaceDescriptors()
+    .withAgeAndGender()
+    .then((result) => {
+      const fps = 1000 / (performance.now() - t0);
+      drawFaces(canvas, result, fps.toLocaleString());
+      requestAnimationFrame(() => detectVideo(video, canvas));
+      return true;
+    })
+    .catch((err) => {
+      log(`Detect Error: ${str(err)}`);
+      return false;
+    });
+}
+
+// just initialize everything and call main function
+async function setupCamera() {
+  const video = document.getElementById('video');
+  const canvas = document.getElementById('canvas');
+  if (!video || !canvas) return null;
+
+  let msg = '';
+  log('Setting up camera');
+  // setup webcam. note that navigator.mediaDevices requires that page is accessed via https
+  if (!navigator.mediaDevices) {
+    log('Camera Error: access not supported');
+    return null;
+  }
+  let stream;
+  const constraints = {
+    audio: false,
+    video: { facingMode: 'user', resizeMode: 'crop-and-scale' },
+  };
+  if (window.innerWidth > window.innerHeight) constraints.video.width = { ideal: window.innerWidth };
+  else constraints.video.height = { ideal: window.innerHeight };
+  try {
+    stream = await navigator.mediaDevices.getUserMedia(constraints);
+  } catch (err) {
+    if (err.name === 'PermissionDeniedError' || err.name === 'NotAllowedError') msg = 'camera permission denied';
+    else if (err.name === 'SourceUnavailableError') msg = 'camera not available';
+    log(`Camera Error: ${msg}: ${err.message || err}`);
+    return null;
+  }
+  // @ts-ignore
+  if (stream) video.srcObject = stream;
+  else {
+    log('Camera Error: stream empty');
+    return null;
+  }
+  const track = stream.getVideoTracks()[0];
+  const settings = track.getSettings();
+  log(`Camera active: ${track.label} ${str(constraints)}`);
+  log(`Camera settings: ${str(settings)}`);
+  return new Promise((resolve) => {
+    video.onloadeddata = async () => {
+      // @ts-ignore
+      canvas.width = video.videoWidth;
+      // @ts-ignore
+      canvas.height = video.videoHeight;
+      // @ts-ignore
+      video.play();
+      detectVideo(video, canvas);
+      resolve(true);
+    };
+  });
+}
+
+async function setupFaceAPI() {
+  // load face-api models
+  log('Models loading');
+  await faceapi.nets.tinyFaceDetector.load(modelPath);
+  await faceapi.nets.ssdMobilenetv1.load(modelPath);
+  await faceapi.nets.ageGenderNet.load(modelPath);
+  await faceapi.nets.faceLandmark68Net.load(modelPath);
+  await faceapi.nets.faceRecognitionNet.load(modelPath);
+  await faceapi.nets.faceExpressionNet.load(modelPath);
+  optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence: minScore, maxResults });
+
+  // check tf engine state
+  const engine = await faceapi.tf.engine();
+  log(`Models loaded: ${str(engine.state)}`);
+}
+
+async function main() {
+  // initialize tfjs
+  log('FaceAPI WebCam Test');
+
+  // if you want to use wasm backend location for wasm binaries must be specified
+  // await faceapi.tf.setWasmPaths('../node_modules/@tensorflow/tfjs-backend-wasm/dist/');
+  // await faceapi.tf.setBackend('wasm');
+
+  // default is webgl backend
+  await faceapi.tf.setBackend('webgl');
+
+  await faceapi.tf.enableProdMode();
+  await faceapi.tf.ENV.set('DEBUG', false);
+  await faceapi.tf.ready();
+
+  // check version
+  log(`Version: TensorFlow/JS ${str(faceapi.tf?.version_core || '(not loaded)')} FaceAPI ${str(faceapi?.version || '(not loaded)')} Backend: ${str(faceapi.tf?.getBackend() || '(not loaded)')}`);
+  log(`Flags: ${str(faceapi.tf.ENV.flags)}`);
+
+  setupFaceAPI();
+  setupCamera();
+}
+
+// start processing as soon as page is loaded
+window.onload = main;
diff --git a/favicon.ico b/favicon.ico
new file mode 100644
index 0000000..1cbc044
Binary files /dev/null and b/favicon.ico differ
diff --git a/package-lock.json b/package-lock.json
index 09cad24..1afb09d 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -156,17 +156,17 @@
       }
     },
     "@tensorflow/tfjs": {
-      "version": "2.8.2",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs/-/tfjs-2.8.2.tgz",
-      "integrity": "sha512-tn1OE/totTuLl2dl7girxwNik2D2O9ZknghjgJvRZegqE/8FO/ix7mo4ICzMq9cHpW/bVCFKCwqu1PjAx3lkzw==",
+      "version": "2.8.3",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs/-/tfjs-2.8.3.tgz",
+      "integrity": "sha512-wJYOaEW9V2woCvTs2vqOtYdb1fr6koVO9YiMXroeRP4BQS3o7x17HtguSVQyQt1mROORbJ1Ji6n63Bc1mZa4rQ==",
       "dev": true,
       "requires": {
-        "@tensorflow/tfjs-backend-cpu": "2.8.2",
-        "@tensorflow/tfjs-backend-webgl": "2.8.2",
-        "@tensorflow/tfjs-converter": "2.8.2",
-        "@tensorflow/tfjs-core": "2.8.2",
-        "@tensorflow/tfjs-data": "2.8.2",
-        "@tensorflow/tfjs-layers": "2.8.2",
+        "@tensorflow/tfjs-backend-cpu": "2.8.3",
+        "@tensorflow/tfjs-backend-webgl": "2.8.3",
+        "@tensorflow/tfjs-converter": "2.8.3",
+        "@tensorflow/tfjs-core": "2.8.3",
+        "@tensorflow/tfjs-data": "2.8.3",
+        "@tensorflow/tfjs-layers": "2.8.3",
         "argparse": "^1.0.10",
         "chalk": "^4.1.0",
         "core-js": "3",
@@ -175,9 +175,9 @@
       }
     },
     "@tensorflow/tfjs-backend-cpu": {
-      "version": "2.8.2",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-2.8.2.tgz",
-      "integrity": "sha512-/puqtocadSmwW+ntiuW6UMhcYk1laMTr1hoWqyvMinaVwxSZwOyfRqDyF9snxHLfCW3uS8HX02LRqh9B4AzD0A==",
+      "version": "2.8.3",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-2.8.3.tgz",
+      "integrity": "sha512-kMUONKdIknwacUZPR1hIKVba1F8gyiYr7NpzNOf/L7y5li87iRVBcMtYoNn/dEfJ8R/wO6SDeYlDRuDcXXNFlA==",
       "dev": true,
       "requires": {
         "@types/seedrandom": "2.4.27",
@@ -185,22 +185,22 @@
       }
     },
     "@tensorflow/tfjs-backend-wasm": {
-      "version": "2.8.2",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-wasm/-/tfjs-backend-wasm-2.8.2.tgz",
-      "integrity": "sha512-smt06cLHJMmiOu0laNhS1xOug9EtWz0Orm/MBELFwYfmJC5u95wAX83Ix9zBEu/wsyP0PFAdX4zEN8BCaez7kA==",
+      "version": "2.8.3",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-wasm/-/tfjs-backend-wasm-2.8.3.tgz",
+      "integrity": "sha512-YPTyHmQuOQM8j1WsICbo78Y/yFUYKj05ZF6yaQWBaTg2YSHLjrkoI1a37P0M1ELoHU7CV6hKigQJG3zhM5SJvg==",
       "dev": true,
       "requires": {
-        "@tensorflow/tfjs-backend-cpu": "2.8.2",
+        "@tensorflow/tfjs-backend-cpu": "2.8.3",
         "@types/emscripten": "~0.0.34"
       }
     },
     "@tensorflow/tfjs-backend-webgl": {
-      "version": "2.8.2",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-2.8.2.tgz",
-      "integrity": "sha512-jfc2mjfwqMvjXDCYvrXwGY1i9MK+1P4COiCnT1r5rMl6IssSNH2WqvaqJFtRWGtgp/QiBY69B6wM6qSJIkOSeg==",
+      "version": "2.8.3",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-2.8.3.tgz",
+      "integrity": "sha512-II+g0LOcudD2NA4X1990EfrCQhkPyJcgoCjRbV8Y4JBJn5kv6SyOGiE15exyQBqTMBxNTwjXukUf9N4x89Lgag==",
       "dev": true,
       "requires": {
-        "@tensorflow/tfjs-backend-cpu": "2.8.2",
+        "@tensorflow/tfjs-backend-cpu": "2.8.3",
         "@types/offscreencanvas": "~2019.3.0",
         "@types/seedrandom": "2.4.27",
         "@types/webgl-ext": "0.0.30",
@@ -209,15 +209,15 @@
       }
     },
     "@tensorflow/tfjs-converter": {
-      "version": "2.8.2",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-converter/-/tfjs-converter-2.8.2.tgz",
-      "integrity": "sha512-JJPrS2SClMEnvoC/vBOUlWa03UBl6ZZRIeddjgTo+V1CJyvtYr+njySz9lX5HoCBtrcrLkMcd+1IYFKtgFH1Cg==",
+      "version": "2.8.3",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-converter/-/tfjs-converter-2.8.3.tgz",
+      "integrity": "sha512-Z107Q8/aNyhBd9E1lN6+aZg/uBWvmFSV8AmZrt7MfvDeAPOSyNTj0L9xYF5fxjlWb9ligLlOIbPkTRJ37vsXYw==",
       "dev": true
     },
     "@tensorflow/tfjs-core": {
-      "version": "2.8.2",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-core/-/tfjs-core-2.8.2.tgz",
-      "integrity": "sha512-r3Df7phzNa80uoTtlcBGJ/rf+kweSKgN1/VmBT7b53womzoYlw3fQHlZ1WKUbHL9kOf2Zt4y2PPcBiKUQYQC+Q==",
+      "version": "2.8.3",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-core/-/tfjs-core-2.8.3.tgz",
+      "integrity": "sha512-wbVuT7clJZIvtYcJPtIX4M49xCC/ccWpHIN+jRWoSBZxR3PJJ9pkh36nBq65aaBsdr5SL6v02oQ2vc7jfjdI3Q==",
       "dev": true,
       "requires": {
         "@types/offscreencanvas": "~2019.3.0",
@@ -228,9 +228,9 @@
       }
     },
     "@tensorflow/tfjs-data": {
-      "version": "2.8.2",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-data/-/tfjs-data-2.8.2.tgz",
-      "integrity": "sha512-hC/lQe65YCGx0GVWDR/Wf4/B2E9HfWwegWmQ0RzVzcmakF3Y4tOdLRo5vTaar80Un374Uu3ARG/h7YBvneKFCQ==",
+      "version": "2.8.3",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-data/-/tfjs-data-2.8.3.tgz",
+      "integrity": "sha512-Rxd4NfKvFQODf0+pMAoCuYWFf8XbaxUbr2gC/06uFnRXrWiTCuKWCSOqAg0lx7V6FLkscmKZNTD7iz0kAr32Og==",
       "dev": true,
       "requires": {
         "@types/node-fetch": "^2.1.2",
@@ -238,19 +238,19 @@
       }
     },
     "@tensorflow/tfjs-layers": {
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+      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.1.0.tgz",
+      "integrity": "sha512-hcVC3wYEziELGGmEEXue7D75zbwIIVUMWAVbHItGPx0ziyXxrOMQx4rQEVEV45Ut/1IotuEvwqPopzIOkDMf0A==",
       "dev": true
     },
     "tsutils": {
-      "version": "3.17.1",
-      "resolved": "https://registry.npmjs.org/tsutils/-/tsutils-3.17.1.tgz",
-      "integrity": "sha512-kzeQ5B8H3w60nFY2g8cJIuH7JDpsALXySGtwGJ0p2LSjLgay3NdIpqq5SoOBe46bKDW2iq25irHCr8wjomUS2g==",
+      "version": "3.19.1",
+      "resolved": "https://registry.npmjs.org/tsutils/-/tsutils-3.19.1.tgz",
+      "integrity": "sha512-GEdoBf5XI324lu7ycad7s6laADfnAqCw6wLGI+knxvw9vsIYBaJfYdmeCEG3FMMUiSm3OGgNb+m6utsWf5h9Vw==",
       "dev": true,
       "requires": {
         "tslib": "^1.8.1"
diff --git a/package.json b/package.json
index 93d3b81..7dc1acf 100644
--- a/package.json
+++ b/package.json
@@ -11,7 +11,8 @@
   },
   "scripts": {
     "start": "node --trace-warnings example/node-singleprocess.js",
-    "build": "rimraf dist/* types/* && node ./build.js",
+    "dev": "npm install && node server/dev.js",
+    "build": "rimraf dist/* types/* && node server/build.js",
     "lint": "eslint src/**/*"
   },
   "keywords": [
@@ -40,14 +41,15 @@
     "@vladmandic/pilogger": "^0.2.11"
   },
   "devDependencies": {
-    "@tensorflow/tfjs": "^2.8.2",
-    "@tensorflow/tfjs-backend-wasm": "^2.8.2",
-    "@tensorflow/tfjs-node": "^2.8.2",
-    "@tensorflow/tfjs-node-gpu": "^2.8.2",
-    "@types/node": "^14.14.19",
-    "@typescript-eslint/eslint-plugin": "^4.11.1",
-    "@typescript-eslint/parser": "^4.11.1",
-    "esbuild": "^0.8.29",
+    "@tensorflow/tfjs": "^2.8.3",
+    "@tensorflow/tfjs-backend-wasm": "^2.8.3",
+    "@tensorflow/tfjs-node": "^2.8.3",
+    "@tensorflow/tfjs-node-gpu": "^2.8.3",
+    "@types/node": "^14.14.20",
+    "@typescript-eslint/eslint-plugin": "^4.12.0",
+    "@typescript-eslint/parser": "^4.12.0",
+    "chokidar": "^3.5.0",
+    "esbuild": "^0.8.31",
     "eslint": "^7.17.0",
     "eslint-config-airbnb-base": "^14.2.1",
     "eslint-plugin-import": "^2.22.1",
@@ -55,7 +57,7 @@
     "eslint-plugin-node": "^11.1.0",
     "eslint-plugin-promise": "^4.2.1",
     "rimraf": "^3.0.2",
-    "tslib": "^2.0.3",
+    "tslib": "^2.1.0",
     "typescript": "^4.1.3"
   }
 }
diff --git a/build.js b/server/build.js
similarity index 100%
rename from build.js
rename to server/build.js
diff --git a/server/dev.js b/server/dev.js
new file mode 100755
index 0000000..d16d3f1
--- /dev/null
+++ b/server/dev.js
@@ -0,0 +1,147 @@
+/*
+  micro http2 server with file monitoring and automatic app rebuild
+  - can process concurrent http requests
+  - monitors specified filed and folders for changes
+  - triggers library and application rebuild
+  - any build errors are immediately displayed and can be corrected without need for restart
+  - passthrough data compression
+*/
+
+const process = require('process');
+const fs = require('fs');
+const zlib = require('zlib');
+const http = require('http');
+const http2 = require('http2');
+const path = require('path');
+// eslint-disable-next-line node/no-unpublished-require, import/no-extraneous-dependencies
+const chokidar = require('chokidar');
+const log = require('@vladmandic/pilogger');
+const build = require('./build.js');
+
+// app configuration
+// you can provide your server key and certificate or use provided self-signed ones
+// self-signed certificate generated using:
+// openssl req -x509 -newkey rsa:4096 -nodes -keyout https.key -out https.crt -days 365 -subj "/C=US/ST=Florida/L=Miami/O=@vladmandic"
+// client app does not work without secure server since browsers enforce https for webcam access
+const options = {
+  key: fs.readFileSync('server/https.key'),
+  cert: fs.readFileSync('server/https.crt'),
+  root: '..',
+  default: 'example/index.html',
+  httpPort: 8000,
+  httpsPort: 8001,
+  monitor: ['package.json', 'example', 'src'],
+};
+
+// just some predefined mime types
+const mime = {
+  '.html': 'text/html',
+  '.js': 'text/javascript',
+  '.css': 'text/css',
+  '.json': 'application/json',
+  '.png': 'image/png',
+  '.jpg': 'image/jpg',
+  '.gif': 'image/gif',
+  '.ico': 'image/x-icon',
+  '.svg': 'image/svg+xml',
+  '.wav': 'audio/wav',
+  '.mp4': 'video/mp4',
+  '.woff': 'application/font-woff',
+  '.ttf': 'application/font-ttf',
+  '.wasm': 'application/wasm',
+};
+
+// watch filesystem for any changes and notify build when needed
+async function watch() {
+  const watcher = chokidar.watch(options.monitor, {
+    persistent: true,
+    ignorePermissionErrors: false,
+    alwaysStat: false,
+    ignoreInitial: true,
+    followSymlinks: true,
+    usePolling: false,
+    useFsEvents: false,
+    atomic: true,
+  });
+  // single event handler for file add/change/delete
+  watcher
+    .on('add', (evt) => build.build(evt, 'add'))
+    .on('change', (evt) => build.build(evt, 'modify'))
+    .on('unlink', (evt) => build.build(evt, 'remove'))
+    .on('error', (err) => log.error(`Client watcher error: ${err}`))
+    .on('ready', () => log.state('Monitoring:', options.monitor));
+}
+
+// get file content for a valid url request
+function handle(url) {
+  return new Promise((resolve) => {
+    let obj = { ok: false };
+    obj.file = url;
+    if (!fs.existsSync(obj.file)) resolve(null);
+    obj.stat = fs.statSync(obj.file);
+    if (obj.stat.isFile()) obj.ok = true;
+    if (!obj.ok && obj.stat.isDirectory()) {
+      obj.file = path.join(obj.file, options.default);
+      // @ts-ignore
+      obj = handle(obj.file);
+    }
+    resolve(obj);
+  });
+}
+
+// process http requests
+async function httpRequest(req, res) {
+  handle(path.join(__dirname, options.root, decodeURI(req.url)))
+    .then((result) => {
+      // get original ip of requestor, regardless if it's behind proxy or not
+      // eslint-disable-next-line dot-notation
+      const forwarded = (req.headers['forwarded'] || '').match(/for="\[(.*)\]:/);
+      const ip = (Array.isArray(forwarded) ? forwarded[1] : null) || req.headers['x-forwarded-for'] || req.ip || req.socket.remoteAddress;
+      if (!result || !result.ok) {
+        res.writeHead(404, { 'Content-Type': 'text/html' });
+        res.end('Error 404: Not Found\n', 'utf-8');
+        log.warn(`${req.method}/${req.httpVersion}`, res.statusCode, req.url, ip);
+      } else {
+        const ext = String(path.extname(result.file)).toLowerCase();
+        const contentType = mime[ext] || 'application/octet-stream';
+        const accept = req.headers['accept-encoding'] ? req.headers['accept-encoding'].includes('br') : false; // does target accept brotli compressed data
+        res.writeHead(200, {
+          // 'Content-Length': result.stat.size, // not using as it's misleading for compressed streams
+          'Content-Language': 'en', 'Content-Type': contentType, 'Content-Encoding': accept ? 'br' : '', 'Last-Modified': result.stat.mtime, 'Cache-Control': 'no-cache', 'X-Powered-By': `NodeJS/${process.version}`,
+        });
+        const compress = zlib.createBrotliCompress({ params: { [zlib.constants.BROTLI_PARAM_QUALITY]: 5 } }); // instance of brotli compression with level 5
+        const stream = fs.createReadStream(result.file);
+        if (!accept) stream.pipe(res); // don't compress data
+        else stream.pipe(compress).pipe(res); // compress data
+
+        // alternative methods of sending data
+        /// 2. read stream and send by chunk
+        // const stream = fs.createReadStream(result.file);
+        // stream.on('data', (chunk) => res.write(chunk));
+        // stream.on('end', () => res.end());
+
+        // 3. read entire file and send it as blob
+        // const data = fs.readFileSync(result.file);
+        // res.write(data);
+        log.data(`${req.method}/${req.httpVersion}`, res.statusCode, contentType, result.stat.size, req.url, ip);
+      }
+      return null;
+    })
+    .catch((err) => log.error('handle error:', err));
+}
+
+// app main entry point
+async function main() {
+  log.header();
+  await watch();
+  // @ts-ignore
+  const server1 = http.createServer(options, httpRequest);
+  server1.on('listening', () => log.state('HTTP server listening:', options.httpPort));
+  server1.listen(options.httpPort);
+  const server2 = http2.createSecureServer(options, httpRequest);
+  server2.on('listening', () => log.state('HTTP2 server listening:', options.httpsPort));
+  server2.listen(options.httpsPort);
+  await build.build('all', 'startup');
+}
+
+main();
diff --git a/server/https.crt b/server/https.crt
new file mode 100644
index 0000000..1cc2228
--- /dev/null
+++ b/server/https.crt
@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIUKQKodDBJnuweJs5IcTyL4NIp3vgwDQYJKoZIhvcNAQEL
+BQAwRTELMAkGA1UEBhMCVVMxEDAOBgNVBAgMB0Zsb3JpZGExDjAMBgNVBAcMBU1p
+YW1pMRQwEgYDVQQKDAtAdmxhZG1hbmRpYzAeFw0yMDExMDcxNTE3NDNaFw0yMTEx
+MDcxNTE3NDNaMEUxCzAJBgNVBAYTAlVTMRAwDgYDVQQIDAdGbG9yaWRhMQ4wDAYD
+VQQHDAVNaWFtaTEUMBIGA1UECgwLQHZsYWRtYW5kaWMwggIiMA0GCSqGSIb3DQEB
+AQUAA4ICDwAwggIKAoICAQDSC88PF8NyLkagK5mAZ/d739SOU16l2Cx3zE35zZQh
+O29+1L4L+oMksLYipo+FMgtGO+MSzFsvGgKCs2sDSdfyoNSTZ3QaN4BAZ0sbq+wL
+cke7yRBTM/XIGOQfhqq8yC2q8/zXwUbZg0UsCAxDGNwUr0Qlm829laIU/UN1KcYS
+57Nebl1z05wMEvYmyl4JBAl9ozne7KS9DyW7jbrAXE8TaEy3+pY66kx5GG6v2+up
+ScITGm4YPmPPlpOF1UjQloosgxdVa+fVp8aNCa/rf0JNO0Uhb3OKOZ+4kYmpfPn/
+trwoKWAa6CV1uAJ+3zDkLMq1JNlrV4OMp1QvX0wzA47a/n466JMN9SFb0Ng5wf19
+VOtT5Zu7chDStBudVjxlMDfUixvhvn4sjbaLNYR1fyWPoNXwr0KX2lpTP1QOzp9/
+Sd0iiJ8RPfXn8Xo26MStu4I52CZjS7yEMgJGCLH/mgPuSbrHHYYrrrCPJgmQOZG2
+TNMI+EqOwQvHh2ghdv7t7EEk4IslBk0QzufMXQ2WFXQ20nvj74mrmmiMuBcmonpR
+0egA5/M18ZPLQxYu0Q86NUr4XHtAG1fq+n8pseQ7Avy6Gk6HRiezCbB7TJ9rnNeu
+jie1TDajC6W7rx0VF7hcxkIrDgNgnYcjXUV2hMx1lo4fIoWkL3nJJVEthMVIcJOX
+EwIDAQABo1MwUTAdBgNVHQ4EFgQUHawIRAo1bW8Xy7l4oKfM+ESjhs0wHwYDVR0j
+BBgwFoAUHawIRAo1bW8Xy7l4oKfM+ESjhs0wDwYDVR0TAQH/BAUwAwEB/zANBgkq
+hkiG9w0BAQsFAAOCAgEAozQJk5Ahx7rDn/aMXLdZFxR81VfkmHDm7NhlJsdVKUx5
+o/iegXnvwc1PoeKsz2S504QiuL8l7jqZoU2WPIm7Vlr+oxBgiKqjo1EqBsUgNCZ7
+qxMD84TVp/KBGjKUh1TXhjJwGGfNNr+R/fJGw+36UeuY3fSckjaYTuNuVElp+DoZ
+/pGyu1qpcybLfiR8mpQkCeU/iBq5gIjWddbVjlYoTKfqULZrpsAF2AeqELEgyshl
+p3PNhW/54TJSn4mWK+39BibYHPkvx8orEuWKyjjRk82hEXi7J3hsGKX29qC3oO40
+67DKDWmZdMCz+E1ERf10V0bSp6iJnnlwknHJloZUETV1NY/DdoSC6e8CN0+0cQqL
+aJefJ483O3sXyN3v3+DaEFBLPFgRFGZB7eaBwR2xAv/KfjT5dSyi+wA4LZAxsQMC
+Q7UYGNAfHLNHJo/bsj12+JDhJaFZ/KoBKzyMUuEXmvjxXNDMCfm+gVQFoLyXkGq3
+491W/O7LjR6pkD+ce0qeTFMu3nfUubyfbONVDEfuH4GC1e+FAggCRaBnFsVzCzXj
+jxOOLoQ9nwLk8v17mx0BSwX4iuqvXFntfJbzfcnzQfx/qqPFheIbGnmKw1lrRML8
+87ZbN6t01+v2YyYe6Mc7p80s1R3jc8aVX8ca2KcYwsJAkg/xz0q5RJwsE1is5UY=
+-----END CERTIFICATE-----
diff --git a/server/https.key b/server/https.key
new file mode 100644
index 0000000..e121711
--- /dev/null
+++ b/server/https.key
@@ -0,0 +1,52 @@
+-----BEGIN PRIVATE KEY-----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+Eyb7vcUSI6OYXFQU7OwO1RGvpKvSJb5Y0wo11DrtRnO16i5gaGDg9u9e8ofISJSz
+kcrZOKCGst1HQ1mXhbB+sbSh0aPnJog4I+OHxkgMdvyVO6vQjXExnAIxzzi8wZ25
++oECggEBAIT6q/sn8xFt5Jwc/0Z7YUjd415Nknam09tnbB+UPRR6lt6JFoILx8by
+5Y1sN30HWDv27v9G32oZhUDii3Rt3PkbYLqlHy7XBMEXA9WIUo+3Be7mtdL8Wfrj
+0zn0b7Hks9a9KsElG1dXUopwjMRL3M22UamaN7e/gl5jz2I7pyc5oaqz9GRDV5yG
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+IxI5x5Lb0fdfvL8JK3nRWn7Uzbmm5Ni/OaODNKP+fIm9m2yDAs8LM8RGpPtk6i0d
+qIRta3H9KNw2Mhpkm77TtUSV/W5aOmY=
+-----END PRIVATE KEY-----